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Publications
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of… (see more) the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about \emph{model collapse}, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of verification on synthesized data to prevent model collapse. We provide a theoretical characterization using Gaussian mixtures, linear classifiers, and linear verifiers to derive conditions with measurable proxies to assess whether the verifier can effectively select synthesized data that leads to optimal performance. We experiment with two practical tasks -- computing matrix eigenvalues with transformers and news summarization with LLMs -- which both exhibit model collapse when trained on generated data, and show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse and that our proposed proxy measure strongly correlates with performance.
La comptabilité véhicule souvent injustement, une image terne et ennuyeuse, auprès du grand public et des jeunes étudiants choisissant l… (see more)eur orientation. Dans cet article, nous questionnons l’effet de pratiques pédagogiques sur la perception par les étudiants, des soft skills attendues par les employeurs. Pour cela nous réalisons une quasi-expérimentation dans laquelle nous comparons les perceptions des étudiants selon que le cours ait été animé sous un format classique (application des connaissances par le biais d’exercices avec corrigé par l’enseignant) ou sous la forme d’une simulation de gestion (application des connaissances en vue de prendre des décisions et piloter une entreprise fictive). Les résultats de la recherche montrent qu’une simulation de gestion, plus que les travaux dirigés classiques, permettent aux primo-apprenants en comptabilité, d’avoir une meilleure perception des soft skills attendues par les praticiens et les recruteurs. Nos résultats rappellent l’importance de donner une représentation réaliste (éloignée des clichés) de la profession, afin de rendre les filières d’enseignement de la comptabilité plus attractives.
The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (see more) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
2025-01-01
IEEE Transactions on Automation Science and Engineering (published)
Machine learning models may capture and amplify biases present in data, leading to disparate test performance across social groups. To bette… (see more)r understand, evaluate, and mitigate these possible biases, a deeper theoretical understanding of how model design choices and data distribution properties could contribute to bias is needed. In this work, we contribute a precise analytical theory in the context of ridge regression, both with and without random projections, where the former models neural networks in a simplified regime. Our theory offers a unified and rigorous explanation of machine learning bias, providing insights into phenomena such as bias amplification and minority-group bias in various feature and parameter regimes. For example, we demonstrate that there may be an optimal regularization penalty or training time to avoid bias amplification, and there can be fundamental differences in test error between groups that do not vanish with increased parameterization. Importantly, our theoretical predictions align with several empirical observations reported in the literature. We extensively empirically validate our theory on diverse synthetic and semi-synthetic datasets.
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (see more)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2025-01-01
European Conference on Information Retrieval (published)
Background:
Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their in… (see more)ductive nature, ensuring the quality of these systems remains a significant challenge for the research community. Traditionally, software systems were constructed deductively, by writing explicit rules that govern the behavior of the system as program code. However, ML/DL systems infer rules from training data i.e., they are generated inductively). Recent research in ML/DL quality assurance has adapted concepts from traditional software testing, such as mutation testing, to improve reliability. However, it is unclear if these proposed testing techniques are adopted in practice, or if new testing strategies have emerged from real-world ML deployments. There is little empirical evidence about the testing strategies.
Aims:
To fill this gap, we perform the first fine-grained empirical study on ML testing in the wild to identify the ML properties being tested, the testing strategies, and their implementation throughout the ML workflow.
Method:
We conducted a mixed-methods study to understand ML software testing practices. We analyzed test files and cases from 11 open-source ML/DL projects on GitHub. Using open coding, we manually examined the testing strategies, tested ML properties, and implemented testing methods to understand their practical application in building and releasing ML/DL software systems.
Results:
Our findings reveal several key insights: 1.) The most common testing strategies, accounting for less than 40%, are Grey-box and White-box methods, such as
Negative Testing
,
Oracle Approximation
, and
Statistical Testing
. 2.) A wide range of
\(17\)
ML properties are tested, out of which only 20% to 30% are frequently tested, including
Consistency
,
Correctness
, and
Efficiency
. 3.)
Bias and Fairness
is more tested in Recommendation (6%) and CV (3.9%) systems, while
Security & Privacy
is tested in CV (2%), Application Platforms (0.9%), and NLP (0.5%). 4.) We identified 13 types of testing methods, such as
Unit Testing
,
Input Testing
, and
Model Testing
.
Conclusions:
This study sheds light on the current adoption of software testing techniques and highlights gaps and limitations in existing ML testing practices.
