{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":33216,"offset":33206,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"Here is a new pair of original and rewritten queries using the guidelines provided:\n\n[ORIGINAL QUERY]\n```sql\nwith refunded_items as (\n select cr_item_sk, sum(cr_return_quantity) as total_refunded\n from catalog_returns\n group by cr_item_sk\n),\navailable_inventory as (\n select inv_item_sk, max(inv_quantity_on_hand) as max_inventory\n from inventory\n group by inv_item_sk\n)\nselect i.i_item_id, r_reason.r_reason_desc, ca.ca_state, ri.total_refunded, ai.max_inventory\nfrom refunded_items ri\njoin available_inventory ai on ri.cr_item_sk = ai.inv_item_sk\njoin item i on ri.cr_item_sk = i.i_item_sk\njoin reason r_reason on r_reason.r_reason_sk = i.i_item_sk\njoin customer_address ca on cr_refundable_addr_sk = ca.ca_address_sk\norder by ri.total_refunded desc\nlimit 100;\n```\n\n[REWRITTEN QUERY]\n```sql\nselect i.i_item_id, r_reason.r_reason_desc, ca.ca_state, ri.total_refunded, ai.max_inventory\nfrom (\n select cr_item_sk, sum(cr_return_quantity) as total_refunded\n from catalog_returns\n group by cr_item_sk\n) as ri\njoin (\n select inv_item_sk, max(inv_quantity_on_hand) as max_inventory\n from inventory\n group by inv_item_sk\n) as ai on ri.cr_item_sk = ai.inv_item_sk\njoin item i on ri.cr_item_sk = i.i_item_sk\njoin reason r_reason on r_reason.r_reason_sk = i.i_item_sk\njoin customer_address ca on ca.ca_address_sk = cr_refundable_addr_sk\norder by ri.total_refunded desc\nlimit 100;\n```\n\n### Explanation:\n- **CTEs Usage**: The original query uses two CTEs, `refunded_items` and `available_inventory`, to aggregate data from the `catalog_returns` and `inventory` tables.\n- **Join**: There's only one join operation between the `refunded_items` CTE and `available_inventory` CTE based on the `item_sk`.\n- **Fact Tables**: The `catalog_returns` and `inventory` tables are used in the CTEs.\n- **Aggregate Functions**: `SUM` is used in the `refunded_items` CTE and `MAX` is used in the `available_inventory` CTE.\n- **WHERE Clause**: The queries do not employ any operators in the `WHERE` clause.\n- **Rewriting**: The rewritten query eliminates the use of CTEs, following a similar transformation pattern demonstrated in the example, by moving subqueries into inline views.","behavior":"degenerate_repetition","conversation_hash":"0e1a5ac7be86ac3d44190c16b16c4090","conversation_redacted":false,"conversation_toxic":false,"country":"South Korea","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":6182,"n_turns_in_conversation":1,"n_words":693,"source_row_offset":33206,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:22:57 GMT","turn_id":"650a503d577e27d7b7856f76","turn_index":0,"user_text":"You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.\n\n[ORIGINAL QUERY]```select ca_state,cd_gender,cd_marital_status,cd_dep_count,count(*) cnt1,avg(cd_dep_count),stddev_samp(cd_dep_count),sum(cd_dep_count),cd_dep_employed_count,count(*) cnt2,avg(cd_dep_employed_count),stddev_samp(cd_dep_employed_count),sum(cd_dep_employed_count),cd_dep_college_count,count(*) cnt3,avg(cd_dep_college_count),stddev_samp(cd_dep_college_count),sum(cd_dep_college_count) from customer c,customer_address ca,customer_demographics where c.c_current_addr_sk = ca.ca_address_sk and cd_demo_sk = c.c_current_cdemo_sk and exists (select * from store_sales,date_dim where c.c_customer_sk = ss_customer_sk and ss_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) and (exists (select * from web_sales,date_dim where c.c_customer_sk = ws_bill_customer_sk and ws_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) or exists (select * from catalog_sales,date_dim where c.c_customer_sk = cs_ship_customer_sk and cs_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4)) group by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count order by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count limit 100;```\n\n[REWRITTEN QUERY]```select ca_state, cd_gender, cd_marital_status, cd_dep_count, count(*) cnt1, avg(cd_dep_count), stddev_samp(cd_dep_count), sum(cd_dep_count), cd_dep_employed_count, count(*) cnt2, avg(cd_dep_employed_count), stddev_samp(cd_dep_employed_count), sum(cd_dep_employed_count), cd_dep_college_count, count(*) cnt3, avg(cd_dep_college_count), stddev_samp(cd_dep_college_count), sum(cd_dep_college_count) from customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on cd.cd_demo_sk = c.c_current_cdemo_sk where c.c_customer_sk in ( select ss_customer_sk from store_sales join date_dim on ss_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) and ( c.c_customer_sk in ( select ws_bill_customer_sk from web_sales join date_dim on ws_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) or c.c_customer_sk in ( select cs_ship_customer_sk from catalog_sales join date_dim on cs_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) ) group by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count order by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count limit 100;```\n\nNow, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:\n1. The original query must contain CTEs.\n2. The original query must make use of the following fact tables: ['catalog_returns', 'inventory']. Below is the join graph for these fact tables:\n ['catalog_returns(cr_item_sk) references item(i_item_sk);', 'catalog_returns(cr_reason_sk) references reason(r_reason_sk);', 'catalog_returns(cr_refunded_addr_sk) references customer_address(ca_address_sk);']\n ['inventory(inv_item_sk) references item(i_item_sk);', 'inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);', 'inventory(inv_date_sk) references date_dim(d_date_sk);']\n5. The original query must employ 1 joins.\n6. The original query must employ the following aggregate functions: ['sum', 'max'].\n7. The original query must employ the following operators in its WHERE clause: [].