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KEEP-Bench: Evaluating Evidence Preservation in Context Compression

Ziqing Man, Di Wu, Jia-Chen Gu, Junyi Zhang, Kai‐Wei Chang, Nanyun Peng · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26827 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

既存の評価はQAメトリックに依存しますが、読者はまだ不忠実な圧縮から正しく答えることができますので、これらのメトリックはそのような損失を過小評価します。このギャップをブリッジするには、情報ボトルネックを通じて忠実性の問題を導入する可能性があります。これらの評価は、不忠実な解像度、明確な資格、および少数派の見通しのための証拠を保持することを必要とする3つの挑戦的な設定で4102ケースで圧縮忠実性を直接評価するコンテキストベンチ、 KEEPベンチの診断基準を導入します。 最も強力なモデルは、このギャップの下で、最も信頼性の高い証拠の調査にもかかわらず信頼性

Machine translation (Meta M2M100); the English original follows.

Abstract

Context compression improves long-context efficiency but may introduce faithfulness issues through an information bottleneck. Existing evaluations rely on QA metrics, but a reader can still answer correctly from an unfaithful compression, so these metrics underestimate such loss. To bridge this gap, we introduce KEEP-Bench, a diagnostic benchmark that directly assesses compression faithfulness with 4,102 cases across three challenging settings that require keeping evidence for plausible reference resolutions, explicit qualifiers, and minority perspectives. Experiments show that even with tailored prompting, the strongest models remain unreliable: despite context windows exceeding 100K tokens and inputs averaging only 3.1K tokens, the best open-weight model (31B) preserves all required evidence in only 66.8% of cases, and the best proprietary model in only 61.6%. Performance also varies sharply across evidence structures, so conclusions from limited structures may be biased. We further introduce a metric, Evidence Preservation Return (EPR), to relate evidence recall to inference FLOPs, and find that it peaks at moderate scales (4B-8B), where recall remains low. Additional output budget mainly benefits already strong models and widens capability gaps, yet the stronger preservation of these models comes at a steep computational cost. These findings highlight the importance for future work to investigate compressors with strong information preservation under a small computation budget, rather than relying on a large output budget or model scaling.

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Topics: Computer science, Context (archaeology), Inference, Benchmark (surveying), Compression (physics), Data compression

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