LongHarness Bench: Stress-Testing Language Model Harnesses for Long-Context Reasoning
Language-model (LM) harnesses enable LMs to operate effectively over long contexts using additional compute. However, existing long-context evaluations are insufficient for distinguishing modern harnesses, reflected by saturated accuracy across harnesses and largely similar evaluation costs. In this paper, we introduce a benchmark for evaluating both the effectiveness and effic
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Language-model (LM) harnesses enable LMs to operate effectively over long contexts using additional compute. However, existing long-context evaluations are insufficient for distinguishing modern harnesses, reflected by saturated accuracy across harnesses and largely similar evaluation costs. In this paper, we introduce a benchmark for evaluating both the effectiveness and effic
This paper introduces a crucial benchmark for evaluating long-context language models, directly addressing the limitations of existing evaluations. For ML engineers, understanding these benchmarks is vital for selecting and optimizing models for real-world applications requiring extensive context processing, impacting both performance and cost efficiency.