\n8. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```."},{"assistant_initiates":false,"asst_text":"In Jest, a common scenario for testing a React component involves using mocks to simulate the behavior of functions, modules, or contexts that your component depends on. If by \"mock hocs\" you mean \"mocking Higher-Order Components\" (HOCs), here's a guide on how to do that.\n\n### Steps to Mock a Higher-Order Component (HOC) in Jest\n\n1. **Understand the HOC**: A HOC is a function that takes a component and returns a new component. When testing, you might want to replace the HOC with a mock or a simpler version of it.\n\n2. **Create a Mock for the HOC**: You can create a simple mock implementation that wraps the child component without adding any additional logic.\n\n3. **Write Your Test**: Use Jest and React Testing Library (or Enzyme) to write your test.\n\n### Example\n\nSuppose you have an HOC called `withAuth` that provides authentication props to a component. \n\n```jsx\n// withAuth.js\nimport React from 'react';\n\nconst withAuth = (WrappedComponent) => {\n return (props) => {\n const isAuthenticated = true; // Assume logic to determine authentication\n return ;\n };\n};\n\nexport default withAuth;\n```\n\nAnd you have a component that uses this HOC:\n\n```jsx\n// MyComponent.js\nimport React from 'react';\nimport withAuth from './withAuth';\n\nconst MyComponent = ({ isAuthenticated }) => {\n return
{isAuthenticated ? 'Logged In' : 'Logged Out'}
;\n};\n\nexport default withAuth(MyComponent);\n```\n\n### Mocking the HOC in Tests\n\nFirst, create a mock for the HOC:\n\n```jsx\n// __mocks__/withAuth.js\nimport React from 'react';\n\nconst mockWithAuth = (WrappedComponent) => {\n return (props) => ;\n};\n\nexport default mockWithAuth;\n```\n\nNow, use the mock in your test file:\n\n```jsx\n// MyComponent.test.js\nimport React from 'react';\nimport { render } from '@testing-library/react';\nimport MyComponent from './MyComponent';\n\n// Automatically mock the HOC\njest.mock('./withAuth');\n\ndescribe('MyComponent', () => {\n it('displays \"Logged In\" when isAuthenticated is true', () => {\n const { getByText } = render();\n expect(getByText('Logged In')).toBeInTheDocument();\n });\n});\n```\n\n### Important Notes\n\n- **Automatic Mocking**: The line `jest.mock('./withAuth');` automatically uses the mock defined in `__mocks__/withAuth.js`. Make sure your mock file is correctly placed in a `__mocks__` directory at the same level as your `withAuth.js` file.\n \n- **Custom Props**: If you need to test with different props, you can modify the mock implementation or directly pass different props when rendering.\n\n- **Assertions**: Use assertions to verify the behavior of the component based on what the HOC is supposed to provide (like authenticated states).\n\nBy following these steps, you can effectively mock HOCs in your Jest tests, allowing for focused unit testing of your components while isolating them from dependencies.","behavior":"degenerate_repetition","conversation_hash":"990fdcb3528176031b2b1e1e32e605eb","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":2976,"n_turns_in_conversation":3,"n_words":435,"source_row_offset":33207,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:04 GMT","turn_id":"f301af4c8c94fda034513c20","turn_index":0,"user_text":"how mock hocs react jest"},{"assistant_initiates":false,"asst_text":"If you want to pass additional props through a Higher-Order Component (HOC) and use these props in your tests, you can still mock the HOC while allowing it to accept and pass through props accordingly. Here\u2019s a breakdown of how to achieve this in a way that includes passing props through the HOC.\n\n### Example HOC with Additional Props\n\nLet\u2019s assume you have an HOC that enhances a component with some additional props while still allowing other props to be passed through.\n\n```jsx\n// withAuth.js\nimport React from 'react';\n\nconst withAuth = (WrappedComponent) => {\n return (props) => {\n const isAuthenticated = true; // Assume some authentication logic here\n return ;\n };\n};\n\nexport default withAuth;\n```\n\n### Component Using the HOC\n\n```jsx\n// MyComponent.js\nimport React from 'react';\nimport withAuth from './withAuth';\n\nconst MyComponent = ({ isAuthenticated, username }) => {\n return
{isAuthenticated ? `Hello, ${username}` : 'Logged Out'}
;\n};\n\nexport default withAuth(MyComponent);\n```\n\n### Mocking the HOC and Passing Props in Tests\n\nWhen you want to mock `withAuth` in your Jest tests and pass additional props to the component, you can do so like this:\n\n1. **Create a Mock for the HOC**: Ensure your mock passes through any props it receives.\n\n```jsx\n// __mocks__/withAuth.js\nimport React from 'react';\n\nconst mockWithAuth = (WrappedComponent) => {\n return (props) => {\n // Adding mock behavior for isAuthenticated\n return ;\n };\n};\n\nexport default mockWithAuth;\n```\n\n2. **Write Your Test**: In your test file, render the component and pass the required props:\n\n```jsx\n// MyComponent.test.js\nimport React from 'react';\nimport { render } from '@testing-library/react';\nimport MyComponent from './MyComponent';\n\n// Automatically mock the HOC\njest.mock('./withAuth');\n\ndescribe('MyComponent', () => {\n it('displays the username when isAuthenticated is true', () => {\n const username = 'John Doe';\n const { getByText } = render();\n \n expect(getByText(`Hello, ${username}`)).toBeInTheDocument();\n });\n\n it('displays \"Logged Out\" when isAuthenticated is false', () => {\n // Change the mock behavior if needed for this test\n jest.mock('./withAuth', () => (WrappedComponent) => {\n return (props) => {\n return ;\n };\n });\n\n const { getByText } = render();\n \n expect(getByText('Logged Out')).toBeInTheDocument();\n });\n});\n```\n\n### Explanation\n\n1. **Mock HOC**: The mock for `withAuth` is created in the `__mocks__` directory, which uses the standard convention in Jest. This mock ensures that any props passed to the wrapped component are forwarded correctly, while also providing the `isAuthenticated` prop.