EVOLvE: Evaluating and Optimizing LLMs For Exploration (2410.06238v1)

Abstract: Despite their success in many domains, LLMs remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many real-world applications, ranging from personalized recommendations to healthcare interventions, demand that LLMs not only predict but also actively learn to make optimal decisions through exploration. In this work, we measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. We develop a comprehensive suite of environments, including both context-free and contextual bandits with varying task difficulties, to benchmark LLMs' performance. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs: by providing explicit algorithm-guided support during inference; and through algorithm distillation via in-context demonstrations and fine-tuning, using synthetic data generated from these algorithms. Impressively, these techniques allow us to achieve superior exploration performance with smaller models, surpassing larger models on various tasks. We conducted an extensive ablation study to shed light on various factors, such as task difficulty and data representation, that influence the efficiency of LLM exploration. Additionally, we conduct a rigorous analysis of the LLM's exploration efficiency using the concept of regret, linking its ability to explore to the model size and underlying algorithm.

• The paper introduces BanditBench, a comprehensive benchmark for evaluating LLMs on multi-armed and contextual bandit tasks.
• It leverages UCB-inspired algorithm-guided inference and distillation to enhance exploration and decision-making under uncertainty.
• Comprehensive experiments with win-rate metrics and ablation studies show that fine-tuning enables smaller models to outperform larger ones.

EVOLvE: Evaluating and Optimizing LLMs For Exploration

The paper "EVOLvE: Evaluating and Optimizing LLMs For Exploration" provides a comprehensive examination of LLMs and their capabilities in decision-making tasks under uncertainty, particularly within multi-armed and contextual bandit settings. The authors introduce BanditBench, a benchmark suite designed to rigorously evaluate LLMs' performance in such environments. Through this work, a detailed exploration of the potential of LLMs to learn effective exploration strategies is pursued, leveraging both algorithmic guidance and algorithm distillation from classical strategies like Upper Confidence Bound (UCB).

Key Contributions

1. BanditBench: The authors develop BanditBench, a comprehensive benchmark suite that includes both multi-armed bandits (MAB) and contextual bandits (CB) with diverse task configurations. This serves as a structured framework to evaluate the in-context decision-making and exploration capabilities of LLMs.
2. Algorithm-Guided Inference: The paper explores leveraging UCB-type algorithms by providing algorithm-guided support during inference. This involves supplementing LLMs with explicit exploitation and exploration values to enhance decision-making, significantly improving performance in contextual bandits compared to raw history inputs.
3. Algorithm Distillation: A major emphasis is placed on algorithm distillation, where optimal trajectories are generated using UCB. This approach aims to transfer efficient exploration behaviors to LLMs through methods like in-context few-shot demonstration and fine-tuning. Notably, oracle behavior fine-tuning (OFT) enables smaller models to exceed the performance of larger counterparts.
4. Comprehensive Evaluation: The paper methodically evaluates various model sizes and configurations across MAB and CB tasks, employing a win-rate metric for nuanced performance comparison. An extensive ablation paper further addresses factors influencing the efficacy of algorithm distillation, such as task difficulty and representation alignment.

Numerical Results and Analysis

The paper reports significant findings, especially the performance boost from oracle behavior fine-tuning which consistently outperforms both few-shot demonstration and baseline approaches across all model sizes in CB and MAB scenarios. Noteworthy is the observation that Gemini-1.5 Flash models, when fine-tuned, match or surpass the performance of the larger Gemini-1.5 Pro models.

Additionally, the exploration efficiencies of different LLM configurations were analyzed using a parametric regret function. This allows for an interpretative framework to understand the dynamics of exploration behavior, showcasing how fine-tuning models achieve sublinear regret patterns similarly to classical bandit algorithms like UCB.

Implications and Future Directions

The findings presented in the paper underscore the potential of integrating algorithmic strategies within LLMs to enhance their decision-making under uncertainty. This integration harbors practical implications, particularly where LLMs could be employed in dynamic environments requiring adaptable exploration-exploitation strategies, such as recommendation systems and automated decision-making applications.

Looking forward, this research opens avenues for further refining algorithm distillation techniques and exploring the role of LLM architectures in capturing complex decision-making dynamics. By bridging LLMs with classical decision-making algorithms, the work presents a pathway for developing more versatile and intelligent systems capable of operating efficiently across broader scenarios.

In summary, the paper offers valuable insights into the optimization of LLMs for exploration, providing a solid foundation for advancing the robustness and applicability of LLMs in decision-making tasks.