opinions_qa | This repository contains the code and data for our paper

by tatsu-lab Jupyter Notebook Version: Current License: No License

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kandi X-RAY | opinions_qa Summary

opinions_qa is a Jupyter Notebook library. opinions_qa has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This repository contains the code and data for our paper:. Whose Opinions Do Language Models Reflect? Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto Paper:

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Support

opinions_qa has a low active ecosystem.

It has 13 star(s) with 2 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

opinions_qa has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of opinions_qa is current.

Quality

opinions_qa has no bugs reported.

Security

opinions_qa has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

opinions_qa does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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opinions_qa releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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opinions_qa Key Features

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opinions_qa Examples and Code Snippets

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Vulnerabilities

No vulnerabilities reported

Install opinions_qa

You can start by cloning our repository and following the steps below.
Download and the OpinionQA dataset in ./data. Included as part of the dataset are: (i) model_input: 1498 multiple-choice questions based on Pew American Trends Panel surveys that can be used to probe LMs, (ii) human_resp: individualized human responses for these questions from Pew, and (iii) runs: pre-computed responses for OpenAI and AI21 Labs models studied in our paper.
Compute human and LM opinion distributions using this notebook.
You can explore human-LM alignment along various axes using the following notebooks: representativeness, steerability, consistency and refusals.
(Optional) If you would like to query models yourself, you will need to set up the crfm-helm Python package.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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