MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing LCU benchmarks for large language models often result in prohibitively high evaluation costs . existing benchmarks exhibit significant redundancy, which means inefficiency in evaluation . |
| Approach: | They propose a data compression method tailored for long-text data with sparse information characteristics. |
| Outcome: | The proposed method reduces evaluation costs to 4.5% of the long-text benchmark LongBench . the proposed method is based on a long-term LCU benchmark with sparse information characteristics . |
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| Challenge: | Existing long context models suffer from performance decline when the input text exceeds their length limit. |
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| Challenge: | Existing multilingual long-context benchmarks are myopic and inherently limited, as successful recall alone does not indicate a model’s capacity to reason over extended contexts. |
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