Papers with Chain of Reasoning
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
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |