Summary of Cognitive Biases in Large Language Models: a Survey and Mitigation Experiments, by Yasuaki Sumita et al.
Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments
by Yasuaki Sumita, Koh Takeuchi, Hisashi Kashima
First submitted to arxiv on: 30 Nov 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 proposed research explores the potential limitations of Large Language Models (LLMs) in making rational decisions due to their susceptibility to cognitive biases. As humans are prone to irrational judgments, it is likely that LLMs can also be influenced by these biases, leading to suboptimal decision-making. The study first reviews existing literature on the topic and then examines two mitigation methods inspired by crowdsourcing techniques. These methods, SoPro and AwaRe, were applied to GPT-3.5 and GPT-4 to evaluate their effectiveness in reducing the influence of six cognitive biases on output responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. But just like humans, they can also make mistakes because of how our brains work. This study looks at how LLMs might make bad decisions if they’re biased the same way we are. They tested two ways to fix this problem by looking at how well GPT-3.5 and GPT-4 perform before and after using these methods. |
Keywords
» Artificial intelligence » Gpt
