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Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps
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Long-context language models (LCLMs), characterized by their extensive context window, are becoming popular. However, despite the fact that they are nearly perfect at standard long-context retrieval tasks, our evaluations demonstrate they fail in some basic cases. Later, we find they can be well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. This result emphasizes the potential necessity of solving specific long-context tasks using long-CoT methods, while previous long-context benchmarks always ignore the necessity of long reasoning for long-context tasks and treat them as direct QA tasks.
Forward citations
Cited by 2 Pith papers
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LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework
LOOM-Scope is a framework that standardizes long-context LLM evaluation across 22 benchmarks and integrates a lightweight 12-benchmark suite, LOOMBench, for fast comprehensive assessment.
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Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
On a new extended needle-in-a-haystack benchmark, explicit anti-hallucination prompts and dispersed fact placement cause some long-context LLMs to over-refuse or collapse in accuracy, while others remain robust.
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