3 papers
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…
physics.soc-ph2026
Generalization to Political Beliefs from Fine-Tuning on Sports Team Preferences
Owen Terry
Fine-tuned LLMs often exhibit unexpected behavior as a result of generalizing beyond the data they're shown. We present results in which an LLM fine-tuned to prefer either coastal…
cs.CL2025
Neologism Learning as a Parameter-Efficient Alternative to Fine-Tuning for Model Steering
Sungjoon Park, Varun Ramamurthi, Owen Terry
In language modeling, neologisms are new tokens trained to represent a concept not already included in a given model's vocabulary. Neologisms can be used to encourage specific beha…