36 citations · 63 across the 32 of their papers we have counts for
12 papers · 2 filters
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…
Revisiting Multilingual Data Mixtures in Language Model Pretraining
Negar Foroutan, Paul Teiletche, Ayush Kumar Tarun +1
The impact of different multilingual data mixtures in pretraining large language models (LLMs) has been a topic of ongoing debate, often raising concerns about potential trade-offs…
RLMEval: Evaluating Research-Level Neural Theorem Proving
Auguste Poiroux, Antoine Bosselut, Viktor Kunčak
Despite impressive results on curated benchmarks, the practical impact of large language models (LLMs) on research-level neural theorem proving and proof autoformalization is still…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining
Deniz Bayazit, Aaron Mueller, Antoine Bosselut
Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, because traditional evaluation methods (e…
Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization
Negar Foroutan, Clara Meister, Debjit Paul +4
Tokenization is the first -- and often least scrutinized -- step of most NLP pipelines. Standard algorithms for learning tokenizers rely on frequency-based objectives, which favor…
PERK: Long-Context Reasoning as Test-Time Learning
Zeming Chen, Angelika Romanou, Gail Weiss +1
Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. In this work, we propose PERK (Parameter Efficient Reasoning over Kn…
ConLID: Supervised Contrastive Learning for Low-Resource Language Identification
Negar Foroutan, Jakhongir Saydaliev, Ye Eun Kim +1
Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting dive…
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
Silin Gao, Antoine Bosselut, Samy Bengio +1
Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in grade school math (GSM) reasoning. In particular, they tend to experi…
Positional Fragility in LLMs: How Offset Effects Reshape Our Understanding of Memorization Risks
Yixuan Xu, Antoni-Joan Solergibert i Llaquet, Antoine Bosselut +1
Large language models are known to memorize parts of their training data, posing risk of copyright violations. To systematically examine this risk, we pretrain language models (1B/…
Creative Preference Optimization
Mete Ismayilzada, Antonio Laverghetta, Simone A. Luchini +4
While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content-characterized…
Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling Opt-Outs
Dongyang Fan, Vinko Sabolčec, Matin Ansaripour +4
The increasing adoption of web crawling opt-outs by copyright holders of online content raises critical questions about the impact of data compliance on large language model (LLM)…