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cs.CL2026
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2
Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation probabilities are often poorly calibrated t…
cs.CL2026
LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents
Davide Baldelli, Ali Parviz, Amal Zouaq +1
As LLMs move from text completion toward autonomous agents, they remain constrained by the standard chat interface, which lacks private working memory. This raises a fundamental qu…
cs.CL2024
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar, Quentin Fournier, Matthew D Riemer +4
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-training remains out of reach for most r…