Ce programme soutient les startups spécialisées en IA à tout moment de l'année. Bénéficiez de ressources de pointe et d'un accompagnement sur mesure pour accélérer le développement de votre technologie.
Développez des compétences fondamentales en intelligence artificielle (IA) responsable grâce à des cours autodirigés, animés par des expert·e·s de Mila reconnu·e·s à l’échelle internationale.
Le Fellowship Mila en politiques de l'IA transforme l'expertise approfondie en IA en politiques rigoureuses d'intérêt public. Découvrez la dernière publication Combler la disparité en matière d’expertise : mécanismes de transfert des connaissances pour la réglementation de l’IA par Moritz von Knebel.
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Publications
Towards Assessing Deep Learning Test Input Generators
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.
ABSTRACT Thraustochytrids, diverse marine unicellular protists encompassing over 10 recognised genera, are renowned for synthesising polyuns… (voir plus)aturated fatty acids (PUFAs), with content and composition varying substantially across genera. While PUFAs are known to be produced via PUFA synthase (PUFA‐S) and/or elongase/desaturase (ELO/DES) pathways, the distinctions in genes involved remain unexplored. This study analysed PUFA biosynthetic genes in 19 thraustochytrid strains across six genera, categorising them into four types. Type I exclusively utilises the ELO/DES pathway, Type II employs both PUFA‐S and complete ELO/DES pathways, while Types III and IV primarily rely on PUFA‐S, with Type III lacking the canonical Δ9 desaturase and Type IV missing most desaturase and elongase enzymes. Notably, the Δ9 desaturase and ATP‐citrate lyase (ACLY) are exclusive to Types I and II, while β‐carotene hydroxylase (CrtZ) is absent in these types. ACLY absence suggests alternative acetyl‐CoA supply pathways in Types III and IV, whereas CrtZ absence implies either a lack of specific xanthophylls or alternative biosynthetic pathways in Types I and II. Synteny analysis revealed conserved genomic organisation of PUFA biosynthetic genes, indicating a shared evolutionary trajectory. This study provides insights into the genetic diversity underlying PUFA biosynthesis in thraustochytrids, while proposing putative evolutionary pathways for the four lineages.
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially du… (voir plus)e to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: 1. Retrieving related works given a query abstract, and 2. Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods, while providing insights into the LLM's decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Further, we demonstrate that our planning-based approach achieves higher-quality reviews by minimizing hallucinated references in the generated review by 18-26% compared to existing simpler LLM-based generation methods.