Respan Dataset Explorer

Select one behavior. Every returned turn has one binary label: Present or Absent. Source: final dense boolean release.

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Shard Start offset Rows Behavior Label Random row JSON API

turns-00040.parquet:18279

cac9c3f45c2adefe625826bc

turn 1/1gpt-4o-2024-08-06EnglishUnited States522 words

degenerate_repetitionAbsentFinal dense release

USER

System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Our counterparties for critical financial relationships may include both domestic and international financial institutions. These institutions could be severely impacted by credit market turmoil, changes in legislation, changes in regulation, allegations of civil or criminal wrongdoing and may as a result experience financial or other pressures. In addition, if a lender or counterparty files for bankruptcy or becomes insolvent, our borrowings under financing agreements with them may become subject to bankruptcy or insolvency proceedings, thus depriving us, at least temporarily, of the benefit of these assets. Such an event could restrict our access to financing and increase our cost of capital. If any of our counterparties were to limit or cease operation, it could lead to financial losses for us.
Assistant:

ASSISTANT

```json
{
    "relevant_tasks": [
        {
            "task": "Risk Classification",
            "examples": [
                {
                    "input": "The paragraph discusses the risk of financial losses due to counterparties facing turmoil or insolvency.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph highlights opportunities for profit increase due to stable financial institutions.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Entity Recognition",
            "examples": [
                {
                    "input": "Financial institutions and counterparties are identified as entities in the paragraph.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph identifies specific individuals involved in the financial agreements.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tense Identification",
            "examples": [
                {
                    "input": "The paragraph expresses potential future risks and consequences.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph describes events that have already happened in the past.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Impact Classification",
            "examples": [
                {
                    "input": "The paragraph indicates a negative impact on financial access and cost of capital if counterparties face issues.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph suggests a positive impact on financial stability due to diversification of counterparties.",
                    "label": "non-entailment"
                }
            ]
        }
    ]
}
```

turns-00040.parquet:18280

6283bd7776400bfe1768a0b7

turn 1/1gpt-4o-2024-08-06EnglishUnited States413 words

degenerate_repetitionAbsentFinal dense release

USER

System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

review: @AppleSupport my phone has been charging since 10 am and it's now 1:34 pm and it's at the same battery level :-(
intention: bug report
review: @Snapchat could use a website feature for regular computers just to watch snaps
intention: feature request
review: @Windows comes up third in OS clash two years early _URL_ via @Computerworld
intention: other
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

ASSISTANT

feature request

turns-00040.parquet:18281

6c45ae1a91f898eb87e92222

turn 1/1gpt-4o-2024-08-06RussianUnited States32 words

degenerate_repetitionAbsentFinal dense release

USER

Привет. Какая ты модель ИИ?

ASSISTANT

Привет! Я - ChatGPT, модель ИИ, разработанная OpenAI. Моя главная задача - помогать пользователям, отвечая на вопросы и предоставляя информацию по широкому кругу тем. Чем могу помочь?

turns-00040.parquet:18282

a171c8db5dc1d4266c140dd4

turn 1/1gpt-4o-2024-08-06EnglishUnited States632 words

degenerate_repetitionAbsentFinal dense release

USER

System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true).

Step 1: Read the paragraph carefully.
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to:
- Topic Classification
- Risk Classification
- Sentiment Classification
- Tone Classification
- Event Detection
- Entity Recognition
- Geographical Recognition
- Trend Detection
- Intent Detection
- Sector Identification
- Impact Classification
- Tense Identification (Past, Present, Future)
- Uncertainty Identification

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person.
Step 4: Output the results in the following JSON format:

{
'relevant_tasks': [
{
'task': <The automatically selected relevant task>,
'examples': [
{
'input': <The entailment example based on the paragraph>,
'label': 'entailment',
},
{
'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
'label': 'non-entailment',
}
]
},
...
]
}
User: We conduct substantially all of our operations and own substantially all of our assets through our holding company subsidiary, Holdco. Holdco has guaranteed certain obligations of our subsidiaries under various arrangements that provide for significant aggregate borrowings. Holdco may in the future guarantee the performance of additional subsidiaries’ obligations. The guarantee agreements contain financial covenants covering liquid assets, debt-to-equity ratio, interest coverage ratio, and net worth requirements. Holdco’s failure to satisfy these covenants and other requirements could result in defaults under the relevant agreements and acceleration of amounts borrowed thereunder. Such defaults could have a material adverse effect on us. We may also agree to guarantee indebtedness incurred by a joint venture or co-investment partner. Such a guarantee may be on a joint and several basis with such joint venture or co-investment partner, in which case we may be liable in the event such partner defaults on its guarantee obligation. The non-performance of such obligations may cause losses to us in excess of the capital we initially may have invested or committed under such obligations and there is no assurance that we will have sufficient capital to cover any such losses.
Assistant:

