Publications

Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code

Vahid Majdinasab

Amin Nikanjam

Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not cont… (voir plus)ain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers' codes are already included in the dataset. Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models. Given the non-disclosure of the training datasets, traditional approaches such as code clone detection are insufficient for asserting copyright infringement. To address this challenge, we propose a new approach, TraWiC; a model-agnostic and interpretable method based on membership inference for detecting code inclusion in an LLM's training dataset. We extract syntactic and semantic identifiers unique to each program to train a classifier for detecting code inclusion. In our experiments, we observe that TraWiC is capable of detecting 83.87% of codes that were used to train an LLM. In comparison, the prevalent clone detection tool NiCad is only capable of detecting 47.64%. In addition to its remarkable performance, TraWiC has low resource overhead in contrast to pair-wise clone detection that is conducted during the auditing process of tools like CodeWhisperer reference tracker, across thousands of code snippets.

2024-11-02

ACM Transactions on Software Engineering and Methodology (publié)

doi.org

arxiv.org

AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

S. Gowaikar

Hugo Berard

Rashid A. Mushkani

Emmanuel Beaudry Marchand

Toumadher Ammar

Shin (Alexandre) Koseki

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (voir plus) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

2024-11-01

ArXiv (prépublication)

doi.org

arxiv.org

AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

S. Gowaikar

Hugo Berard

Rashid A. Mushkani

Emmanuel Beaudry Marchand

Toumadher Ammar

Shin (Alexandre) Koseki

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (voir plus) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

2024-11-01

arXiv (publié)

doi.org

arxiv.org

Association Between Circulating Vitamin K Levels, Gut Microbiome, and Type 1 Diabetes: A Mendelian Randomization Study

Samuel De La Barrera

Benjamin De La Barrera

Marc-André Legault

Isabel Gamache

Despoina Manousaki

2024-11-01

Nutrients (publié)

doi.org

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region

Armando Romani

A. Antonietti

Davide Bella

Julian Budd

Elisabetta Giacalone

Kerem Kurban

Sára Sáray

Marwan Abdellah

Alexis Arnaudon

Elvis Boci

Cristina Colangelo

Jean-Denis Courcol

Thomas Delemontex

András Ecker

Joanne Falck

Cyrille Favreau

Michael Gevaert

Juan B. Hernando

Joni Herttuainen

Genrich Ivaska … (voir 28 de plus)

Lida Kanari

Anna-Kristin Kaufmann

James King

Pramod Kumbhar

Sigrun Lange

Huanxiang Lu

Carmen Alina Lupascu

Rosanna Migliore

Fabien Petitjean

Judit Planas

Pranav Rai

Srikanth Ramaswamy

Michael W. Reimann

Juan Luis Riquelme

Nadir Román Guerrero

Ying Shi

Vishal Sood

Mohameth François Sy

Werner Van Geit

Liesbeth Vanherpe

Tamás F. Freund

Audrey Mercer

Eilif Benjamin Muller

Felix Schürmann

Alex M. Thomson

Michele Migliore

Szabolcs Káli

Henry Markram

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (voir plus)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

2024-11-01

PLoS Biology (publié)

doi.org

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region

Armando Romani

Alberto Antonietti

Davide Bella

Julian Budd

Elisabetta Giacalone

Kerem Kurban

Sára Sáray

Marwan Abdellah

Alexis Arnaudon

Elvis Boci

Cristina Colangelo

Jean-Denis Courcol

Thomas Delemontex

András Ecker

Joanne Falck

Cyrille Favreau

Michael Gevaert

Juan B. Hernando

Joni Herttuainen

Genrich Ivaska … (voir 28 de plus)

Lida Kanari

Anna-Kristin Kaufmann

James King

Pramod Kumbhar

Sigrun Lange

Huanxiang Lu

Carmen Alina Lupascu

Rosanna Migliore

Fabien Petitjean

Judit Planas

Pranav Rai

Srikanth Ramaswamy

Michael W. Reimann

Juan Luis Riquelme

Nadir Román Guerrero

Ying Shi

Vishal Sood

Mohameth François Sy

Werner Van Geit

Liesbeth Vanherpe

Tamás F. Freund

Audrey Mercer

Eilif Benjamin Muller

Felix Schürmann

Alex M. Thomson

Michele Migliore

Szabolcs Káli

Henry Markram

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (voir plus)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

2024-11-01

PLoS Biology (publié)

doi.org

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

Prakhar Ganeesh

Usman Gohar

Lu Cheng

Golnoosh Farnadi

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (voir plus)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.

2024-11-01

arXiv (publié)

doi.org

arxiv.org

Evaluating Generative AI Systems is a Social Science Measurement Challenge

Hanna Wallach

Meera Desai

Nicholas Pangakis

A. Feder Cooper

Angelina Wang

Solon Barocas

Alexandra Chouldechova

Chad Atalla

Su Lin Blodgett

Emily Corvi

P. A. Dow

Jean Garcia-Gathright

Alexandra Olteanu

Stefanie Reed

Emily Sheng

Dan Vann

Jennifer Wortman Vaughan

Matthew Vogel

Hannah Washington

Abigail Z. Jacobs … (voir 1 de plus)

Microsoft Research

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI … (voir plus)(GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.

2024-11-01

arXiv (publié)

doi.org

arxiv.org

GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models

Nizar Islah

Justine Gehring

Diganta Misra

Eilif Muller

Irina Rish

Terry Yue Zhuo

Massimo Caccia