565 citations · 1k across the 23 of their papers we have counts for
26 papers
Equilibrium in a Reaction Network of Assemblies
Giampaolo Folena, Germán Kruszewski
We study a mean-field reaction network whose species are assemblies built from identical atoms by reversible coagulation and fragmentation. Each assembly is an ordered binary tree,…
Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity
Germán Kruszewski, Pierre Erbacher, Jos Rozen +1
Reinforcement Learning (RL) has become the de facto standard for tuning LLMs to solve tasks involving reasoning. However, growing evidence shows that models trained in such way oft…
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Thibaut Thonet, Germán Kruszewski, Jos Rozen +2
LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization -- tai…
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM
Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2
Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various L…
Compositional preference models for aligning LMs
Dongyoung Go, Tomasz Korbak, Germán Kruszewski +2
As language models (LMs) become more capable, it is increasingly important to align them with human preferences. However, the dominant paradigm for training Preference Models (PMs)…
Should you marginalize over possible tokenizations?
Nadezhda Chirkova, Germán Kruszewski, Jos Rozen +1
Autoregressive language models (LMs) map token sequences to probabilities. The usual practice for computing the probability of any character string (e.g. English sentences) is to f…