Should You Use Your Large Language Model to Explore or Exploit?

2025-01-28 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A January 2025 study evaluated Gpt-4, Gpt-4o, and Gpt-3.5's capabilities in exploration-exploitation tradeoffs for decision-making agents, specifically in contextual and multi-armed bandit tasks. Researchers found that while LLMs generally struggle with exploitation, even with in-context mitigations, their performance on small-scale tasks improved but remained inferior to a simple linear regression baseline. Exploitation accuracy degraded as history length increased and empirical gap decreased. Conversely, LLMs proved effective at exploration, particularly in large action spaces with inherent semantics. They successfully suggested candidate actions for text-based multi-armed bandit problems, including generating answers to open-ended questions and proposing arXiv paper titles, demonstrating their utility in narrowing down high-dimensional action spaces.

Key takeaway

For Machine Learning Engineers designing agents for decision-making under uncertainty, avoid using LLMs as primary exploitation mechanisms in bandit tasks; they perform worse than simpler models like linear regression. Instead, strategically integrate LLMs as powerful exploration oracles. Leverage their ability to generate diverse, semantically relevant candidate actions in large spaces, then pair this with traditional, robust algorithms for the exploitation phase to build more effective hybrid decision-making systems.

Key insights

LLMs underperform in exploitation tasks but effectively explore large, semantically rich action spaces.

Principles

• LLMs struggle with exploitation in bandit tasks.
• In-context mitigations boost LLM exploitation, but often fall short.
• LLMs excel at exploring large, semantic action spaces.

Method

LLMs are evaluated as "exploitation oracles" (best action from history) and "exploration oracles" (candidate action generation) within contextual bandit frameworks.

In practice

• Employ LLMs for generating diverse candidate actions.
• Use in-context mitigations for small-scale exploitation.
• Prefer linear regression for robust exploitation.

Topics

• Large Language Models
• Exploration-Exploitation
• Contextual Bandits
• Reinforcement Learning
• In-context Learning
• Decision-Making Agents

Best for: NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.