Summary of Leave No Document Behind: Benchmarking Long-context Llms with Extended Multi-doc Qa, by Minzheng Wang et al.
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
by Minzheng Wang, Longze Chen, Cheng Fu, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel benchmark, Loong, aims to bridge the gap between existing benchmarks and real-world scenarios of long-context applications by introducing a multi-document question answering task. The benchmark consists of four types of tasks: Spotlight Locating, Comparison, Clustering, and Chain of Reasoning, with varying context lengths. This allows for a more comprehensive evaluation of long-context understanding in Large Language Models (LLMs). Existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, which is unrealistic and does not align with real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find an answer by reading multiple documents that are related to each other. This is what Loong benchmark does! It tests how well a computer program can understand long pieces of text by asking questions based on the content of several connected documents. The goal is to make sure the model can really comprehend and not just rely on individual words or sentences. |
Keywords
» Artificial intelligence » Clustering » Question answering
