Human Judgments On Model Conversations

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Human Judgments on Model Conversations

Human Judgments on Conversational Models

By lmsys (From Huggingface) [source]

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About this dataset

The dataset is structured with several columns that provide valuable information. The model_a and model_b columns indicate the names or identifiers of the first and second models involved in each conversation. The winner column specifies which model was judged to have performed better in a particular conversation.

Each conversation is represented by two separate columns: conversation_a and conversation_b. These columns contain the text generated by model_a and model_b respectively.

The turn number of each conversation is recorded in the column labeled as turn. This helps to track and analyze different stages or rounds within a conversation.

To facilitate data organization, some columns such as model_a, model_b, winner, and turn are duplicated for easy reference.

This dataset serves as a valuable resource for understanding human judgments on conversations generated by different models. The context provided by having both conversations from multiple models as well as expert evaluations can be instrumental in developing advanced conversational AI systems

How to use the dataset

Introduction:

Dataset Overview:

• human.csv: This file contains detailed judgments by humans regarding model conversations.
  • Columns include:
    • model_a: The name or identifier of the first model in the conversation.
    • model_b: The name or identifier of the second model in the conversation.
    • winner: The model that was judged to have performed better in the conversation.
    • conversation_a: The conversation generated by model_a.
    • conversation_b: The conversation generated by model_b.
    • turn: The turn number in the conversation.
• gpt4_pair.csv: This file provides an overview of different aspects such as versions, winners, judges, and actual conversations.
  • Columns include:
    • Same as 'human.csv' except there is no repetition (e.g., only one occurrence for each column).

Both files aim to capture human judgments from diverse conversational scenarios.

Guide: How to Use this Dataset:

• Research Analysis: Researchers can leverage this dataset to analyze how models perform against each other in generating conversational responses. By examining which models were considered superior (as determined by human judges), researchers can gain insights into model strengths and weaknesses.

• Model Development: For developers working on conversational AI models, this dataset can serve as a benchmark for evaluating and enhancing their models. The winner column provides a reference point regarding the preferred model performance.

• Model Comparison: This dataset enables users to compare different models and observe their conversation quality through human judgment. By examining conversations from multiple models, users can identify trends or patterns that contribute to better conversational outcomes.

• Model Validation: The judgments made by human judges in this dataset provide valuable validation data for AI models' conversational capabilities. Developers can use these human evaluations as a benchmark for measuring the effectiveness of their own models.

• **Natural Language Processing (NLP) Tasks

Research Ideas

• Evaluating and comparing the performance of different models in generating conversations: This dataset allows researchers to compare the performance of different language models by examining the judgments made by human judges. It can be used to analyze which model performs better in terms of generating coherent and contextually appropriate conversations.
• Training and improving conversational AI systems: The dataset can be used to train conversational AI systems by using the human judgments as training labels. By training on this dataset, developers can improve their models' ability to generate high-quality conversations.
• Analyzing biases in conversational AI systems: Researchers can analyze this dataset to identify any biases or preferences that may exist in the judgments made by human judges. This analysis can help understand how these biases may influence the performance evaluation of different models and shed light on potential ethical concerns related to conversational AI technologies

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: human.csv

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File: gpt4_pair.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit lmsys (From Huggingface).