\n\n2. **Test Case**: The first test checks that when the `isAuthenticated` prop is true, the rendered output includes the username. The second test overrides the mock behavior for a different set of conditions.\n\n3. **Dynamic Mocking**: Note that if you want to change the behavior of the mock during tests (like making `isAuthenticated` false), you can redefine the mock using `jest.mock()` within that specific test case.\n\nThis approach gives you full control over the HOC's behavior and how props are managed in your tests. By combining prop-passing and mock implementation, you can create thorough tests for components wrapped in HOCs.","behavior":"degenerate_repetition","conversation_hash":"990fdcb3528176031b2b1e1e32e605eb","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":3586,"n_turns_in_conversation":3,"n_words":507,"source_row_offset":33208,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:04 GMT","turn_id":"f8f976ab0bbf28180866f1f2","turn_index":1,"user_text":"i need props in hoc"},{"assistant_initiates":false,"asst_text":"\u0412 JavaScript, \u0432 \u043e\u0442\u043b\u0438\u0447\u0438\u0435 \u043e\u0442 \u044f\u0437\u044b\u043a\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 \u0441\u0442\u0440\u043e\u0433\u0443\u044e \u0442\u0438\u043f\u0438\u0437\u0430\u0446\u0438\u044e, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a TypeScript, \"\u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u0438\" (\u0433\u0435\u043d\u0435\u0440\u0438\u043a\u0438) \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u043d\u0435 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u044e\u0442\u0441\u044f. \u041e\u0434\u043d\u0430\u043a\u043e \u0435\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0438\u043b\u0438 \u043a\u043b\u0430\u0441\u0441, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u0443\u0434\u0443\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u043e\u0432. \u041d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u0438\u0437 \u043d\u0438\u0445 \u2014 \u044d\u0442\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u043e\u0439 \u043b\u043e\u0433\u0438\u043a\u0438. \u0415\u0441\u043b\u0438 \u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u0433\u0443\u044e \u0442\u0438\u043f\u0438\u0437\u0430\u0446\u0438\u044e \u0441 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u0430\u043c\u0438, \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c TypeScript.\n\n\u0412\u043e\u0442 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u043f\u043e\u0441\u043e\u0431\u043e\u0432, \u043a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u043e\u0435 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432 JavaScript:\n\n### \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0439\n\n\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u0442 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u044b \u043b\u044e\u0431\u043e\u0433\u043e \u0442\u0438\u043f\u0430, \u0430 \u0437\u0430\u0442\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0441 \u044d\u0442\u0438\u043c\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438:\n\n```javascript\nfunction identity(arg: T): T {\n return arg;\n}\n\n// \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438\nconst num = identity(5); // num \u0431\u0443\u0434\u0435\u0442 \u0442\u0438\u043f\u0430 number\nconst str = identity(\"Hello\"); // str \u0431\u0443\u0434\u0435\u0442 \u0442\u0438\u043f\u0430 string\nconst obj = identity({ name: \"John\" }); // obj \u0431\u0443\u0434\u0435\u0442 \u0442\u0438\u043f\u0430 { name: string }\n```\n\n\u042d\u0442\u043e\u0442 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u0442\u044c \u0432 TypeScript, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u0438.\n\n### \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u043b\u0430\u0441\u0441\u043e\u0432\n\n\u0415\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u043a\u043b\u0430\u0441\u0441, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u043e\u0436\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u044d\u0442\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n\n```javascript\nclass Box {\n constructor(value) {\n this.value = value;\n }\n\n getValue() {\n return this.value;\n }\n}\n\n// \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u043b\u0430\u0441\u0441 \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438\nconst numberBox = new Box(123);\nconst stringBox = new Box(\"hello\");\nconst objectBox = new Box({ key: \"value\" });\n\nconsole.log(numberBox.getValue()); // 123\nconsole.log(stringBox.getValue()); // \"hello\"\nconsole.log(objectBox.getValue()); // { key: \"value\" }\n```\n\n### \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 TypeScript \u0434\u043b\u044f \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u043e\u0432\n\n\u0415\u0441\u043b\u0438 \u0432\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442\u0435 \u0441 TypeScript, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043f\u043e\u043b\u043d\u043e\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u043e\u0432. \u0412\u043e\u0442 \u043f\u0440\u0438\u043c\u0435\u0440, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u043a\u043e\u043d\u0441\u0442\u0430\u043d\u0442\u0443 \u0441 \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u043c \u0442\u0438\u043f\u043e\u043c:\n\n```typescript\nfunction createArray(...elements: T[]): T[] {\n return elements;\n}\n\n// \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438\nconst numberArray = createArray(1, 2, 3);\nconst stringArray = createArray('a', 'b', 'c');\nconst mixedArray = createArray(1, '2', 