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Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation (2402.11794v1)

Published 19 Feb 2024 in cs.CL and cs.IR

Abstract: Retrieval-augmented generation framework can address the limitations of LLMs by enabling real-time knowledge updates for more accurate answers. An efficient way in the training phase of retrieval-augmented models is attention distillation, which uses attention scores as a supervision signal instead of manually annotated query-document pairs. Despite its growing popularity, the detailed mechanisms behind the success of attention distillation remain unexplored, particularly the specific patterns it leverages to benefit training. In this paper, we address this gap by conducting a comprehensive review of attention distillation workflow and identifying key factors influencing the learning quality of retrieval-augmented LLMs. We further propose indicators for optimizing models' training methods and avoiding ineffective training.

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Authors (3)
  1. Zizhong Li (9 papers)
  2. Haopeng Zhang (32 papers)
  3. Jiawei Zhang (529 papers)
Citations (2)
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Summary

Investigating Attention Distillation in Retrieval-augmented Generation Models

Introduction

Recent advancements in LLMs have significantly propelled the field of NLP. Despite their successes, LLMs are hindered by their inability to update knowledge in real-time and safeguard sensitive training data. Retrieval-augmented LLMs emerge as a promising solution, combining retriever and reader components to dynamically incorporate external knowledge, enhancing model accuracy while potentially reducing training complexities. An intriguing strategy within this domain is attention distillation, a process that distills attention scores from the reader to guide the retriever in identifying key information assets. This paper explores the intricacies of attention distillation, exploring its operational mechanisms and presenting indicators for optimizing training efficiency.

Methodological Overview

The paper adopts the ATLAS architecture, utilizing a decoder-only LLM configuration focusing on QA tasks to scrutinize the attention score distillation mechanisms. Attention scores are employed as indicators of document relevance, steering away from the more traditional cross-attention scores. The uniqueness of this paper lies in its evaluation of both the self-attention scores and the value vector norms to comprehensively understand their combined effect on distilling knowledge into the retriever component.

Experimental Insights

The experimental setup spans Falcon-1b as the reader and Contriver as the retriever, examining performance across varied training configurations using the NaturalQuestions and TriviaQA benchmarks. A pivotal finding is the dichotomy in performance between off-the-shelf and fine-tuned distillation training, with the latter significantly outperforming the former. This discrepancy alludes to the critical role of reader model quality in attention distillation efficacy. Furthermore, the research navigates through token-level quantitative analyses, uncovering notable patterns in attention distribution that align with optimal supervisory signals from high-quality readers. Such insights pave the way for introducing two key indicators of attention distillation quality, hinged on the attention allocation to answer-related and question-related tokens.

Implications and Future Directions

This paper elucidates the complex dynamics of attention distillation within retrieval-augmented LLMs, providing a nuanced understanding that can refine the retriever-reader interplay. Practically, the indicators proposed offer a methodical approach to enhancing training methodologies, potentially steering future research toward more nuanced, attention-based knowledge distillation techniques. Theoretically, these findings contribute significantly to the broader discourse on improving the efficiency and effectiveness of retrieval-augmented models. However, recognising the limitation of focusing on decoder-only models, the paper underscores the necessity of extending this exploration to encompass larger-scale models, aiming to affirm the generalizability of its findings across varying model architectures.

Conclusion

Through a detailed examination of attention distillation in retrieval-augmented generation, this paper highlights the paramount influence of attention scores derived from high-quality reader models in enhancing retriever performance. By proposing novel metrics for evaluating the attention distillation quality, the research not only addresses the imperative need for efficient training strategies but also lays the groundwork for future investigations aimed at refining the symbiosis between retrieval and generation components in LLMs.

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