123 citations · 137 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging
Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret +1
The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. W…
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…
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…
Landmark Attention: Random-Access Infinite Context Length for Transformers
Amirkeivan Mohtashami, Martin Jaggi
While Transformers have shown remarkable success in natural language processing, their attention mechanism's large memory requirements have limited their ability to handle longer c…
Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models
Amirkeivan Mohtashami, Mauro Verzetti, Paul K. Rubenstein
Learned metrics such as BLEURT have in recent years become widely employed to evaluate the quality of machine translation systems. Training such metrics requires data which can be…