281 citations · 451 across the 25 of their papers we have counts for
30 papers
Scaling Laws for Multimodal Data Mixtures
Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu +5
Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the m…
Learning What to Imitate: Entropy-Aware Distribution Mixing
Juan Garcia Giraldo, Matteo Santelmo, Eduard Durech +3
Small language models are often post-trained as students on reasoning traces from stronger teacher models to efficiently learn new skills. However, token-level imitation on traces…
Adapting Linear-Time Architectures for Tabular In-Context Learning
David Schnurr, Felix Sarnthein, Thomas Hofmann +1
Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large datasets. Existing linear-time…
Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing
Tommaso Cerruti, Tim Rieder, George Rowlands +2
Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context. This paper prese…
POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents
Qiaoyuan Zheng, Yiqu Yang, Qi Gao +1
LLM agents increasingly have access to private user data and act on the user's behalf when interacting with third-party systems. The user defines what may and must not be shared, a…
SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
Alexis Limozin, Eduard Durech, Torsten Hoefler +2
Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements over the standard SFT-then-RL…
An Engineering Journey Training Large Language Models at Scale on Alps: The Apertus Experience
Jonathan Coles, Stefano Schuppli, Lukas Drescher +20
Large Language Models (LLMs) have surged as a transformative technology for science and society, prompting governments worldwide to pursue sovereign AI capabilities that ensure dat…
Benchmarking Concept-Spilling Across Languages in LLMs
Ilia Badanin, Daniil Dzenhaliou, Imanol Schlag
Multilingual Large Language Models (LLMs) exhibit remarkable cross-lingual abilities, yet often exhibit a systematic bias toward the representations from other languages, resulting…
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…
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/…
Towards Fully FP8 GEMM LLM Training at Scale
Alejandro Hernández-Cano, Dhia Garbaya, Imanol Schlag +1
Despite the significant potential of FP8 data formats for large language model (LLM) pre-training, their adoption has been limited due to challenges in maintaining stability at sca…
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)…
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova +56
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal val…
Deriving Activation Functions Using Integration
Allen Hao Huang, Imanol Schlag
Our work proposes a novel approach to designing activation functions by focusing on their gradients and deriving the corresponding activation functions using integration. We introd…
Understanding and Minimising Outlier Features in Neural Network Training
Bobby He, Lorenzo Noci, Daniele Paliotta +2
Outlier Features (OFs) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width. They are well known to emerge during standard tr…
The Role of Language Imbalance in Cross-lingual Generalisation: Insights from Cloned Language Experiments
Anton Schäfer, Shauli Ravfogel, Thomas Hofmann +2
Multilinguality is crucial for extending recent advancements in language modelling to diverse linguistic communities. To maintain high performance while representing multiple langu…
On the Effect of (Near) Duplicate Subwords in Language Modelling
Anton Schäfer, Thomas Hofmann, Imanol Schlag +1
Tokenisation is a core part of language models (LMs). It involves splitting a character sequence into subwords which are assigned arbitrary indices before being served to the LM. W…
Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag +1
In recent years, the state-of-the-art in deep learning has been dominated by very large models that have been pre-trained on vast amounts of data. The paradigm is very simple: inve…
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
Aleksandar Stanić, Dylan Ashley, Oleg Serikov +5
The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field…
Large Language Model Programs
Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz +4
In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to param…
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge, Haozhe Liu, Francesco Faccio +23
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each o…
Solving Quantitative Reasoning Problems with Language Models
Aitor Lewkowycz, Anders Andreassen, David Dohan +11
Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally stru…
Block-Recurrent Transformers
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu +2
We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length.…
A Modern Self-Referential Weight Matrix That Learns to Modify Itself
Kazuki Irie, Imanol Schlag, Róbert Csordás +1
The weight matrix (WM) of a neural network (NN) is its program. The programs of many traditional NNs are learned through gradient descent in some error function, then remain fixed.…