Related papers: Multi-Armed Bandits Meet Large Language Models

Multi-Armed Bandits Meet Large Language Models

URL: http://arxiv.org/abs/2505.13355v1
Date: Mon, 19 May 2025 16:57:57 GMT
Title: Multi-Armed Bandits Meet Large Language Models
Authors: Djallel Bouneffouf, Raphael Feraud,
Abstract summary: Bandit algorithms and Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence.<n>We first examine the role of bandit algorithms in optimizing LLM fine-tuning, prompt engineering, and adaptive response generation.<n>We then explore how LLMs can augment bandit algorithms through advanced contextual understanding, dynamic adaptation, and improved policy selection using natural language reasoning.
Score: 6.320763879009171
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Bandit algorithms and Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, each addressing distinct yet complementary challenges in decision-making and natural language processing. This survey explores the synergistic potential between these two fields, highlighting how bandit algorithms can enhance the performance of LLMs and how LLMs, in turn, can provide novel insights for improving bandit-based decision-making. We first examine the role of bandit algorithms in optimizing LLM fine-tuning, prompt engineering, and adaptive response generation, focusing on their ability to balance exploration and exploitation in large-scale learning tasks. Subsequently, we explore how LLMs can augment bandit algorithms through advanced contextual understanding, dynamic adaptation, and improved policy selection using natural language reasoning. By providing a comprehensive review of existing research and identifying key challenges and opportunities, this survey aims to bridge the gap between bandit algorithms and LLMs, paving the way for innovative applications and interdisciplinary research in AI.

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