Validating Repair Claims after Evidence Restoration in Compressed Prompts
DOI:
https://doi.org/10.54097/qpnkcr67Keywords:
Prompt Compression; Evidence Restoration; Repair Claim Validation; Matched Shams; Verification CostAbstract
An answer that recovers after deleted evidence is restored does not, by itself, establish a reliable repair. The recovery may depend on sampling variation or on other changes to the prompt. We evaluate repair claims by fixing the restoration candidate and testing it against three distinct length-matched sham texts in fresh paired calls. An entry screen restricts search to questions that pass with the full context and fail after compression. With controlled deletion at a retention ratio of 0.5, protocol pass rates rose from 20.0% to 45.0% on 60 development items. On 100 held-out test items, they rose from 17.0% to 36.0%. Of 36 search candidates, 34 passed confirmation; one failed and one lacked enough matched texts to complete testing. All confirmed repairs were on GSM8K, with none on HotpotQA. During confirmation, target evidence was prepended and shams were appended, limiting the inference to a contrast involving both content and position. Search and confirmation together cost an estimated US$3.927; confirmation contributed 37.2%. The resulting record distinguishes candidates found during search from repairs supported by fresh control comparisons and makes the cost of that support explicit.
References
[1] Jiang, H., Wu, Q., Lin, C. Y., et al. (2023). LLMLingua: Compressing prompts for accelerated inference of large language models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 13358–13376. https://doi.org/10.18653/v1/2023.emnlp-main.825
[2] Jiang, H., Wu, Q., Luo, X., et al. (2024). LongLLMLingua: Accelerating and enhancing LLMs in long context scenarios via prompt compression. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1658–1677. https://doi.org/10.18653/v1/2024.acl-long.91
[3] Pan, Z., Wu, Q., Jiang, H., et al. (2024). LLMLingua-2: Data distillation for efficient and faithful task-agnostic prompt compression. Findings of the Association for Computational Linguistics: ACL 2024, 963–981. https://doi.org/10.18653/v1/2024.findings-acl.57
[4] Hu, Z., Li, K., Fu, D., et al. (2026). Relevant but incomplete: Referential dangling as a paradigm-level failure mode in hard prompt compression. arXiv preprint arXiv:2608.04569. https://doi.org/10.48550/arXiv.2608.04569
[5] Du, W. (2026). What would fix this RAG failure? Auditing counterfactual response with paired evidence interventions. arXiv preprint arXiv:2608.08944. https://doi.org/10.48550/arXiv.2608.08944
[6] Min, G., Wu, L., Shi, M., et al. (2026). Adapting context compression for long-horizon agents with counterfactual continuations. arXiv preprint arXiv:2609.36526. https://doi.org/10.48550/arXiv.2609.36526
[7] Chang, Y., Cao, B., Wang, Y., et al. (2025). JoPA: Explaining large language model's generation via joint prompt attribution. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 22106–22122. https://doi.org/10.18653/v1/2025.acl-long.1074
[8] Zeller, A., & Hildebrandt, R. (2002). Simplifying and isolating failure-inducing input. IEEE Transactions on Software Engineering, 28(2), 183–200. https://doi.org/10.1109/32.988498
[9] Tao, Y., & Xue, J. (2025). Accelerating delta debugging through probabilistic monotonicity assessment. Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering, 12–22. https://doi.org/10.1145/3756681.3756940
[10] Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212. https://doi.org/10.1080/01621459.1927.10502953
[11] Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall. https://doi.org/10.1007/978-1-4899-4541-9
[12] McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
[13] Yang, Z., Qi, P., Zhang, S., et al. (2018). HotpotQA: A dataset for diverse, explainable multi-hop question answering. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2369–2380. https://doi.org/10.18653/v1/D18-1259
[14] Cobbe, K., Kosaraju, V., Bavarian, M., et al. (2021). Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168. https://doi.org/10.48550/arXiv.2110.14168
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Copyright (c) 2026 Ziye Yue, Yuejin Zhang
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