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CompAct: Compressing Retrieved Documents Actively for Question Answering

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arxiv 2407.09014 v3 pith:SNI2FTOG submitted 2024-07-12 cs.CL

Chanwoong Yoon , Taewhoo Lee , Hyeon Hwang , Minbyul Jeong , Jaewoo Kang This is my paper · ORCID
classification cs.CL
keywords compactinformationcompressiondocumentsextensivelanguagemodelsachieving
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Retrieval-augmented generation supports language models to strengthen their factual groundings by providing external contexts. However, language models often face challenges when given extensive information, diminishing their effectiveness in solving questions. Context compression tackles this issue by filtering out irrelevant information, but current methods still struggle in realistic scenarios where crucial information cannot be captured with a single-step approach. To overcome this limitation, we introduce CompAct, a novel framework that employs an active strategy to condense extensive documents without losing key information. Our experiments demonstrate that CompAct brings significant improvements in both performance and compression rate on multi-hop question-answering benchmarks. CompAct flexibly operates as a cost-efficient plug-in module with various off-the-shelf retrievers or readers, achieving exceptionally high compression rates (47x).

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents

    cs.LG 2026-07 conditional novelty 6.0 of 10

    KV-cache eviction, prompt compression, recurrent state bounding, and agent memory consolidation are unified as one rate-distortion problem with a shared lower bound, shared failure mode, and transferable mechanisms.

  2. Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

    cs.LG 2026-02 unverdicted novelty 6.0 of 10

    Extra-CoT trains a semantic compressor on math CoT data, applies mixed-ratio SFT, and uses CHRPO reinforcement learning to achieve over 73% token reduction on MATH-500 with 0.6% accuracy gain on Qwen3-1.7B.

  3. MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

    cs.AI 2025-10 conditional novelty 6.0 of 10

    MHA-RAG encodes retrieved exemplars into order-invariant soft prompts via multi-head attention, claiming ~20-point effective-accuracy gains over RAG at ~10x lower inference FLOPs.

  4. Provence: efficient and robust context pruning for retrieval-augmented generation

    cs.CL 2025-01 conditional novelty 6.0 of 10

    Provence prunes and reranks retrieved contexts in one pass, compressing 50-80% of the context while keeping question-answering accuracy close to the full-context baseline.

  5. ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

    cs.AI 2026-07 unverdicted novelty 5.0 of 10

    ContextSniper reduces token use by 38.9-51.5% in repository-level program repair agents on SWE-bench Lite with 2 percentage point drops in resolution rate.

  6. Boosting Long-Context Management via Query-Guided Activation Refilling

    cs.CL 2024-12 conditional novelty 4.0 of 10

    ACRE uses a bi-layer KV cache with query-guided refilling to answer long-context questions beyond a model's native window, reporting gains over RAG and compression baselines.

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