565 citations · 1.1k across the 13 of their papers we have counts for
14 papers
Ready to Speak: Aligning LLMs for TTS-Friendly Text Generation
Thibaut Thonet, Jos Rozen, Laurent Besacier
Current Large Language Models (LLMs) are primarily optimized for written text, often producing outputs that are grammatically correct and helpful yet poorly suited for spoken deliv…
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…
Guaranteed Generation from Large Language Models
Minbeom Kim, Thibaut Thonet, Jos Rozen +3
As large language models (LLMs) are increasingly used across various applications, there is a growing need to control text generation to satisfy specific constraints or requirement…
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models
Thibaut Thonet, Jos Rozen, Laurent Besacier
Research on Large Language Models (LLMs) has recently witnessed an increasing interest in extending the models' context size to better capture dependencies within long documents. W…
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…
disco: a toolkit for Distributional Control of Generative Models
Germán Kruszewski, Jos Rozen, Marc Dymetman
Pre-trained language models and other generative models have revolutionized NLP and beyond. However, these models tend to reproduce undesirable biases present in their training dat…
Aligning Language Models with Preferences through f-divergence Minimization
Dongyoung Go, Tomasz Korbak, Germán Kruszewski +3
Aligning language models with preferences can be posed as approximating a target distribution representing some desired behavior. Existing approaches differ both in the functional…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Unsupervised and Distributional Detection of Machine-Generated Text
Matthias Gallé, Jos Rozen, Germán Kruszewski +1
The power of natural language generation models has provoked a flurry of interest in automatic methods to detect if a piece of text is human or machine-authored. The problem so far…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
Self-Supervised and Controlled Multi-Document Opinion Summarization
Hady Elsahar, Maximin Coavoux, Matthias Gallé +1
We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that…