Summary of Llms May Perform Mcqa by Selecting the Least Incorrect Option, By Haochun Wang et al.
LLMs May Perform MCQA by Selecting the Least Incorrect Option
by Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, Ting Liu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper focuses on assessing the performance of Large Language Models (LLMs) in Natural Language Processing (NLP). While MCQA has gained popularity as a benchmark, concerns arise about its robustness. This study reveals that LLMs may not necessarily choose the correct answer but rather select the least incorrect option. This suggests that multiple options might be considered correct, potentially undermining the reliability of MCQA. To address this issue, the authors introduce an enhanced dataset augmentation method for MCQA, dubbed MCQA+, to provide a more accurate reflection of model performance. The paper highlights the need for sophisticated evaluation mechanisms in assessing LLM capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about how good language models are at answering questions with multiple correct answers. Right now, we use a method called Multiple Choice Question Answering (MCQA) to test these models, but some experts worry that this method might not be reliable. The researchers found that the language models don’t always choose the correct answer, but instead pick the least wrong one. This means that the models think multiple answers are correct, which could make MCQA less accurate. To fix this problem, the authors created a new way to test the models called MCQA+, which should give us a better idea of how well they’re really doing. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Question answering
