Publications

From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.
Zahra Sheikhbahaee
Adam Safron
Casper Hesp
Guillaume Dumas
2023-12-01
Physics of Life Reviews (publié)
doi.org
arxiv.org
Large language models: What could they do for neurology?
Guillaume Lajoie
2023-12-01
Journal of the Neurological Sciences (publié)
doi.org
A large-scale exploratory study of android sports apps in the google play store
Bhagya Chembakottu
Heng Li
Foutse Khomh
2023-12-01
Information and Software Technology (publié)
doi.org
arxiv.org
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
Amirhossein Kazemnejad
Mehdi Rezagholizadeh
Prasanna Parthasarathi
Sarath Chandar
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Nash Learning from Human Feedback
R'emi Munos
Michal Valko
Daniele Calandriello
M. G. Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (voir plus)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (prépublication)
doi.org
arxiv.org
Predictive inference for travel time on transportation networks
Mohamad Elmasri
Aurélie Labbe
Denis Larocque
Laurent Charlin
2023-12-01
The Annals of Applied Statistics (publié)
doi.org
arxiv.org
Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding
Cesare Spinoso-Di Piano
Samira Abbasgholizadeh-Rahimi
Jackie Cheung
Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative… (voir plus) codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
Maxime DARRIN
Pablo Piantanida
Pierre Colombo
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots… (voir plus), is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.
2023-12-01
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Shape-Based Measures Improve Scene Categorization
Morteza Rezanejad
John Wilder
Dirk B. Walther
Allan D. Jepson
Sven Dickinson
Kaleem Siddiqi
Converging evidence indicates that deep neural network models that are trained on large datasets are biased toward color and texture informa… (voir plus)tion. Humans, on the other hand, can easily recognize objects and scenes from images as well as from bounding contours. Mid-level vision is characterized by the recombination and organization of simple primary features into more complex ones by a set of so-called Gestalt grouping rules. While described qualitatively in the human literature, a computational implementation of these perceptual grouping rules is so far missing. In this article, we contribute a novel set of algorithms for the detection of contour-based cues in complex scenes. We use the medial axis transform (MAT) to locally score contours according to these grouping rules. We demonstrate the benefit of these cues for scene categorization in two ways: (i) Both human observers and CNN models categorize scenes most accurately when perceptual grouping information is emphasized. (ii) Weighting the contours with these measures boosts performance of a CNN model significantly compared to the use of unweighted contours. Our work suggests that, even though these measures are computed directly from contours in the image, current CNN models do not appear to extract or utilize these grouping cues.
2023-12-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
Spectral Temporal Contrastive Learning
Sacha Morin
Somjit Nath
Samira Ebrahimi Kahou
Guy Wolf
Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, pa… (voir plus)rticularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.
2023-12-01
ArXiv (prépublication)
doi.org
arxiv.org
SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking
Javin Liu
Hao Yu
Vidya Sujaya
Pratheeksha Nair
Kellin Pelrine
Reihaneh Rabbany
In this work, we propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting perso… (voir plus)n names from noisy escort advertisements. Our method combines the simplicity of rule-matching (through antirules, i.e., negated rules) and the generalizability of large language models fine-tuned on benchmark, domain-specific and synthetic datasets, treating them as weak labels. One of the major challenges in this domain is limited labeled data. SWEET addresses this by obtaining multiple weak labels through labeling functions and effectively aggregating them. SWEET outperforms the previous supervised SOTA method for this task by 9% F1 score on domain data and better generalizes to common benchmark datasets. Furthermore, we also release HTGEN, a synthetically generated dataset of escort advertisements (built using ChatGPT) to facilitate further research within the community.
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Systematic Generalization by Finetuning? Analyzing Pretrained Language Models Using Constituency Tests
Aishik Chakraborty
Jackie Ck Cheung
Timothy O'Donnell
Constituents are groups of words that behave as a syntactic unit. Many linguistic phenomena (e.g., question formation, diathesis alternation… (voir plus)s) require the manipulation and rearrangement of constituents in a sentence. In this paper, we investigate how different finetuning setups affect the ability of pretrained sequence-to-sequence language models such as BART and T5 to replicate constituency tests — transformations that involve manipulating constituents in a sentence. We design multiple evaluation settings by varying the combinations of constituency tests and sentence types that a model is exposed to during finetuning. We show that models can replicate a linguistic transformation on a specific type of sentence that they saw during finetuning, but performance degrades substantially in other settings, showing a lack of systematic generalization. These results suggest that models often learn to manipulate sentences at a surface level unrelated to the constituent-level syntactic structure, for example by copying the first word of a sentence. These results may partially explain the brittleness of pretrained language models in downstream tasks.
2023-12-01
Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP (publié)
doi.org