EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation (2412.12559v3)

Abstract: We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). Current RAG systems often struggle when retrieval models fail to rank the most relevant documents, leading to the inclusion of more context at the expense of latency and accuracy. While abstractive compression methods can drastically reduce token counts, their token-by-token generation process significantly increases end-to-end latency. Conversely, existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection, failing to fully utilize contextual information. EXIT addresses these limitations by classifying sentences from retrieved documents - while preserving their contextual dependencies - enabling parallelizable, context-aware extraction that adapts to query complexity and retrieval quality. Our evaluations on both single-hop and multi-hop QA tasks show that EXIT consistently surpasses existing compression methods and even uncompressed baselines in QA accuracy, while also delivering substantial reductions in inference time and token count. By improving both effectiveness and efficiency, EXIT provides a promising direction for developing scalable, high-quality QA solutions in RAG pipelines. Our code is available at https://github.com/ThisIsHwang/EXIT

• The paper introduces EXIT, a novel framework that adaptively compresses context to reduce inference latency and boost accuracy in RAG systems.
• It employs a binary classification strategy to select contextually relevant sentences, significantly reducing token counts and computational costs.
• Experimental results demonstrate that EXIT outperforms traditional methods on multiple QA datasets, highlighting its scalable plug-and-play potential for RAG applications.

An In-Depth Analysis of EXIT: Enhancing Retrieval-Augmented Generation with Context-Aware Extractive Compression

The paper introduces EXIT, a novel context compression framework designed to enhance the performance of Retrieval-Augmented Generation (RAG) systems, particularly in the domain of Question Answering (QA). RAG systems integrate LLMs with external information retrieval components, aiming to ground their responses on factual evidence. However, as the paper outlines, a key challenge surfaces when traditional retrieval models fail to prioritize the most relevant documents, leading to inefficiencies and inaccuracies in subsequent answer generation.

Challenges in Current RAG Systems

Current methodologies in RAG face significant bottlenecks, particularly when retrieval models cannot effectively rank relevant documents. The conventional solution is to enlarge the document set retrieved, which, while comprehensive, adversely affects both efficiency and performance due to the increased context size. The latency increase is attributed mainly to the attention mechanism's quadratic complexity during inference in LLM systems. In essence, while an increased context might improve comprehensiveness, it imposes unsustainable computational costs, signaling a dire need for more efficient context processing mechanisms.

The EXIT Framework

EXIT responds to these challenges with a context-aware extractive compression strategy that is distinct in its consideration of the entire document context while deciding which sentences to preserve. This approach contrasts significantly with existing paradigms, which often treat sentence selection as an independent process, devoid of broader context acknowledgment. EXIT capitalizes on adaptive, query-sensitive sentence classification, which not only reduces token count but also drastically cuts down inference latency.

EXIT's design involves a binary classification applied in parallel across sentences of retrieved documents, preserving context dependencies and effectively adapting to the complexity of queries. This results in substantial advances in both speed and accuracy over traditional compression approaches—whether uncompressed baselines or other compression methods—as evidenced by the experimental results.

Experimental Validation and Implications

Robust evaluations across several QA datasets reveal that EXIT consistently surpasses both abstractive and extractive compression baselines and even performs better than the uncompressed state, substantiating its efficacy. Notably, EXIT achieves an exceptional balance between accuracy and processing efficiency, delivering a substantial reduction in token counts and inference time without sacrificing accuracy. It stands out particularly in contexts described as involving "multi-hop" reasoning, demonstrating its capability to manage and compress complex document sets efficiently.

Future Directions and Considerations

EXIT's contributions broadly underscore an emerging area in RAG methodologies: context-aware extractive compression that does not require architectural changes in existing pipelines. The framework's nature as a plug-and-play component suggests wide applicability and scalability across varied RAG tasks.

However, several areas for further exploration remain. The reliance on sentence-level annotations for classifier training could be expanded to automate this component, potentially using methods like signals from advanced LLMs such as GPT-4. Moreover, generalizing EXIT's efficacy to domain-specific applications or multi-step retrieval processes poses another potential path for extending its utility.

In conclusion, the paper presents EXIT as a promising evolution in the design of RAG systems, offering a meaningful direction for enhancing both the effectiveness and efficiency of QA tasks. The framework's integration of context-awareness and adaptability marks a significant advance in meeting the dual challenges of speed and accuracy, which are critical to the future developments of RAG technologies.