aiqbench analysis
This research exposes a critical floating-point precision failure in ALiBi positional encodings, where linear bias scaling causes attention heads to become effectively blind during specific tasks like token retrieval. The study demonstrates that while this issue does not significantly impact standard decoder benchmarks, it can severely degrade performance on specialized "needle-in-a-haystack" scenarios and offers four concrete training-time mitigation strategies.
When Attention Goes Blind: Numerical Failure in ALiBi Positional Encodings
arXiv:2608.03994v1 Announce Type: new Abstract: We identify a previously overlooked failure mode of ALiBi positional encoding: its linear bias scaling underflows floating-point precision, which zeroes out a large fraction of attention weights and renders the affected attention heads partially blind. We analyze this failure mode, characterize its impact, and examine four mitigation strategies. We further demonstrate its occurrence in state-of-the-art pretrained models based on ALiBi. Comprehensive pretraining experiments with 148M-parameter decoder models help us to disentangle its effects from out-of-context degradation. We find that ALiBi's failure mode can substantially impair token retrieval while having only a minor effect on standard decoder benchmarks. We propose four training-time mitigation strategies and evaluate them individually and in combinations, finding that log-scaled distances yield the most consistent improvements in passkey retrieval. Despite this problem, default ALiBi slopes remain a surprisingly strong baseline, particularly for needle-in-a-haystack retrieval. Based on these findings we provide concrete recommendations on how to train models with ALiBi.
Abstract:We identify a previously overlooked failure mode of ALiBi positional encoding: its linear bias scaling underflows floating-point precision, which zeroes out a large fraction of attention weights and renders the affected attention heads partially blind. We analyze this failure mode, characterize its impact, and examine four mitigation strategies. We further demonstrate its occurrence in state-of-the-art pretrained models based on ALiBi. Comprehensive pretraining experiments with 148M-parameter decoder models help us to disentangle its effects from out-of-context degradation. We find that ALiBi's failure mode can substantially impair token retrieval while having only a minor effect on standard decoder benchmarks. We propose four training-time mitigation strategies and evaluate them individually and in combinations, finding that log-scaled distances yield the most consistent improvements in passkey retrieval. Despite this problem, default ALiBi slopes remain a surprisingly strong baseline, particularly for needle-in-a-haystack retrieval. Based on these findings we provide concrete recommendations on how to train models with ALiBi.
Computation and Language (cs.CL)
arXiv:2608.03994 [cs.CL]
(or arXiv:2608.03994v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.03994 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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