Summary of An Empirical Analysis on Large Language Models in Debate Evaluation, by Xinyi Liu et al.
An Empirical Analysis on Large Language Models in Debate Evaluation
by Xinyi Liu, Pinxin Liu, Hangfeng He
First submitted to arxiv on: 28 May 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 study investigates the capabilities and biases of advanced large language models (LLMs) GPT-3.5 and GPT-4 in debate evaluation. These LLMs outperform humans and state-of-the-art methods on extensive datasets. The analysis reveals various biases, including positional bias, lexical bias, order bias, which affect their evaluative judgments. Specifically, the models exhibit a consistent bias towards the second candidate response presented due to prompt design, as well as lexical biases when label sets carry connotations such as numerical or sequential. Furthermore, both models tend to favor the concluding side as the winner, indicating an end-of-discussion bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how good big language models are at judging debates and if they have any biases. These models can do better than humans and other special techniques that have been tested on lots of data. The researchers found out that these models have some built-in preferences, like preferring the second answer given or being influenced by certain words. They also noticed that these models tend to pick the side that’s at the end as the winner. |
Keywords
» Artificial intelligence » Gpt » Prompt
