123 citations · 163 across the 9 of their papers we have counts for
12 papers
OptiSelect: How does the Optimizer Shape Data Curriculum?
Simin Fan, Alireza Abdollahpoorrostam, Martin Jaggi
Online data selection has demonstrated substantial efficiency gains for LLM pretraining by training on the most valuable candidates within each batch. Since a candidate's value is…
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…
Semantic uncertainty in advanced decoding methods for LLM generation
Darius Foodeei, Simin Fan, Martin Jaggi
This study investigates semantic uncertainty in large language model (LLM) outputs across different decoding methods, focusing on emerging techniques like speculative sampling and…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
GRAPE: Optimize Data Mixture for Group Robust Multi-target Adaptive Pretraining
Simin Fan, Maria Ios Glarou, Martin Jaggi
The performance of large language models (LLMs) across diverse downstream applications is fundamentally governed by the quality and composition of their pretraining corpora. Existi…
NeuralGrok: Accelerate Grokking by Neural Gradient Transformation
Xinyu Zhou, Simin Fan, Martin Jaggi +1
Grokking is proposed and widely studied as an intricate phenomenon in which generalization is achieved after a long-lasting period of overfitting. In this work, we propose NeuralGr…
HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation
Xinyu Zhou, Simin Fan, Martin Jaggi
Influence functions provide a principled method to assess the contribution of individual training samples to a specific target. Yet, their high computational costs limit their appl…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
Deep Grokking: Would Deep Neural Networks Generalize Better?
Simin Fan, Razvan Pascanu, Martin Jaggi
Recent research on the grokking phenomenon has illuminated the intricacies of neural networks' training dynamics and their generalization behaviors. Grokking refers to a sharp rise…
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Zeming Chen, Alejandro Hernández Cano, Angelika Romanou +17
Large language models (LLMs) can potentially democratize access to medical knowledge. While many efforts have been made to harness and improve LLMs' medical knowledge and reasoning…
Irreducible Curriculum for Language Model Pretraining
Simin Fan, Martin Jaggi
Automatic data selection and curriculum design for training large language models is challenging, with only a few existing methods showing improvements over standard training. Furt…
DoGE: Domain Reweighting with Generalization Estimation
Simin Fan, Matteo Pagliardini, Martin Jaggi
The coverage and composition of the pretraining data significantly impacts the generalization ability of Large Language Models (LLMs). Despite its importance, recent LLMs still rel…