Efficiënte Monsteruitlijning voor Taalmodel met Beperkte Gegevens

Sample-Efficient Alignment for LLMs

November 3, 2024
Auteurs: Zichen Liu, Changyu Chen, Chao Du, Wee Sun Lee, Min Lin
cs.AI

Samenvatting

We bestuderen methoden voor het efficiënt afstemmen van grote taalmodellen (LLM's) op menselijke voorkeuren gegeven een beperkt online budget voor feedback. We formuleren eerst het probleem van het afstemmen van LLM's binnen het kader van contextuele duellerende bandieten. Deze formulering, waarin recente paradigma's zoals online RLHF en online DPO zijn opgenomen, streeft inherent naar algoritmes die efficiënt omgaan met voorbeelden en online actieve verkenning integreren. Door inzichten uit de bandietentheorie te benutten, introduceren we een verenigd algoritme gebaseerd op Thompson-sampling en benadrukken we de toepassingen ervan in twee verschillende scenario's voor het afstemmen van LLM's. De praktische agent die dit algoritme efficiënt implementeert, genaamd SEA (Sample-Efficient Alignment), wordt empirisch gevalideerd via uitgebreide experimenten over drie modelgroottes (1B, 2.8B, 6.9B) en drie algoritmes voor voorkeursleren (DPO, IPO, SLiC). De resultaten tonen aan dat SEA zeer efficiënte afstemming met de voorkeuren van de orakel bereikt, waarbij het recente methoden voor actieve verkenning van LLM's overtreft. Daarnaast stellen we de implementatie van SEA beschikbaar samen met een efficiënte codebase die is ontworpen voor online afstemming van LLM's, met als doel toekomstig onderzoek op dit gebied te versnellen.
English
We study methods for efficiently aligning large language models (LLMs) with human preferences given budgeted online feedback. We first formulate the LLM alignment problem in the frame of contextual dueling bandits. This formulation, subsuming recent paradigms such as online RLHF and online DPO, inherently quests for sample-efficient algorithms that incorporate online active exploration. Leveraging insights from bandit theory, we introduce a unified algorithm based on Thompson sampling and highlight its applications in two distinct LLM alignment scenarios. The practical agent that efficiently implements this algorithm, named SEA (Sample-Efficient Alignment), is empirically validated through extensive experiments across three model scales (1B, 2.8B, 6.9B) and three preference learning algorithms (DPO, IPO, SLiC). The results demonstrate that SEA achieves highly sample-efficient alignment with oracle's preferences, outperforming recent active exploration methods for LLMs. Additionally, we release the implementation of SEA together with an efficient codebase designed for online alignment of LLMs, aiming to accelerate future research in this field.

Summary

AI-Generated Summary

Paper Overview

This paper focuses on efficiently aligning large language models (LLMs) with human preferences within a limited online feedback budget. It introduces the SEA algorithm, based on Thompson sampling, for LLM alignment, outperforming recent active exploration techniques. The study addresses two LLM alignment scenarios: user feedback-based and crowdsourcing-based, with a detailed comparison of prior alignment approaches.

Core Contribution

The key innovation lies in the introduction of the SEA algorithm for sample-efficient LLM alignment, demonstrating superior efficiency in aligning with oracle preferences and surpassing active exploration techniques for LLMs.

Research Context

The research positions itself within the realm of optimizing agent behavior with Exploration and Exploitation (E&E) or Best Arm Identification (BAI) objectives, modeling human preferences using the Bradley-Terry model, and exploring the balance between exploration and exploitation in LLM alignment.

Keywords

Large Language Models (LLMs), Thompson Sampling, Active Exploration, Bradley-Terry Model, Online Learning, Sample Efficiency, Epistemic Reward Models

Background

The research addresses the challenge of aligning LLMs with human preferences efficiently. It identifies gaps in existing literature related to sample-efficient online LLM alignment, technical challenges in online exploration, and evaluates prior approaches to LLM alignment.

Research Gap

Existing literature lacks efficient methods for online LLM alignment with limited feedback budgets, necessitating the development of novel algorithms like SEA.

Technical Challenges

Technical obstacles include the complexity of online exploration in LLMs, the need for balancing exploration and exploitation, and addressing sample inefficiencies in prior alignment methods.

Prior Approaches

Critical analysis of existing solutions reveals the limitations in sample efficiency and the need for more effective alignment techniques for LLMs.

Methodology

The paper's methodology involves formulating the LLM alignment problem within the contextual dueling bandits framework, implementing the SEA algorithm with Thompson sampling, and incorporating techniques like epistemic reward models and policy-guided searches for efficient alignment.

Theoretical Foundation

The study is grounded in the contextual dueling bandits framework, leveraging Thompson sampling for LLM alignment and exploring active exploration strategies for response selection.

Technical Architecture

The technical design includes the implementation of SEA with epistemic reward models, policy-guided searches, and mixed preference learning to enhance sample efficiency in online LLM alignment.

Implementation Details

Specific algorithms like Thompson sampling, Dyna architecture, and Epistemic Reward Models are employed to facilitate online exploration in LLMs and improve alignment efficiency.

Innovation Points

The innovation lies in combining active exploration and policy-guided searches in the SEA algorithm, leading to superior sample efficiency and alignment performance in LLMs.

Experimental Validation

The experimental validation involves configuring SEA for various model sizes and preference learning algorithms, demonstrating its effectiveness through empirical results, comparative analyses, and performance evaluations.

Setup

Exact configurations, parameters, and datasets used in the experiments are detailed, showcasing the effectiveness of SEA in achieving higher win rates and improved sample efficiency across different model sizes.

Metrics

Evaluation criteria focus on sample efficiency, win rates, and effectiveness in online LLM alignment compared to baseline methods, highlighting the advantages of the SEA algorithm.

Results

Quantitative and qualitative findings from experiments show that SEA outperforms baseline methods, achieving strong empirical results in LLM alignment with active exploration techniques.

Comparative Analysis

Detailed comparisons with baseline methods and ablation analyses confirm the superior performance of SEA in combining active exploration and policy-guided searches for efficient online LLM alignment.

Impact and Implications

The study's impact and implications revolve around the key findings, limitations, future research directions, and practical significance of the proposed SEA algorithm for online LLM alignment.

Key Findings

The research highlights the effectiveness of SEA in achieving sample-efficient online LLM alignment, inspiring future research in this domain with its open-source codebase and innovative algorithm.

Limitations

An honest assessment of the study's limitations is provided, paving the way for further improvements in sample efficiency and alignment techniques for LLMs.

Future Directions

Concrete research opportunities are identified, emphasizing the need for advancements in online LLM alignment methods and the exploration of new strategies for efficient alignment with human preferences.

Practical Significance

The practical applications of the SEA algorithm in real-world scenarios are discussed, showcasing its potential to enhance online LLM alignment and improve user experience through efficient alignment strategies.

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