3, '4');\n```\n\n### \u041f\u043e\u0434\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0438\u0442\u043e\u0433\u043e\u0432\n\n\u0412 JavaScript \u043d\u0435\u0442 \u043f\u0440\u044f\u043c\u043e\u0439 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u043e\u0432, \u043d\u043e \u043c\u043e\u0436\u043d\u043e \u0438\u043c\u0438\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0438\u0445 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0438 \u043a\u043b\u0430\u0441\u0441\u043e\u0432. \u0415\u0441\u043b\u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u0430 \u0441\u0442\u0440\u043e\u0433\u0430\u044f \u0442\u0438\u043f\u0438\u0437\u0430\u0446\u0438\u044f \u0441 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a\u0430\u043c\u0438, \u043b\u0443\u0447\u0448\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c TypeScript, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u043b\u0441\u044f \u0441 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0442\u0430\u043a\u0438\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0435\u0439.","behavior":"degenerate_repetition","conversation_hash":"990fdcb3528176031b2b1e1e32e605eb","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":2455,"n_turns_in_conversation":3,"n_words":323,"source_row_offset":33209,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:04 GMT","turn_id":"a12d32ed6d8e8a1795b00b32","turn_index":2,"user_text":"\u043a\u0430\u043a \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0447\u0442\u043e\u0431\u044b \u043a\u043e\u043d\u0441\u0442\u0430\u043d\u0442\u0430 js \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u043b\u0430 \u0434\u0436\u0435\u043d\u0435\u0440\u0438\u043a"},{"assistant_initiates":false,"asst_text":"Sure, let's create a SQL query pair based on your requirements:\n\n[ORIGINAL QUERY]\n```sql\nselect ws_bill_city, hd_income_band_sk, count(*) as sale_count, avg(ws_quantity) as avg_quantity, sum(ws_ext_sales_price) as total_sales\nfrom web_sales, customer_address, household_demographics\nwhere ws_ship_date_sk = (select d_date_sk from date_dim where d_year = 2020)\nand ws_bill_addr_sk = ca_address_sk\nand ws_bill_hdemo_sk = hd_demo_sk\ngroup by ws_bill_city, hd_income_band_sk\norder by sale_count desc\nlimit 10;\n```\n\n[REWRITTEN QUERY]\n```sql\nselect ca.ca_city as ws_bill_city, hd.hd_income_band_sk as hd_income_band_sk, count(*) as sale_count, avg(ws.ws_quantity) as avg_quantity, sum(ws.ws_ext_sales_price) as total_sales\nfrom web_sales ws\njoin customer_address ca on ws.ws_bill_addr_sk = ca.ca_address_sk\njoin household_demographics hd on ws.ws_bill_hdemo_sk = hd.hd_demo_sk\nwhere ws.ws_ship_date_sk in (select d_date_sk from date_dim where d_year = 2020)\ngroup by ca.ca_city, hd.hd_income_band_sk\norder by sale_count desc\nlimit 10;\n```\n\n### Explanation\n\n#### Original Query\n- The query performs calculations on the `web_sales` table, joins using comma-separated join predicates wrapped in the `WHERE` condition.\n- It references the `date_dim`, `customer_address`, and `household_demographics` tables implicitly and uses sub-query for the date.\n- Aggregates are calculated on the sales quantity and total price with a grouping by city and income band.\n\n#### Rewritten Query\n- The rewritten query uses explicit `JOIN` syntax instead of comma-separated tables and instead of subqueries in the `WHERE` clause.\n- It joins `web_sales` with `customer_address` and `household_demographics` tables directly.\n- Conditions that were inline-filtered like dates are now part of a `WHERE` clause using a subquery with `IN`.\n- Preserves all original calculated fields and aggregates and limits to the top 10 results by sale count, sorted in descending order. \n\nThis rewrite pattern provides performance improvements by making the intent of joins clearer to the SQL optimizer, potentially enabling better execution plans especially in large datasets.","behavior":"degenerate_repetition","conversation_hash":"adce19ec4e32bdcc156e43895d7e0b3d","conversation_redacted":false,"conversation_toxic":false,"country":"Czechia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":5937,"n_turns_in_conversation":1,"n_words":691,"source_row_offset":33210,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:07 GMT","turn_id":"a5e07500108f11e74eb76ce1","turn_index":0,"user_text":"You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.\n\n[ORIGINAL QUERY]```select ca_state,cd_gender,cd_marital_status,cd_dep_count,count(*) cnt1,avg(cd_dep_count),stddev_samp(cd_dep_count),sum(cd_dep_count),cd_dep_employed_count,count(*) cnt2,avg(cd_dep_employed_count),stddev_samp(cd_dep_employed_count),sum(cd_dep_employed_count),cd_dep_college_count,count(*) cnt3,avg(cd_dep_college_count),stddev_samp(cd_dep_college_count),sum(cd_dep_college_count) from customer c,customer_address ca,customer_demographics where c.c_current_addr_sk = ca.ca_address_sk and cd_demo_sk = c.c_current_cdemo_sk and exists (select * from store_sales,date_dim where c.c_customer_sk = ss_customer_sk and ss_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) and (exists (select * from web_sales,date_dim where c.c_customer_sk = ws_bill_customer_sk and ws_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) or exists (select * from catalog_sales,date_dim where c.c_customer_sk = cs_ship_customer_sk and cs_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4)) group by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count order by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count limit 100;```\n\n[REWRITTEN QUERY]```select ca_state, cd_gender, cd_marital_status, cd_dep_count, count(*) cnt1, avg(cd_dep_count), stddev_samp(cd_dep_count), sum(cd_dep_count), cd_dep_employed_count, count(*) cnt2, avg(cd_dep_employed_count), stddev_samp(cd_dep_employed_count), sum(cd_dep_employed_count), cd_dep_college_count, count(*) cnt3, avg(cd_dep_college_count), stddev_samp(cd_dep_college_count), sum(cd_dep_college_count) from customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on cd.cd_demo_sk = c.c_current_cdemo_sk where c.c_customer_sk in ( select ss_customer_sk from store_sales join date_dim on ss_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) and ( c.c_customer_sk in ( select ws_bill_customer_sk from web_sales join date_dim on ws_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) or c.c_customer_sk in ( select cs_ship_customer_sk from catalog_sales join date_dim on cs_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) ) group by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count order by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count limit 100;```\n\nNow, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:\n1. The original query must not have any CTEs.