Publications

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results
Cantin Baron
Sarah Cherkaoui
Sandra Therrien-Laperriere
Yann Ilboudo
Raphaël Poujol
Pamela Mehanna
Melanie E. Garrett
Marilyn J. Telen
Allison E. Ashley-Koch
Pablo Bartolucci
John D. Rioux
Guillaume Lettre
Christine Des Rosiers
Matthieu Ruiz
Julie G. Hussin
Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights in… (voir plus)to our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.
2023-11-30
iScience (publié)
doi.org
Growth of TiO2 single crystals by the Verneuil method at different gas flow ratio
Xudong Liu
Hanshu Ma
Wei Wang
Yongqi Hu
Xudong Sun
2023-11-30
Journal of Crystal Growth (publié)
doi.org
Large language models: What could they do for neurology?
Guillaume Lajoie
2023-11-30
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
2023-11-30
Information and Software Technology (publié)
doi.org
arxiv.org
McGill BabyLM Shared Task Submission: The Effects of Data Formatting and Structural Biases
Ziling Cheng
Rahul Aralikatte
Ian Porada
Cesare Spinoso-Di Piano
Jackie Chi Kit Cheung
In this study, we describe our submission to the 2023 BabyLM shared-task’s strict-small track. Our findings demonstrate the feasibility of… (voir plus) training high-performing models within the constraints of limited data, computational resources, and time. We provide evidence that the formatting of input can significantly impact downstream performance. Furthermore, the induction of structural biases into the models through the use of part-of-speech trees yields modest benefits. Our most successful model achieves 79% on the BLiMP evaluations and 72% on the SuperGLUE evaluations. All models trained during this study can be found at https://huggingface . co/mcgill-babylm . 12
2023-11-30
Conference on Computational Natural Language Learning (publié)
doi.org
McGill BabyLM Shared Task Submission: The Effects of Data Formatting and Structural Biases
Ziling Cheng
Rahul Aralikatte
Ian Porada
Cesare Spinoso-Di Piano
Jackie CK Cheung
In this study, we describe our submission to the 2023 BabyLM shared-task's strict-small track.Our findings demonstrate the feasibility of tr… (voir plus)aining high-performing models within the constraints of limited data, computational resources, and time.We provide evidence that the formatting of input can significantly impact downstream performance.Furthermore, the induction of structural biases into the models through the use of part-of-speech trees yields modest benefits.Our most successful model achieves 79% on the BLiMP evaluations and 72% on the SuperGLUE evaluations.
2023-11-30
BabyLM Challenge @ Conference on Computational Natural Language Learning (publié)
doi.org
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
Amirhossein Kazemnejad
Mehdi Rezagholizadeh
Prasanna Parthasarathi
Sarath Chandar
While pre-trained language models (PLMs) have shown evidence of acquiring vast amounts of knowledge, it remains unclear how much of this par… (voir plus)ametric knowledge is actually usable in performing downstream tasks. We propose a systematic framework to measure parametric knowledge utilization in PLMs. Our framework first extracts knowledge from a PLM's parameters and subsequently constructs a downstream task around this extracted knowledge. Performance on this task thus depends exclusively on utilizing the model's possessed knowledge, avoiding confounding factors like insufficient signal. As an instantiation, we study factual knowledge of PLMs and measure utilization across 125M to 13B parameter PLMs. We observe that: (1) PLMs exhibit two gaps - in acquired vs. utilized knowledge, (2) they show limited robustness in utilizing knowledge under distribution shifts, and (3) larger models close the acquired knowledge gap but the utilized knowledge gap remains. Overall, our study provides insights into PLMs' capabilities beyond their acquired knowledge.
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Predictive inference for travel time on transportation networks
Mohamad Elmasri
Aurélie Labbe
Denis Larocque
Laurent Charlin
2023-11-30
The Annals of Applied Statistics (publié)
doi.org
arxiv.org
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Gaurav Sahu
Olga Vechtomova
Dzmitry Bahdanau
Issam Laradji
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. … (voir plus)Recent work often tackles this problem using large language models (LLMs) like GPT3 that can generate new examples given already available ones. In this work, we propose a method to generate more helpful augmented data by utilizing the LLM's abilities to follow instructions and perform few-shot classifications. Our specific PromptMix method consists of two steps: 1) generate challenging text augmentations near class boundaries; however, generating borderline examples increases the risk of false positives in the dataset, so we 2) relabel the text augmentations using a prompting-based LLM classifier to enhance the correctness of labels in the generated data. We evaluate the proposed method in challenging 2-shot and zero-shot settings on four text classification datasets: Banking77, TREC6, Subjectivity (SUBJ), and Twitter Complaints. Our experiments show that generating and, crucially, relabeling borderline examples facilitates the transfer of knowledge of a massive LLM like GPT3.5-turbo into smaller and cheaper classifiers like DistilBERT
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
openreview.net
Qualitative Code Suggestion: A Human-Centric Approach To Qualitative Coding
Cesare Spinoso-Di Piano
Samira Abbasgholizadeh Rahimi
Jackie Chi Kit 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.
2023-11-30
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.
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Yu Lu Liu
Meng Cao
Su Lin Blodgett
Jackie CK Cheung
A.R. Olteanu
Adam Trischler
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net