Beyond Numeric Awards: In-Context Dueling Bandits with LLM Agents

• URL: http://arxiv.org/abs/2407.01887v2
• Date: Tue, 15 Oct 2024 03:46:28 GMT
• Title: Beyond Numeric Awards: In-Context Dueling Bandits with LLM Agents
• Authors: Fanzeng Xia, Hao Liu, Yisong Yue, Tongxin Li,
• Abstract summary: This paper is the first to investigate the performance of Large Language Models (LLMs) as decision-makers in the context of Dueling Bandits (DB) Our results reveal that LLMs, particularly GPT-4 Turbo, quickly identify the Condorcet winner, thus outperforming existing state-of-the-art algorithms in terms of weak regret. To overcome these issues, we introduce a hybrid algorithm: LLM-Enhanced Adaptive Dueling (LEAD), which takes advantage of both in-context decision-making capabilities of LLMs and theoretical guarantees inherited from classic DB algorithms.
• Score: 25.825941077332182
• License:
• Abstract: In-context decision-making is an important capability of artificial general intelligence, which Large Language Models (LLMs) have effectively demonstrated in various scenarios. However, LLMs often face challenges when dealing with numerical contexts, and limited attention has been paid to evaluating their performance through preference feedback generated by the environment. This paper is the first to investigate the performance of LLMs as decision-makers in the context of Dueling Bandits (DB). We compare GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, Llama 3.1, and o1-preview against eight well-established DB algorithms. Our results reveal that LLMs, particularly GPT-4 Turbo, quickly identify the Condorcet winner, thus outperforming existing state-of-the-art algorithms in terms of weak regret. Nevertheless, LLMs struggle to converge even when explicitly prompted to do so and are sensitive to prompt variations. To overcome these issues, we introduce a hybrid algorithm: LLM-Enhanced Adaptive Dueling (LEAD), which takes advantage of both in-context decision-making capabilities of LLMs and theoretical guarantees inherited from classic DB algorithms. We show that LEAD has theoretical guarantees on both weak and strong regret and validate its robustness even with noisy and adversarial prompts. The design of such an algorithm sheds light on how to enhance trustworthiness for LLMs used in decision-making tasks where performance robustness matters.

Related papers

• LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints [86.59857711385833]
  We introduce RealInstruct, the first benchmark designed to evaluate LLMs' ability to follow real-world multi-constrained instructions. To address the performance gap between open-source and proprietary models, we propose the Decompose, Critique and Refine (DeCRIM) self-correction pipeline. Our results show that DeCRIM improves Mistral's performance by 7.3% on RealInstruct and 8.0% on IFEval even with weak feedback.
  arXiv  Detail & Related papers  (2024-10-09T01:25:10Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Control Large Language Models via Divide and Conquer [94.48784966256463]
  This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG) We evaluate the performance of LLMs on satisfying lexical constraints with prompt-based control, as well as their efficacy in downstream applications.
  arXiv  Detail & Related papers  (2024-10-06T21:20:06Z)
• UBENCH: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions [10.28688988951815]
  UBENCH is a benchmark for evaluating large language models. It includes 3,978 multiple-choice questions covering knowledge, language, understanding, and reasoning abilities. We also evaluate the reliability of 15 popular LLMs, finding GLM4 to be the most outstanding.
  arXiv  Detail & Related papers  (2024-06-18T16:50:38Z)
• Enabling Weak LLMs to Judge Response Reliability via Meta Ranking [38.63721941742435]
  We propose a novel cross-query-comparison-based method called $textitMeta Ranking$ (MR) MR assesses reliability by pairwisely ranking the target query-response pair with multiple reference query-response pairs. We show that MR can enhance strong LLMs' performance in two practical applications: model cascading and instruction tuning.
  arXiv  Detail & Related papers  (2024-02-19T13:57:55Z)
• NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes [32.154637177467684]
  NPHardEval is designed to evaluate the reasoning abilities of Large Language Models (LLMs) across a broad spectrum of 900 questions. It is meticulously chosen to represent a wide range of complexity class below the NP-hard complexity class. It is designed with a dynamic update mechanism, where the datapoints are refreshed on a monthly basis.
  arXiv  Detail & Related papers  (2023-12-22T18:07:44Z)
• Sentiment Analysis through LLM Negotiations [58.67939611291001]
  A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round. This paper introduces a multi-LLM negotiation framework for sentiment analysis.
  arXiv  Detail & Related papers  (2023-11-03T12:35:29Z)
• Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE [62.13435256279566]
  Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, their large size makes their inference slow and computationally expensive. We show that it enables these layers to acquire 'good' generation ability without affecting the generation ability of the final layer.
  arXiv  Detail & Related papers  (2023-10-28T04:07:58Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.