Longgenbench: Benchmarking Long-form Generation In Long Context Llms
Wu Yuhao, Hee Ming Shan, Hu Zhiqing, Lee Roy Ka-wei. Arxiv 2024
[Paper]
The abilities of long-context language models (LMs) are often evaluated using the “Needle-in-a-Haystack” (NIAH) test, which comprises tasks designed to assess a model’s ability to identify specific information (“needle”) within large text sequences (“haystack”). While these benchmarks measure how well models understand long-context input sequences, they do not effectively gauge the quality of long-form text generation–a critical aspect for applications such as design proposals and creative writing. To address this gap, we have introduced a new long-form text evaluation benchmark, LongGenbench, which tests models’ ability to identify specific events within generated long text sequences. In this benchmark, we prompt long-context LMs to create long-form text that must include particular events or constraints and evaluate their ability to incorporate these elements. We evaluated ten long-context LMs across four distinct scenarios, three types of prompt instructions, and two different generation-length settings (16K and 32K). Although these models perform well on NIAH benchmarks, none demonstrated satisfactory performance on the LongGenbench, raising concerns about their ability to generate coherent long-form text that follows instructions. Additionally, as the length of the generated text increases, all models exhibit a significant drop in performance.
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