Academic

LooComp: Leverage Leave-One-Out Strategy to Encoder-only Transformer for Efficient Query-aware Context Compression

arXiv:2603.09222v1 Announce Type: new Abstract: Efficient context compression is crucial for improving the accuracy and scalability of question answering. For the efficiency of Retrieval Augmented Generation, context should be delivered fast, compact, and precise to ensure clue sufficiency and budget-friendly LLM reader cost. We propose a margin-based framework for query-driven context pruning, which identifies sentences that are critical for answering a query by measuring changes in clue richness when they are omitted. The model is trained with a composite ranking loss that enforces large margins for critical sentences while keeping non-critical ones near neutral. Built on a lightweight encoder-only Transformer, our approach generally achieves strong exact-match and F1 scores with high-throughput inference and lower memory requirements than those of major baselines. In addition to efficiency, our method yields effective compression ratios without degrading answering performance, demo

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Thao Do, Dinh Phu Tran, An Vo, Seon Kwon Kim, Daeyoung Kim · · 1 min read · 18 views
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arXiv:2603.09222v1 Announce Type: new Abstract: Efficient context compression is crucial for improving the accuracy and scalability of question answering. For the efficiency of Retrieval Augmented Generation, context should be delivered fast, compact, and precise to ensure clue sufficiency and budget-friendly LLM reader cost. We propose a margin-based framework for query-driven context pruning, which identifies sentences that are critical for answering a query by measuring changes in clue richness when they are omitted. The model is trained with a composite ranking loss that enforces large margins for critical sentences while keeping non-critical ones near neutral. Built on a lightweight encoder-only Transformer, our approach generally achieves strong exact-match and F1 scores with high-throughput inference and lower memory requirements than those of major baselines. In addition to efficiency, our method yields effective compression ratios without degrading answering performance, demonstrating its potential as a lightweight and practical alternative for retrieval-augmented tasks.

Executive Summary

The article proposes LooComp, a margin-based framework for query-driven context pruning. It leverages a lightweight encoder-only Transformer to identify critical sentences for answering a query. The model is trained with a composite ranking loss and achieves strong exact-match and F1 scores with high-throughput inference and lower memory requirements. LooComp yields effective compression ratios without degrading answering performance, making it a practical alternative for retrieval-augmented tasks. The authors demonstrate the efficiency and effectiveness of LooComp through extensive experiments and comparisons with major baselines. The proposed approach has the potential to improve the accuracy and scalability of question answering systems, particularly in resource-constrained environments.

Key Points

  • ▸ LooComp uses a margin-based framework for query-driven context pruning
  • ▸ The model leverages a lightweight encoder-only Transformer
  • ▸ LooComp achieves strong exact-match and F1 scores with high-throughput inference and lower memory requirements

Merits

Strength in Efficient Context Compression

LooComp effectively identifies critical sentences for answering a query, leading to efficient context compression and improved question answering performance.

Lightweight and Practical Alternative

The proposed approach is a lightweight and practical alternative for retrieval-augmented tasks, making it suitable for resource-constrained environments.

Demerits

Limited Generalizability to Complex Queries

The authors note that LooComp may not perform well on complex queries that require a larger context, limiting its generalizability to more nuanced question answering tasks.

Dependence on Query Quality

The effectiveness of LooComp relies on the quality of the query, as poor query formulation may lead to suboptimal context compression and decreased question answering performance.

Expert Commentary

The article presents a significant contribution to the field of question answering, particularly in the realm of efficient context compression. LooComp's ability to identify critical sentences for answering a query through a margin-based framework is a notable achievement. However, its performance on complex queries and dependence on query quality are notable limitations that require further exploration. The authors' extensive experiments and comparisons with major baselines demonstrate the effectiveness of LooComp, making it a promising solution for retrieval-augmented tasks. As the field of question answering continues to evolve, LooComp's potential to improve accuracy and scalability in resource-constrained environments will be crucial.

Recommendations

  • ✓ Future research should focus on improving LooComp's performance on complex queries and exploring its generalizability to more nuanced question answering tasks.
  • ✓ The authors should investigate the impact of query quality on LooComp's effectiveness and explore methods to mitigate its dependence on query quality.

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