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

TL;DR

This paper introduces LooComp, a query-aware context compression method using leave-one-out strategy with an encoder-only Transformer, achieving efficient, accurate, and compact context delivery for question answering tasks.

Contribution

LooComp is a novel margin-based framework that effectively prunes context sentences based on their importance for answering queries, improving efficiency without sacrificing accuracy.

Findings

Achieves high exact-match and F1 scores with high throughput.

Reduces memory usage compared to major baselines.

Maintains answer quality with effective compression ratios.

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.