The learned input-output behavior of GLU-based neurons in large language models (LLMs) is analyzed and it is found that weakening neurons display surprising behavior: even though there are few, they activate often and have a large influence on model behavior.
Sebastian Gerstner, Hilal AlQuabeh, Kentaro Inui et al.· 0 citations
This work presents a unified evaluation of representative CLC-enhancement methods for question answering, spanning inference-time interventions and post-training approaches across three model families and three closed-form benchmarks, highlighting the need to evaluate CLC enhancement for both cross-domain robustness an...
Jirui Qi, Ming-Yang Wang, Hinrich Schütze et al.· 0 citations
This study highlights the potential of NLP tools to streamline the semiautomatic annotation process, reducing the reliance on extensive linguistic expertise and manual effort, and paving the way for broader applications in digital humanities research.
Ercong Nie, Siyao Peng, Helmut Schmid et al.· Digital Scholarship in the H...· 0 citations
A target-centric survey of QAT is provided, aimed at clarifying both its theoretical foundations and its evolving implementation landscape and synthesizing cross-target differences in error characteristics, numerical formats, and strategy transferability.
Jiashun Song, Mengjie Zhao, Zi-Jing Wang et al.· 0 citations
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