Summary of Reinforcement Learning For Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting, by Mohamed Salim Aissi et al.
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting
by Mohamed Salim Aissi, Clement Romac, Thomas Carta, Sylvain Lamprier, Pierre-Yves Oudeyer, Olivier Sigaud, Laure Soulier, Nicolas Thome
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel framework is proposed to analyze the sensitivity of large language models (LLMs) to prompt formulations following reinforcement learning (RL) training in a textual environment. The study reveals that LLM performance degrades when faced with prompts different from those used during RL training, and attributes this sensitivity to changes in internal representations and salient tokens. To mitigate this sensitivity and improve robustness, the authors suggest using a contrastive loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how fine-tuning large language models with reinforcement learning affects their abilities. Researchers found that when these models are given new prompts, they don’t perform well if those prompts are different from what they were trained on. This is because the models change internally to fit the original training prompts. To make them more robust, the study suggests using a special type of loss function. |
Keywords
» Artificial intelligence » Contrastive loss » Fine tuning » Loss function » Prompt » Reinforcement learning