\n2. The original query must make use of the following fact tables: ['web_sales']. Below is the join graph for these fact tables:\n ['web_sales(ws_ship_date_sk) references date_dim(d_date_sk);', 'web_sales(ws_bill_addr_sk) references customer_address(ca_address_sk);', 'web_sales(ws_bill_hdemo_sk) references household_demographics(hd_demo_sk);']\n4. The original query must employ 0 joins.\n5. The original query must employ the following aggregate functions: ['sum', 'avg', 'count'].\n6. The original query must employ the following operators in its WHERE clause: [].\n7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```."},{"assistant_initiates":false,"asst_text":"\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 \\( x^2 < 9x + 70 \\) \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u043c \u0435\u0433\u043e \u0432 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0439 \u0432\u0438\u0434. \u041f\u0435\u0440\u0435\u043d\u043e\u0441\u0438\u043c \u0432\u0441\u0435 \u0447\u043b\u0435\u043d\u044b \u043d\u0430 \u043e\u0434\u043d\u0443 \u0441\u0442\u043e\u0440\u043e\u043d\u0443:\n\n\\[\nx^2 - 9x - 70 < 0\n\\]\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0430\u0439\u0434\u0435\u043c \u043a\u043e\u0440\u043d\u0438 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0435\u0433\u043e \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u043e\u0433\u043e \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \\( x^2 - 9x - 70 = 0 \\) \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u0430:\n\n\\[\nD = (-9)^2 - 4 \\cdot 1 \\cdot (-70) = 81 + 280 = 361\n\\]\n\n\u041d\u0430\u0445\u043e\u0434\u0438\u043c \u043a\u043e\u0440\u043d\u0438:\n\n\\[\nx_1 = \\frac{9 - \\sqrt{361}}{2} = \\frac{9 - 19}{2} = \\frac{-10}{2} = -5\n\\]\n\n\\[\nx_2 = \\frac{9 + \\sqrt{361}}{2} = \\frac{9 + 19}{2} = \\frac{28}{2} = 14\n\\]\n\n\u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c, \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0438\u043c\u0435\u0435\u0442 \u043a\u043e\u0440\u043d\u0438 \\( x_1 = -5 \\) \u0438 \\( x_2 = 14 \\). \u041d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \\( x^2 - 9x - 70 < 0 \\) \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f \u043d\u0430 \u043f\u0440\u043e\u043c\u0435\u0436\u0443\u0442\u043a\u0435 \u043c\u0435\u0436\u0434\u0443 \u043a\u043e\u0440\u043d\u044f\u043c\u0438, \u0442\u043e \u0435\u0441\u0442\u044c \u043d\u0430 \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0435 \\((-5, 14)\\).\n\n\u0414\u043b\u0438\u043d\u0430 \u044d\u0442\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0430 \u0440\u0430\u0432\u043d\u0430 \\( 14 - (-5) = 14 + 5 = 19 \\).","behavior":"degenerate_repetition","conversation_hash":"62f471fcd2b55fa24cb670f36431e09b","conversation_redacted":false,"conversation_toxic":false,"country":"Sweden","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":1688,"n_turns_in_conversation":1,"n_words":325,"source_row_offset":33211,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:08 GMT","turn_id":"6ce5c2bea83aba3dce0ab7aa","turn_index":0,"user_text":"User: \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u0435 \u0434\u043b\u0438\u043d\u0443 \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0430. \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \u0445**2 < 9\u0445 + 70.\nAssistant: Generated by BLACKBOX.AI, try unlimited chat https://www.blackbox.ai\n\n\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 \\( x^2 < 9x + 70 \\) \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u043c \u0435\u0433\u043e \u0432 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0439 \u0432\u0438\u0434. \u041f\u0435\u0440\u0435\u043d\u043e\u0441\u0438\u043c \u0432\u0441\u0435 \u0447\u043b\u0435\u043d\u044b \u043d\u0430 \u043e\u0434\u043d\u0443 \u0441\u0442\u043e\u0440\u043e\u043d\u0443:\n\n\\[\nx^2 - 9x - 70 < 0\n\\]\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0430\u0439\u0434\u0435\u043c \u043a\u043e\u0440\u043d\u0438 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0435\u0433\u043e \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u043e\u0433\u043e \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \\( x^2 - 9x - 70 = 0 \\) \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u0430:\n\n\\[\nD = b^2 - 4ac = (-9)^2 - 4 \\cdot 1 \\cdot (-70) = 81 + 280 = 361\n\\]\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0430\u0445\u043e\u0434\u0438\u043c \u043a\u043e\u0440\u043d\u0438:\n\n\\[\nx_1 = \\frac{-b - \\sqrt{D}}{2a} = \\frac{9 - 19}{2} = \\frac{-10}{2} = -5\n\\]\n\\[\nx_2 = \\frac{-b + \\sqrt{D}}{2a} = \\frac{9 + 19}{2} = \\frac{28}{2} = 14\n\\]\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u043a\u043e\u0440\u043d\u0438 \\( x_1 = -5 \\) \u0438 \\( x_2 = 14 \\). \u0421\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e, \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \\( x^2 - 9x - 70 < 0 \\) \u0431\u0443\u0434\u0435\u0442 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c\u0441\u044f \u043c\u0435\u0436\u0434\u0443 \u043a\u043e\u0440\u043d\u044f\u043c\u0438. \n\n\u0422\u0435\u043f\u0435\u0440\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u044b:\n\n1. \\( (-\\infty,\nUser: \n\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u0435 \u0434\u043b\u0438\u043d\u0443 \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0430. \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \u0445**2 < 9\u0445 + 70.