Summary of When Not to Answer: Evaluating Prompts on Gpt Models For Effective Abstention in Unanswerable Math Word Problems, by Asir Saadat et al.
When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems
by Asir Saadat, Tasmia Binte Sogir, Md Taukir Azam Chowdhury, Syem Aziz
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research explores the capabilities of large language models (LLMs) in solving complex mathematical word problems. While GPT models are widely used, they may generate inaccurate results when presented with unanswerable questions. The study investigates whether GPTs can effectively respond to unanswerable math word problems and enhance their abstention capabilities. The authors utilize the Unanswerable Word Math Problem (UWMP) dataset and apply prompts typically used in solvable mathematical scenarios. Evaluation metrics are introduced, integrating abstention, correctness, and confidence. The findings reveal critical gaps in GPT models and highlight the need for improved models capable of managing uncertainty and complex reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can solve math problems. But sometimes they get stuck on questions they can’t answer and might give wrong answers. Researchers looked at how these models, called GPTs, handle unanswerable math word problems. They used a special dataset of math problems with no answers and tested the GPTs to see if they could stop giving answers when they got stuck. The results showed that the GPTs aren’t very good at knowing when to give up and might give wrong answers instead. This means we need better models that can handle tricky math problems. |
Keywords
» Artificial intelligence » Gpt
