Summary of Longgenbench: Long-context Generation Benchmark, by Xiang Liu et al.
LongGenBench: Long-context Generation Benchmark
by Xiang Liu, Peijie Dong, Xuming Hu, Xiaowen Chu
First submitted to arxiv on: 5 Oct 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 This paper introduces a new benchmark for evaluating long-context generation capabilities of Large Language Models (LLMs). Current benchmarks primarily focus on retrieval-based tests, such as the needle-in-a-haystack (NIAH) benchmark. In contrast, LongGenBench allows for flexible configurations of customized generation context lengths and requires LLMs to respond with a single, cohesive long-context answer. The authors observe that both API accessed and open source models exhibit performance degradation in long-context generation scenarios, ranging from 1.2% to 47.1%. The findings highlight the challenges faced by LLMs when generating coherent and contextually accurate text that spans across lengthy passages or documents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LongGenBench is a new benchmark for evaluating the ability of language models to generate long-context text. This means that instead of just finding specific information within a passage, the model has to create its own coherent text that makes sense over many paragraphs or even entire documents. The authors found that most language models do worse when generating long-text answers compared to shorter ones. This is important because it shows how challenging it can be for these AI systems to understand and generate text that is relevant and makes sense in a longer context. |
