A training-free compression pipeline that updates calibration activations layer by layer and re-allocates ranks iteratively improves compressed LLM accuracy by up to a few points on five zero-shot tasks.
DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities
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abstract
The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework however does not provide a means of analyzing what factors, beyond context length, contribute to LMs' abilities or inabilities to separate and recall needles from their haystacks. To provide a systematic means of assessing what features contribute to LMs' NIAH capabilities, we developed a synthetic benchmark called DENIAHL (Data-oriented Evaluation of NIAH for LLM's). Our work expands on previous NIAH studies by ablating NIAH features beyond typical context length including data type, size, and patterns. We find stark differences between GPT-3.5 and LLaMA 2-7B's performance on DENIAHL, and drops in recall performance when features like item size are increased, and to some degree when data type is changed from numbers to letters. This has implications for increasingly large context models, demonstrating factors beyond item-number impact NIAH capabilities.
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cs.AI 1years
2026 1verdicts
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Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs
A training-free compression pipeline that updates calibration activations layer by layer and re-allocates ranks iteratively improves compressed LLM accuracy by up to a few points on five zero-shot tasks.