Summary of Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models For Multiple-choice Questions, by Liman Wang et al.
Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions
by Liman Wang, Hanyang Zhong, Wenting Cao, Zeyuan Sun
First submitted to arxiv on: 16 Jun 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 This study investigates the role of cognitive biases in large language models’ (LLMs) decision-making processes, disputing the conventional goal of eliminating all biases. The authors demonstrate that certain cognitive biases can enhance decision-making efficiency when properly balanced, utilizing rational deviations and heuristic shortcuts. By introducing heuristic moderation and an abstention option, which allows LLMs to withhold responses when uncertain, error rates are reduced, decision accuracy is improved, and optimal decision rates are achieved. The findings are validated using the Balance Rigor and Utility (BRU) dataset, developed through expert collaboration. This approach offers a novel way to leverage cognitive biases to improve the practical utility of LLMs across various applications, such as natural language processing, question answering, and text summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big computer programs called large language models make decisions. Instead of trying to get rid of all mistakes, this research shows that some “mistakes” can actually help the program make better choices. The team found a way to improve the program’s decision-making by letting it sometimes say “I’m not sure” instead of making a guess. This helps reduce errors and makes the program more reliable. The researchers used a special set of examples to test their ideas, and they found that this new approach works well for big computer programs like these language models. |
Keywords
» Artificial intelligence » Natural language processing » Question answering » Summarization
