Summary of The Generative Ai Paradox on Evaluation: What It Can Solve, It May Not Evaluate, by Juhyun Oh et al.
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate
by Juhyun Oh, Eunsu Kim, Inha Cha, Alice Oh
First submitted to arxiv on: 9 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 As we investigate whether Large Language Models (LLMs) that excel in generation tasks can also accurately evaluate answers, our study reveals a significant performance disparity. We analyze the capabilities of three LLMs and one open-source LM on Question-Answering (QA) and evaluation tasks using the TriviaQA dataset. Our findings indicate that these models perform worse in evaluation tasks compared to their performance in generation tasks. Moreover, we identify instances where LLMs accurately evaluate answers in areas where they lack competence, emphasizing the importance of examining the faithfulness and trustworthiness of LLMs as evaluators. This research contributes to the understanding of “the Generative AI Paradox,” highlighting the need to explore the correlation between generative excellence and evaluation proficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart at generating text, but can they also correctly answer questions? Our study shows that these models don’t do as well when evaluating answers as they do when creating new text. We tested three powerful LLMs and one open-source model on a tricky question-and-answer test called TriviaQA. The results showed that the models were much worse at answering questions than they were at generating text. But here’s the really interesting part: sometimes these models can correctly evaluate answers even if they don’t know the right answer themselves! This means we need to be careful when using these powerful AI models as evaluators. |
Keywords
» Artificial intelligence » Question answering
