25 citations · 25 across the 5 of their papers we have counts for
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cs.CL2026
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization
Yujie Chen, Weiyan Zheng, Yiwu Yao +7
Ultra-low-bit scalar quantization offers a regular arithmetic structure for efficient deployment of large language models, but preserving predictive behavior at extreme precision r…
cs.CL2025
Inoculation Prompting: Eliciting traits from LLMs during training can suppress them at test-time
Daniel Tan, Anders Woodruff, Niels Warncke +4
Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning dat…
cs.CL2025★ 25 cited
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
Jan Betley, Daniel Tan, Niels Warncke +5
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting mode…