Summary of Large Language Models Are Biased Reinforcement Learners, by William M. Hayes et al.
Large Language Models are Biased Reinforcement Learners
by William M. Hayes, Nicolas Yax, Stefano Palminteri
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper investigates how large language models (LLMs) perform reinforcement learning tasks, such as making reward-maximizing choices in simple bandit tasks. The study focuses on whether these models exhibit biases when encoding rewarding outcomes, similar to those observed in humans. Results show that LLMs do exhibit a relative value bias, which affects their performance and generalization abilities. When given explicit outcome comparisons, some models improve maximization but impair generalization, while others exhibit the opposite effect. The findings are explained by a simple reinforcement learning algorithm that incorporates relative values during outcome encoding. The study’s implications highlight the importance of understanding LLM biases for decision-making applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models learn to make good choices in different situations. It wants to know if these models are biased when they decide what’s good or bad, like humans are. The results show that the models do have a bias and it affects how well they do their job. When given more information about how things compare to each other, some models get better at making good choices but struggle with new situations, while others get worse. The study helps us understand why this happens and what it means for using these models in real-life decisions. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
