36 citations · 51 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Personalized Collaborative Fine-Tuning for On-Device Large Language Models
Nicolas Wagner, Dongyang Fan, Martin Jaggi
We explore on-device self-supervised collaborative fine-tuning of large language models with limited local data availability. Taking inspiration from the collaborative learning com…
Controllable Topic-Focused Abstractive Summarization
Seyed Ali Bahrainian, Martin Jaggi, Carsten Eickhoff
Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects by shifting the distribution of generated text towards a…
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…
CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference
Amirkeivan Mohtashami, Matteo Pagliardini, Martin Jaggi
Scaling language models to larger and deeper sizes has led to significant boosts in performance. Even though the size of these models limits their application in compute-constraine…