One Thousand and One Pairs: A “novel” challenge for long-context language models (2024.emnlp-main)
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| Challenge: | Existing long-context evaluation methods measure surface-level retrieval capabilities, but do not assess performance on the more challenging task of synthesizing distant and underlying information. |
| Approach: | They propose a dataset of 1,001 minimally different pairs of true and false claims about 67 recently-published English fictional books. |
| Outcome: | The proposed model performs better on pairs that require only sentence-level retrieval vs. global reasoning . the proposed model also performs worse on speculative fiction books with extensive world-building . |
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| Challenge: | Existing multilingual long-context benchmarks are myopic and inherently limited, as successful recall alone does not indicate a model’s capacity to reason over extended contexts. |
| Approach: | They propose a new synthetic benchmark for multilingual long-context reasoning that includes bAbI-style tasks that test multi-hop inference, aggregation, and epistemic reasoning. |
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| Challenge: | Recent large language models claim long context windows, but evaluations often involve simple retrieval tasks or synthetic tasks padded with irrelevant text. |
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Literary Evidence Retrieval via Long-Context Language Models (2025.acl-short)
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| Challenge: | a recent study shows that long-context language models can exceed human expert performance in literary analysis . despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration. |
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| Challenge: | Recent large language models demonstrate remarkable abilities in responding to queries in diverse languages, but their ability to handle long multilingual contexts is unexplored. |
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LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)
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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
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Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) context windows have enabled them to process inputs over 100K tokens and generate outputs of up to 10K token. |
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Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
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LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)
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| Challenge: | Long-context processing ability has emerged as a significant challenge for large language models. |
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Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters (2022.findings-emnlp)
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| Challenge: | Recent advances in modeling and datasets demonstrate promising performance for NLI. |
| Approach: | They explore the direct zero-shot applicability of NLI models to real applications . they analyze the robustness of models to longer and out-of-domain inputs . |
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Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context language models have lagged behind . however, there is still room for improvement as the context window and complexity of the tasks increase. |
| Approach: | They propose a long-context benchmark to evaluate the performance of long-text language models. |
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