UQ: Assessing Language Models on Unsolved Questions

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Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

Can AI Answer the Unanswerable? A New Test for Language Models Using Unsolved Problems

This paper introduces "UQ," a new benchmark for AI that uses unsolved problems sourced from Stack Exchange. It uses a combination of automated filtering and human review to select questions and also utilizes an LLM-based validation system to assess AI-generated answers before human verification. Initial results show current AI models struggle with these hard questions, but the system allows for continuous, community-driven evaluation.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Heavy reliance on human evaluation

Since there are no right answers readily available for validation, human experts are still needed to verify the proposed solutions. The process of human verification can be time-consuming and potentially inconsistent depending on the expertise and availability of human reviewers.

Limited domain coverage of the UQ dataset

The current UQ dataset is heavily skewed towards STEM fields and may not accurately reflect the performance of language models on problems in other domains, such as humanities or social sciences. It would be beneficial to diversify the question pool to include a broader range of disciplines.

Potential bias in the UQ dataset introduced by manual selection

The reliance on user contributions and expert reviews might introduce biases in question selection and solution verification. This is particularly true in the early stages of the platform where the user base is small and likely to be less representative of the overall expert community.

Sustainability of the UQ platform

The success of the UQ platform depends heavily on community engagement and the availability of expert reviewers. It remains to be seen whether the platform can attract and maintain a sufficiently large and active user base for continuous, reliable evaluation.

Rating Explanation

This paper presents a novel and promising approach to evaluating large language models by assessing their performance on unsolved questions. The methodology is sound and addresses important limitations of current benchmarks. The creation of the UQ dataset, validation strategies, and the open platform contributes significantly to the field. Although the reliance on human evaluation and the current domain concentration are limitations, the overall impact and potential of the approach warrant a strong rating.

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