ASSISTANT

```json
{
    "relevant_tasks": [
        {
            "task": "Risk Classification",
            "examples": [
                {
                    "input": "The paragraph suggests that Holdco's failure to meet financial covenants could lead to defaults and significant financial risks for the company.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph implies that Holdco faces no financial risks, even if it fails to meet its financial covenants.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tone Classification",
            "examples": [
                {
                    "input": "The paragraph is written in a cautionary tone, expressing concerns about potential defaults and financial risks.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph has an optimistic tone, with no indication of any financial risks or potential defaults.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Impact Classification",
            "examples": [
                {
                    "input": "The paragraph describes a scenario where the failure to meet obligations could have a material adverse effect on the company.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph indicates that failing to meet obligations will have no adverse impact on the company.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tense Identification",
            "examples": [
                {
                    "input": "The paragraph is predominantly describing future events and hypothetical scenarios related to financial risks and guarantees.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph entirely discusses past events without any reference to future risks or obligations.",
                    "label": "non-entailment"
                }
            ]
        }
    ]
}
```

turns-00040.parquet:18283

2549a40655e75919aacc44e5

turn 1/1gpt-4o-2024-08-06EnglishItaly421 words

degenerate_repetitionAbsentFinal dense release

USER

ASSISTANT

other

turns-00040.parquet:18284

fd2815abc1bd9f38b7ba76b1

turn 1/1gpt-4o-2024-08-06EnglishJapan417 words

degenerate_repetitionAbsentFinal dense release

USER

ASSISTANT

bug report

turns-00040.parquet:18285

375eea9491b7d0520eb116ec

turn 1/1gpt-4o-2024-08-06RussianUnited States73 words

degenerate_repetitionAbsentFinal dense release

USER

User: Привет. Какая ты модель ИИ?
Assistant: Привет! Я - ChatGPT, модель ИИ, разработанная OpenAI. Моя главная задача - помогать пользователям, отвечая на вопросы и предоставляя информацию по широкому кругу тем. Чем могу помочь?
User: Ты GPT-4o?
Assistant:

ASSISTANT

Я являюсь моделью на основе GPT-3.5, разработанной OpenAI. Хотя я не GPT-4, я всё же могу помочь вам с широким спектром вопросов и задач. Если у вас есть какие-либо вопросы или задачи, не стесняйтесь обращаться!

turns-00040.parquet:18286

5d3750845c40889ee40897a7

turn 1/1gpt-4o-2024-08-06Englishunknown country422 words

degenerate_repetitionAbsentFinal dense release

USER

ASSISTANT

bug report

turns-00040.parquet:18287

851a6c51f58db0e25ae627c4

turn 1/1gpt-4o-2024-08-06EnglishDenmark417 words

degenerate_repetitionAbsentFinal dense release

USER

ASSISTANT

bug report

turns-00040.parquet:18288

b7ddb31b0be90e33559bd86c

turn 1/1gpt-4o-2024-08-06EnglishJapan678 words

degenerate_repetitionAbsentFinal dense release

USER

System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: The illiquidity of certain of our loans and other investments may make it difficult for us to sell such loans and other investments if the need or desire arises. In addition, certain of our loans and other investments may become less liquid after we originate or acquire them as a result of periods of delinquencies or defaults or turbulent market conditions, including due to current market conditions and exacerbated market volatility, which may make it more difficult for us to dispose of such loans and other investments at advantageous times or in a timely manner. Moreover, we expect that many of our investments are not or will not be registered under the relevant securities laws, resulting in prohibitions against their transfer, sale, pledge or their disposition except in transactions that are exempt from registration requirements or are otherwise in accordance with such laws. As a result, many of our loans and other investments are or will be illiquid, and if we are required to liquidate all or a portion of our portfolio quickly, for example as a result of margin calls, we may realize significantly less than the value at which we have previously recorded our investments. For a discussion of losses that we recorded from sales of our CRE debt securities that we made in connection with margin calls against our former CRE debt securities portfolio in March and April of 2020, see “—Risks Related to Our Financing —Our financing arrangements may require us to provide additional collateral or repay debt.”
Assistant:

ASSISTANT

```json
{
    "relevant_tasks": [
        {
            "task": "Risk Classification",
            "examples": [
                {
                    "input": "The paragraph entails high financial risk due to the illiquidity of loans and investments.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph indicates that the company's loans and investments are highly liquid and pose minimal risk.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tense Identification",
            "examples": [
                {
                    "input": "The paragraph primarily discusses the present tense situation regarding the illiquidity of loans.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph predominantly refers to future changes in the liquidity status of investments.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Uncertainty Identification",
            "examples": [
                {
                    "input": "The paragraph reflects uncertainty about realizing the recorded value of investments during rapid liquidation.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph assures certainty that all investments will be sold at their recorded value regardless of market conditions.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Event Detection",
            "examples": [
                {
                    "input": "The paragraph refers to past events in March and April 2020 involving sales of CRE debt securities.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph refers to a future event where CRE debt securities will be sold at a profit.",
                    "label": "non-entailment"
                }
            ]
        }
    ]
}
```