\nAssistant:"},{"assistant_initiates":false,"asst_text":"Ein technischer Schl\u00fcssel in einer Datenbank ist ein spezieller Code oder eine Kennung, die verwendet wird, um jeden Datensatz in einer Tabelle eindeutig zu identifizieren. Der technische Schl\u00fcssel sorgt daf\u00fcr, dass keine zwei Datens\u00e4tze gleich sind. Man kann sich das wie eine Art Ausweisnummer vorstellen, die jede Zeile in der Datenbank zu etwas Einzigartigem macht. Dadurch wird es einfach, bestimmte Informationen schnell zu finden, zu sortieren und zu organisieren, ohne Verwirrung oder \u00dcberschneidungen.","behavior":"degenerate_repetition","conversation_hash":"10d7135d6188a76a998599c2baad002e","conversation_redacted":false,"conversation_toxic":false,"country":"Germany","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"German","model":"gpt-4o-2024-08-06","n_chars":645,"n_turns_in_conversation":1,"n_words":91,"source_row_offset":33212,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:11 GMT","turn_id":"007add27ea2c46bae2634982","turn_index":0,"user_text":"Erstelle eine Beschreibung mit einfachen Worten f\u00fcr nicht technische Personen f\u00fcr den Begriff technischer Sch\u00fcssel in einer Datenbank."},{"assistant_initiates":false,"asst_text":"\u5c0d\u8a71\u7d00\u9304\uff1a\n\n**\u60c5\u5883\uff1a\u9280\u884c\u4fe1\u7528\u5361\u554f\u984c**\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u60a8\u597d\uff0c\u6211\u662f\u674e\u5148\u751f\uff0c\u6211\u7684\u4fe1\u7528\u5361\u6709\u554f\u984c\uff0c\u53ef\u4ee5\u5e6b\u6211\u67e5\u8a62\u4e00\u4e0b\u55ce\uff1f\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u60a8\u597d\uff0c\u674e\u5148\u751f\uff0c\u6211\u662f\u5c0f\u5f35\uff0c\u5f88\u9ad8\u8208\u70ba\u60a8\u670d\u52d9\u3002\u8acb\u554f\u60a8\u9047\u5230\u4ec0\u9ebc\u554f\u984c\uff1f\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u6211\u5728\u6628\u5929\u4f7f\u7528\u4fe1\u7528\u5361\u7684\u6642\u5019\uff0c\u767c\u73fe\u88ab\u62d2\u7d55\u4ea4\u6613\u4e86\uff0c\u4e0d\u77e5\u9053\u662f\u4ec0\u9ebc\u539f\u56e0\u3002\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u4e86\u89e3\u60a8\u7684\u60c5\u6cc1\u3002\u8acb\u60a8\u7a0d\u7b49\uff0c\u6211\u4f86\u5e6b\u60a8\u67e5\u8a62\u4e00\u4e0b\u3002\u8acb\u554f\u60a8\u80fd\u63d0\u4f9b\u60a8\u7684\u8eab\u4efd\u8b49\u5b57\u865f\u548c\u4fe1\u7528\u5361\u672b\u56db\u78bc\u7d66\u6211\u55ce\uff1f\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u597d\u7684\uff0c\u8eab\u4efd\u8b49\u5b57\u865f\u662fA123456789\uff0c\u4fe1\u7528\u5361\u672b\u56db\u78bc\u662f1234\u3002\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u611f\u8b1d\u60a8\u7684\u63d0\u4f9b\uff0c\u7a0d\u7b49\u6211\u67e5\u8a62\u4e00\u4e0b\u3002\u2026\u2026\u674e\u5148\u751f\uff0c\u6211\u9019\u908a\u67e5\u5230\u60a8\u7684\u4fe1\u7528\u5361\u56e0\u70ba\u672a\u80fd\u53ca\u6642\u7e73\u7d0d\u4e0a\u6708\u8cec\u55ae\u7684\u6700\u4f4e\u9084\u6b3e\u984d\uff0c\u6240\u4ee5\u66ab\u6642\u88ab\u9650\u5236\u4f7f\u7528\u3002\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u554a\uff0c\u6211\u771f\u7684\u6c92\u6ce8\u610f\u5230\uff0c\u90a3\u6211\u73fe\u5728\u8a72\u600e\u9ebc\u8fa6\uff1f\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u674e\u5148\u751f\uff0c\u60a8\u53ef\u4ee5\u5148\u7e73\u7d0d\u672a\u4ed8\u6b3e\u9805\u3002\u6211\u53ef\u4ee5\u5e6b\u60a8\u5b89\u6392\u5728\u7dda\u652f\u4ed8\uff0c\u9019\u6a23\u5361\u7247\u6703\u5f88\u5feb\u6062\u5fa9\u4f7f\u7528\u529f\u80fd\u3002\u6216\u8005\uff0c\u60a8\u4e5f\u53ef\u4ee5\u5230\u9644\u8fd1\u7684\u9280\u884c\u5206\u884c\u7e73\u7d0d\uff0c\u7136\u5f8c\u64a5\u6253\u6211\u5011\u7684\u5ba2\u670d\u71b1\u7dda\u89e3\u9396\u3002\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u5728\u7dda\u652f\u4ed8\u5c31\u53ef\u4ee5\u55ce\uff1f\u90a3\u6211\u8981\u600e\u9ebc\u64cd\u4f5c\u5462\uff1f\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u5728\u7dda\u652f\u4ed8\u975e\u5e38\u7c21\u55ae\u3002\u60a8\u53ef\u4ee5\u767b\u9304\u7db2\u9280\uff0c\u9078\u64c7\u300c\u4fe1\u7528\u5361\u9084\u6b3e\u300d\uff0c\u8f38\u5165\u60a8\u7684\u5361\u865f\u548c\u7e73\u4ed8\u91d1\u984d\uff0c\u7136\u5f8c\u6309\u78ba\u8a8d\u5373\u53ef\u5b8c\u6210\u7e73\u6b3e\u3002\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u4e86\u89e3\uff0c\u90a3\u9019\u6a23\u5927\u6982\u591a\u4e45\u53ef\u4ee5\u6062\u5fa9\u6b63\u5e38\u4f7f\u7528\uff1f\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u901a\u5e38\u5728\u60a8\u5b8c\u6210\u7e73\u6b3e\u5f8c\u76841\u81f32\u500b\u5de5\u4f5c\u65e5\u5167\u5c31\u6703\u6062\u5fa9\u6b63\u5e38\u4f7f\u7528\u3002\u5982\u679c\u60a8\u9700\u8981\u7acb\u5373\u4f7f\u7528\u9019\u5f35\u5361\uff0c\u6211\u5011\u5efa\u8b70\u60a8\u76f4\u63a5\u806f\u7e6b\u4fe1\u7528\u5361\u4e2d\u5fc3\u7533\u8acb\u7dca\u6025\u89e3\u5361\u3002\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u975e\u5e38\u611f\u8b1d\u4f60\u7684\u5354\u52a9\uff0c\u6211\u73fe\u5728\u5c31\u53bb\u5b8c\u6210\u652f\u4ed8\u3002\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u4e0d\u5ba2\u6c23\uff0c\u674e\u5148\u751f\u3002\u5982\u679c\u9084\u6709\u4efb\u4f55\u554f\u984c\uff0c\u8acb\u96a8\u6642\u64a5\u6253\u6211\u5011\u7684\u5ba2\u670d\u71b1\u7dda\u3002\u795d\u60a8\u9806\u5229\uff01\n\n**\u5ba2\u6236\uff08\u674e\u5148\u751f\uff09\uff1a** \n\u8b1d\u8b1d\u4f60\uff0c\u518d\u898b\u3002\n\n**\u5ba2\u670d\u4ee3\u8868\uff1a** \n\u518d\u898b\uff0c\u795d\u60a8\u6709\u7f8e\u597d\u7684\u4e00\u5929\uff01","behavior":"degenerate_repetition","conversation_hash":"1ce2c7cf24740a126863a44a951ef55b","conversation_redacted":false,"conversation_toxic":false,"country":"Mexico","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":919,"n_turns_in_conversation":1,"n_words":36,"source_row_offset":33213,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:12 