Summary of Beyond Numeric Awards: In-context Dueling Bandits with Llm Agents, by Fanzeng Xia et al.
Beyond Numeric Awards: In-Context Dueling Bandits with LLM Agents
by Fanzeng Xia, Hao Liu, Yisong Yue, Tongxin Li
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 In-context reinforcement learning (ICRL) is a promising paradigm for solving complex problems using large language models (LLMs). This paper explores LLMs’ capabilities as in-context decision-makers under the Dueling Bandits (DB) framework, which extends the classic Multi-Armed Bandit model by incorporating preference feedback. We compare five LLMs against nine well-established DB algorithms and find that GPT-4 Turbo achieves surprisingly low weak regret without training on the task. However, an optimality gap exists between LLMs and classic DB algorithms in terms of strong regret. To bridge this gap, we propose LEAD (LLM with Enhanced Algorithmic Dueling), which integrates off-the-shelf DB algorithms with LLM agents through fine-grained adaptive interplay. We validate LEAD’s efficacy and robustness even with noisy and adversarial prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using large language models to make decisions in uncertain situations. It tests how well these models can learn from experience and make good choices without being specifically trained for a task. The researchers compared these models against other approaches and found that one model, GPT-4 Turbo, does surprisingly well without training. However, they also found that the models still have some limitations and need to be improved. To fix this, they came up with an idea called LEAD, which combines the language models with other decision-making strategies to make better choices. |
Keywords
* Artificial intelligence * Gpt * Reinforcement learning
