3 papers
cs.LG2026
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
Bohan Zhang, Anqi Ni, Yixin Wang +1
Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight…
cs.HC2026
Who Owns the Text? Design Patterns for Preserving Authorship in AI-Assisted Writing
Bohan Zhang, Chengke Bu, Paramveer S. Dhillon
AI writing assistants can reduce effort and improve fluency, but they may also weaken writers' sense of authorship. We study this tension with an ownership-aware co-writing editor…
cs.LG2025
ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models
Yachuan Liu, Xiaochun Wei, Lin Shi +4
Large language models (LLMs) face significant challenges in ex-ante reasoning, where analysis, inference, or predictions must be made without access to information from future even…