The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Open accessAbstract
This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs).Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood.We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length.We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations.Our findings show a notable degradation in LLMs' reasoning performance at much shorter input lengths than their technical maximum.We show that the degradation trend appears in every version of our dataset, although at different intensities.Additionally, our study reveals that the traditional metric of next word prediction correlates negatively with performance of LLMs' on our reasoning dataset.We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.
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“However, the viability of this future remains largely uncertain given the many engineering hurdles involved and the apparent limitations of long-context LLMs, even within current context lengths [41, 45, 96, 21].”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [41]
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“Longcontext studies have shown that language models struggle to utilize relevant information within lengthy inputs, with performance often degrading well below the nominal context-window limit (Liu et al., 2024; Levy et al., 2024; Fraga, 2024; He et al., 2024; Hsieh et al., 2024; Tian et al., 2025).”
From Weighted Memory Tree: Remembering What Matters for Long-Horizon LLM Agents (2026) · cites this paper as Levy et al., 2024
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“Models can be sensitive to the position of relevant evidence and can exhibit reasoning degradation as input length increases (Liu et al., 2024; Levy et al., 2024).”
From Weighted Memory Tree: Remembering What Matters for Long-Horizon LLM Agents (2026) · cites this paper as Levy et al., 2024
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“Long-context studies show that increasing the available context does not ensure reliable use of all included information.”
From Weighted Memory Tree: Remembering What Matters for Long-Horizon LLM Agents (2026) · cites this paper as Levy et al., 2024
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“Third, it possible that such very long context windows will continue to lead to degraded performance, as has been seen in current models [33].”
From Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated (2025) · cites this paper as [33]
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Research works printed but not linked: 6
- Hong Liu, Sang Michael Xie, Zhiyuan Li, and Tengyu Ma. 2023a. Same pre-training loss, better down- stream: Implicit bias matters for language models. In International Conference on Machine Learning, pages 22188-22214.
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- Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2020. Generation-augmented retrieval for open- domain question answering. In Annual Meeting of the Association for Computational Linguistics.
- Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023. Large language models can be easily distracted by irrelevant context. In Inter- national Conference on Machine Learning, pages 31210-31227.
- PMLR.
- Jason Weston, Antoine Bordes, Sumit Chopra, Alexan- der M Rush, Bart Van Merriënboer, Armand Joulin, and Tomas Mikolov. 2016. Towards ai-complete question answering: A set of prerequisite toy tasks. In 4th International Conference on Learning Repre- sentations, ICLR 2016.
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RULER: What's the Real Context Size of Your Long-Context Language Models?
Hsieh, C.-P., Sun, S., Kriman, S., Acharya, S., Rekesh, D., Jia, F., Zhang, Y., & Ginsburg, B. (2024) · arXiv · Cited by 1,068
Clinical entity augmented retrieval for clinical information extraction
López, I., Swaminathan, A., Vedula, K. S., Narayanan, S., Haredasht, F. N., Ma, S. P., Liang, A. S., Tate, S., Maddali, M. V., Gallo, R. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01377-1 · Cited by 97
Du, X., Zhou, Z., Wang, Y., Chuang, Y.-W., Li, Y., Yang, R. C., Zhang, W., Wang, X., Chen, X., Guan, H., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf233 · Cited by 56
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
Shankar, S., Chambers, T., Shah, T., Parameswaran, A., & Wu, E. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3746405.3746426 · Cited by 147
Accelerating knowledge graph and ontology engineering with large language models
Shimizu, C., & Hitzler, P. (2025) · Journal of Web Semantics · doi:10.1016/j.websem.2025.100862 · Cited by 50
Metadata
- Authors
- Levy, M., Jacoby, A., & Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 15339-15353
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402683949
- Cited by
- 119
- References
- 0
- Citations by year
- 2026: 48 · 2025: 57 · 2024: 14
- FWCI
- 35.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (HORIZON2020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, reasoning performance, question answering, padding, next-word prediction, performance degradation, failure modes
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