Summary of Ada-leval: Evaluating Long-context Llms with Length-adaptable Benchmarks, by Chonghua Wang et al.
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks
by Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin, Kai Chen
First submitted to arxiv on: 9 Apr 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 novel benchmark called Ada-LEval, designed to evaluate the long-context understanding capabilities of large language models (LLMs). The existing benchmarks for evaluating LLMs’ abilities in handling extremely long documents have limitations, including failing to cover ultralong settings. To address this gap, Ada-LEval constructs test sets based on open-source datasets, focusing on question-answering and summarization tasks. The benchmark includes two challenging subsets: TSort and BestAnswer, which enable a more reliable evaluation of LLMs’ long-context capabilities. Furthermore, Ada-LEval allows for the manipulation of text sample lengths up to 128k tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a better way to test how well language models can understand very long pieces of writing. Right now, there are some ways to test this, but they don’t cover really long texts. The new benchmark, called Ada-LEval, makes it possible to create text samples up to 128k tokens and tests the models’ ability to understand these longer texts. |
Keywords
» Artificial intelligence » Question answering » Summarization