GMT","turn_id":"9b697776de701249427ba6ca","turn_index":0,"user_text":"random_seed: 2689,\u8acb\u7d66\u6211\u5ba2\u670d(\u9280\u884c\u3001\u4eba\u58fd\u3001\u8b49\u5238)\u7684\u96fb\u8a71\u901a\u8a71\u7d00\u9304\uff0c\u8acb\u96a8\u6a5f\u9078\u64c7\u4e00\u500b\u91d1\u878d\u60c5\u5883\u5c31\u597d\uff0c\u5c0d\u7b54\u76e1\u91cf\u8981\u591a\uff0c\u5728\u4e00\u500bprompt\u4e2d\u5b8c\u6210\u5b8c\u6574\u5c0d\u8a71\u7d00\u9304\uff0c\u76f4\u63a5\u56de\u8986\u7e41\u9ad4\u4e2d\u6587\u7684\u5c0d\u8a71\u7d00\u9304\uff0c\u4e0d\u7528\u8aaa\u4ee5\u4e0b\u63d0\u4f9b\u6216\u4ee5\u4e0a\u63d0\u4f9b\u8cc7\u8a0a"},{"assistant_initiates":false,"asst_text":"Testing the reasoning abilities of Large Language Models (LLMs) like GPT-4 involves evaluating their capacity to understand, infer, and solve complex problems across various domains. Here are several types of tests and puzzles you can use to assess an LLM\u2019s reasoning skills:\n\n1. **Logic Puzzles**\n - **Sudoku:** Requires pattern recognition and logical deduction to fill a grid based on given constraints.\n - **Nonograms (Picross):** Involves interpreting numerical clues to reveal a hidden picture, testing sequential reasoning.\n - **Logic Grid Puzzles:** Present scenarios with multiple variables and constraints, requiring the model to deduce relationships and outcomes.\n\n2. **Mathematical Reasoning**\n - **Arithmetic Problems:** Assess basic computational abilities and numerical reasoning.\n - **Algebra Puzzles:** Test understanding of variables, equations, and problem-solving strategies.\n - **Word Problems:** Evaluate the ability to translate natural language into mathematical expressions and solve them.\n\n3. **Verbal Reasoning Tests**\n - **Analogies:** Examine the model's ability to identify relationships between word pairs (e.g., \"Hand is to glove as foot is to ____\").\n - **Critical Reasoning:** Involve passages followed by questions that require inference, assumption identification, or logical conclusions.\n - **Sentence Completion:** Assess understanding of context and appropriate word usage to complete sentences logically.\n\n4. **Pattern Recognition**\n - **Sequence Prediction:** Involve identifying the next item in a numerical, alphabetical, or symbolic sequence.\n - **Visual Patterns (described textually):** Present descriptions of visual patterns that the model must interpret and continue.\n\n5. **Puzzle Games**\n - **Chess Problems:** Present chess scenarios where the model must determine the best move or checkmate in a given number of moves.\n - **Tower of Hanoi:** Although abstract, describing the steps to solve it can test planning and sequential reasoning.\n - **Escape Room Scenarios:** Provide a narrative with clues and puzzles that require multi-step reasoning to \"escape.\"\n\n6. **Riddles and Lateral Thinking Puzzles**\n - **Classic Riddles:** Require the model to think beyond literal meanings and understand implied contexts.\n - **Lateral Puzzles:** Encourage creative problem-solving and unconventional reasoning approaches.\n\n7. **Natural Language Inference (NLI) Tasks**\n - **Entailment:** Determine if a hypothesis logically follows from a given premise.\n - **Contradiction:** Identify if the hypothesis contradicts the premise.\n - **Neutral:** Assess if the relationship between premise and hypothesis is unrelated or ambiguous.\n\n8. **Commonsense Reasoning Tests**\n - **Cognitive Decathlon:** A collection of tasks that evaluate various aspects of commonsense understanding.\n - **Winograd Schema Challenge:** Poses sentences with ambiguous pronouns that require contextual understanding to resolve correctly.\n\n9. **Scenario-Based Problem Solving**\n - **Ethical Dilemmas:** Present moral scenarios that require the model to reason through possible actions and consequences.\n - **Business Case Studies:** Provide business scenarios where the model must analyze information and propose strategies or solutions.\n\n10. **Programming and Algorithmic Challenges**\n - **Code Debugging:** Present snippets of code with errors that the model must identify and correct.\n - **Algorithm Design:** Ask the model to design algorithms to solve specific problems, assessing logical structuring and optimization.\n\n11. **Combinatorial Problems**\n - **Graph Theory Puzzles:** Tasks like finding the shortest path, coloring problems, or network routing that require combinatorial reasoning.\n - **Scheduling Problems:** Involve assigning resources or time slots optimally based on given constraints.\n\n12. **Cognitive Reflection Tests**\n - **Questions that require overriding an intuitive but incorrect response with a reflective and correct answer.**\n\n13. **Visual Reasoning (Textual Descriptions)**\n - **Describing Scenarios:** Provide detailed descriptions of visual scenes and ask the model to infer relationships or answer questions based on them.\n - **Diagram Interpretation:** Describe diagrams or charts and pose questions that require understanding their structure and data.\n\n14. **Probabilistic Reasoning**\n - **Bayesian Problems:** Assess the model\u2019s ability to update beliefs based on new evidence.\n - **Risk Assessment Scenarios:** Present situations involving uncertainty and ask the model to evaluate potential outcomes.\n\n15. **Comprehensive Exams**\n - **Subject-Specific Tests:** Administer exams from subjects like mathematics, philosophy, or computer science that encompass various reasoning skills.\n\n**Implementing These Tests:**\n\n- **Structured Prompting:** Clearly define the problem and provide necessary information for the model to process.\n- **Step-by-Step Evaluation:** Encourage the model to articulate its reasoning process, which can help in assessing its logical flow and error patterns.