Seeking Interpretability and Explainability in Binary Activated Neural Networks
Benjamin Leblanc
Should We Attend More or Less? Modulating Attention for Fairness
Abdelrahman Zayed
Goncalo Mordido
Samira Shabanian
A Survey on Deep Learning for Theorem Proving
Zhaoyu Li
Jialiang Sun
Logan Murphy
Qidong Su
Zenan Li
Xian Zhang
Kaiyu Yang
The black box of the relationship between breast cancer patients and accompanying patients: the accompanied patients’ point of view
Marie-Pascale Pomey
Monica Iliescu Nelea
Cécile Vialaron
Louise Normandin
Marie‐Andrée Côté
Mado Desforges
Pénélope Pomey‐Carpentier
Nesrine Adjtoutah
Israël Fortin
Isabelle Ganache
Zeev Rosberger
Danielle Charpentier
Lynda Bélanger
Michel Dorval
Djahanchah Philip Ghadiri
Mélanie Lavoie-Tremblay
Antoine Boivin
Jean-François Pelletier
Nicolas Fernandez … (see 2 more)
Alain M. Danino
Michèle de Guise
Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild
Niloofar Mireshghallah
Maria Antoniak
Yash More
Yejin Choi
Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate pr… (see more)ivacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.
V-STaR: Training Verifiers for Self-Taught Reasoners
Arian Hosseini
Xingdi Yuan
Nikolay Malkin
Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on sel… (see more)f-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions. To address this shortcoming, we propose V-STaR that utilizes both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4% to 17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA2 models.
Web Retrieval Agents for Evidence-Based Misinformation Detection
Jacob-Junqi Tian
Hao Yu
Yury Orlovskiy
Tyler Vergho
Mauricio Rivera
Mayank Goel
Zachary Yang
Kellin Pelrine
What makes a good metric? Evaluating automatic metrics for text-to-image consistency
Candace Ross
Melissa Hall
Adina Williams
Automated River Substrate Mapping From Sonar Imagery With Machine Learning
C. S. Bodine
D. Buscombe
Canada’s approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics
Sheila F. O’Brien
Michael Asamoah-Boaheng
Brian Grunau
Mel Krajden
David M. Goldfarb
Maureen Anderson
Marc Germain
Patrick Brown
Derek R. Stein
Kami Kandola
Graham Tipples
Philip Awadalla
Amanda Lang
Lesley Behl
Tiffany Fitzpatrick
Steven J. Drews
Canada's approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics.
Sheila F. O’Brien
Michael Asamoah-Boaheng
Brian Grunau
Mel Krajden
David M. Goldfarb
Maureen Anderson
Marc Germain
Patrick Brown
Derek R. Stein
Kami Kandola
Graham Tipples
Philip Awadalla
Amanda Lang
Lesley Behl
Tiffany Fitzpatrick
Steven J. Drews
SETTING In Canada's federated healthcare system, 13 provincial and territorial jurisdictions have independent responsibility to collect data… (see more) to inform health policies. During the COVID-19 pandemic (2020-2023), national and regional sero-surveys mostly drew upon existing infrastructure to quickly test specimens and collect data but required cross-jurisdiction coordination and communication. INTERVENTION There were 4 national and 7 regional general population SARS-CoV-2 sero-surveys. Survey methodologies varied by participant selection approaches, assay choices, and reporting structures. We analyzed Canadian pandemic sero-surveillance initiatives to identify key learnings to inform future pandemic planning. OUTCOMES Over a million samples were tested for SARS-CoV-2 antibodies from 2020 to 2023 but siloed in 11 distinct datasets. Most national sero-surveys had insufficient sample size to estimate regional prevalence; differences in methodology hampered cross-regional comparisons of regional sero-surveys. Only four sero-surveys included questionnaires. Sero-surveys were not directly comparable due to different assays, sampling methodologies, and time-frames. Linkage to health records occurred in three provinces only. Dried blood spots permitted sample collection in remote populations and during stay-at-home orders. IMPLICATIONS To provide timely, high-quality information for public health decision-making, routine sero-surveillance systems must be adaptable, flexible, and scalable. National capability planning should include consortiums for assay design and validation, defined mechanisms to improve test capacity, base documents for data linkage and material transfer across jurisdictions, and mechanisms for real-time communication of data. Lessons learned will inform incorporation of a robust sero-survey program into routine surveillance with strategic sampling and capacity to adapt and scale rapidly as a part of a comprehensive national pandemic response plan.
Adaptive Accompaniment with ReaLchords
Yusong Wu
Tim Cooijmans
Kyle Kastner
Adam Roberts
Ian Simon
Alexander Scarlatos
Chris Donahue
Cassie Tarakajian
Shayegan Omidshafiei
Natasha Jaques
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (see more)essive output but are not able to generate in an online manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to unfamiliar input and produce fitting accompaniment. ReaLchords opens the door to live jamming, as well as simultaneous co-creation in other modalities.