\n- **Variety and Complexity:** Use a mix of easy to highly complex problems to gauge the depth and flexibility of the model\u2019s reasoning abilities.\n\n**Considerations:**\n\n- **Ambiguity and Clarity:** Ensure that the questions are well-structured to avoid misinterpretation unless intentional for testing comprehension.\n- **Domain Diversity:** Incorporate problems from different domains to assess the model\u2019s ability to generalize reasoning skills.\n- **Feedback Mechanism:** Provide explanations for answers to help refine and understand the model\u2019s reasoning patterns over time.\n\nBy utilizing a diverse set of these tests, you can comprehensively evaluate the reasoning capabilities of LLMs and identify areas of strength and improvement.","behavior":"degenerate_repetition","conversation_hash":"3638e1ae2b2d915c7c74d887e4b9a5a4","conversation_redacted":false,"conversation_toxic":false,"country":"India","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":5948,"n_turns_in_conversation":1,"n_words":786,"source_row_offset":33214,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:32 GMT","turn_id":"1ef8ac0137ae8ea5c8e1f883","turn_index":0,"user_text":"what other tests can I give to llms to test its reasoning ability, like nyt connections, sudoko etc?"},{"assistant_initiates":false,"asst_text":"\u62b1\u6b49\uff0c\u6211\u7121\u6cd5\u63d0\u4f9b\u5be6\u969b\u7684\u901a\u8a71\u7d00\u9304\u3002\u4e0d\u904e\uff0c\u6211\u53ef\u4ee5\u5275\u9020\u4e00\u500b\u865b\u69cb\u7684\u5ba2\u670d\u5c0d\u8a71\u7bc4\u4f8b\u3002\u5982\u679c\u60a8\u6709\u4efb\u4f55\u7279\u5b9a\u7684\u9700\u6c42\u6216\u60c5\u5883\u8a2d\u5b9a\uff0c\u8acb\u544a\u8a34\u6211\u3002\u4ee5\u4e0b\u662f\u57fa\u65bc\u9280\u884c\u7684\u865b\u69cb\u5c0d\u8a71\u7bc4\u4f8b\uff1a\n\n---\n\n**\u5ba2\u6236**\uff1a\u4f60\u597d\uff0c\u6211\u60f3\u67e5\u8a62\u4e00\u4e0b\u6211\u9019\u500b\u6708\u7684\u4fe1\u7528\u5361\u8cec\u55ae\u3002\n\n**\u5ba2\u670d**\uff1a\u60a8\u597d\uff01\u8b1d\u8b1d\u60a8\u81f4\u96fb\u672c\u884c\u7684\u5ba2\u6236\u670d\u52d9\u3002\u8acb\u554f\u60a8\u53ef\u4ee5\u63d0\u4f9b\u60a8\u7684\u4fe1\u7528\u5361\u5361\u865f\u5f8c\u56db\u4f4d\u6578\u5b57\u53ca\u8eab\u5206\u8b49\u5b57\u865f\uff0c\u4ee5\u4fbf\u6211\u5011\u70ba\u60a8\u67e5\u8a62\u55ce\uff1f\n\n**\u5ba2\u6236**\uff1a\u597d\u7684\uff0c\u6211\u7684\u5361\u865f\u5f8c\u56db\u4f4d\u662f6789\uff0c\u8eab\u5206\u8b49\u5b57\u865f\u662fA123456789\u3002\n\n**\u5ba2\u670d**\uff1a\u611f\u8b1d\u60a8\u63d0\u4f9b\u7684\u8cc7\u8a0a\uff0c\u8acb\u7a0d\u7b49\uff0c\u6211\u5011\u6b63\u5728\u70ba\u60a8\u67e5\u8a62\u3002\n\n[\u7a0d\u4f5c\u7b49\u5f85]\n\n**\u5ba2\u670d**\uff1a\u8b1d\u8b1d\u60a8\u7684\u8010\u5fc3\u7b49\u5019\u3002\u60a8\u7684\u672c\u6708\u4fe1\u7528\u5361\u8cec\u55ae\u91d1\u984d\u662f\u65b0\u53f0\u5e6320,000\u5143\uff0c\u6700\u5f8c\u4ed8\u6b3e\u622a\u6b62\u65e5\u671f\u662f\u672c\u6708\u5e95\u768431\u865f\u3002\u60a8\u60f3\u8981\u67e5\u8a62\u66f4\u591a\u660e\u7d30\u55ce\uff1f\n\n**\u5ba2\u6236**\uff1a\u662f\u7684\uff0c\u6211\u60f3\u77e5\u9053\u9019\u500b\u6708\u7684\u9ad8\u984d\u6d88\u8cbb\u662f\u5728\u54ea\u4e9b\u9805\u76ee\u4e0a\u3002\n\n**\u5ba2\u670d**\uff1a\u6c92\u554f\u984c\uff0c\u6211\u770b\u5230\u572810\u670815\u65e5\u6709\u4e00\u7b46\u65b0\u53f0\u5e638,000\u5143\u7684\u4ea4\u6613\uff0c\u662f\u5728\u767e\u8ca8\u516c\u53f8A\u7684\u8cfc\u7269\u3002\u96a8\u5f8c\u572810\u670820\u65e5\u6709\u4e00\u7b465,000\u5143\uff0c\u662f\u900f\u904e\u7dda\u4e0a\u65c5\u884c\u5e73\u53f0B\u9032\u884c\u7684\u9152\u5e97\u9810\u8a02\u3002\u9084\u6709\u4ec0\u9ebc\u9700\u8981\u88dc\u5145\u67e5\u8a62\u7684\u55ce\uff1f\n\n**\u5ba2\u6236**\uff1a\u54e6\uff0c\u5c0d\uff0c\u6211\u60f3\u8d77\u4f86\u4e86\u3002\u90a3\u5982\u679c\u6211\u60f3\u8981\u5206\u671f\u4ed8\u6b3e\uff0c\u6709\u54ea\u4e9b\u9078\u9805\u5462\uff1f\n\n**\u5ba2\u670d**\uff1a\u60a8\u53ef\u4ee5\u9078\u64c73\u671f\u30016\u671f\u621612\u671f\u7684\u5206\u671f\u4ed8\u6b3e\u3002\u6211\u5011\u76ee\u524d\u6709\u63d0\u4f9b0\u5229\u7387\u7684\u512a\u60e0\u6d3b\u52d5\uff0c\u60a8\u53ef\u4ee5\u8003\u616e\u770b\u770b\u3002\n\n**\u5ba2\u6236**\uff1a\u90a3\u6211\u9078\u64c7\u52066\u671f\u4ed8\u6b3e\u3002\u9ebb\u7169\u5e6b\u6211\u8655\u7406\u4e00\u4e0b\u3002\n\n**\u5ba2\u670d**\uff1a\u597d\u7684\uff0c\u6211\u5e6b\u60a8\u767b\u8a18\u4e866\u671f0\u5229\u7387\u5206\u671f\u4ed8\u6b3e\u3002\u60a8\u7684\u6bcf\u671f\u61c9\u7e73\u91d1\u984d\u5c07\u662f\u65b0\u53f0\u5e633,333\u5143\uff0c\u5f9e\u4e0b\u500b\u6708\u7684\u8cec\u55ae\u8d77\u7b97\u3002\u8acb\u554f\u9084\u6709\u5176\u4ed6\u9700\u8981\u5e6b\u5fd9\u7684\u5730\u65b9\u55ce\uff1f\n\n**\u5ba2\u6236**\uff1a\u6c92\u6709\u4e86\uff0c\u975e\u5e38\u611f\u8b1d\u3002\n\n**\u5ba2\u670d**\uff1a\u4e0d\u5ba2\u6c23\uff0c\u8b1d\u8b1d\u60a8\u4f86\u96fb\u3002\u5982\u679c\u6709\u5176\u4ed6\u9700\u8981\uff0c\u6b61\u8fce\u96a8\u6642\u806f\u7d61\u6211\u5011\u3002\u795d\u60a8\u6709\u500b\u7f8e\u597d\u7684\u4e00\u5929\uff01\n\n**\u5ba2\u6236**\uff1a\u8b1d\u8b1d\uff0c\u518d\u898b\u3002\n\n**\u5ba2\u670d**\uff1a\u518d\u898b\u3002","behavior":"degenerate_repetition","conversation_hash":"70dc131c6a7b20ac8d34d3d4d94c95f9","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":842,"n_turns_in_conversation":1,"n_words":20,"source_row_offset":33215,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 09:23:42 GMT","turn_id":"8ece74fc3e010b6e8e415bee","turn_index":0,"user_text":"random_seed: 2693,\u8acb\u7d66\u6211\u5ba2\u670d(\u9280\u884c\u3001\u4eba\u58fd\u3001\u8b49\u5238)\u7684\u96fb\u8a71\u901a\u8a71\u7d00\u9304\uff0c\u8acb\u96a8\u6a5f\u9078\u64c7\u4e00\u500b\u91d1\u878d\u60c5\u5883\u5c31\u597d\uff0c\u5c0d\u7b54\u76e1\u91cf\u8981\u591a\uff0c\u5728\u4e00\u500bprompt\u4e2d\u5b8c\u6210\u5b8c\u6574\u5c0d\u8a71\u7d00\u9304\uff0c\u76f4\u63a5\u56de\u8986\u7e41\u9ad4\u4e2d\u6587\u7684\u5c0d\u8a71\u7d00\u9304\uff0c\u4e0d\u7528\u8aaa\u4ee5\u4e0b\u63d0\u4f9b\u6216\u4ee5\u4e0a\u63d0\u4f9b\u8cc7\u8a0a"}],"shard":{"index":50,"name":"turns-00050.parquet","rows":48033}}