21 citations · 22 across the 13 of their papers we have counts for
5 papers · 2 filters
Do Generalisation Results Generalise?
Matteo Boglioni, Andrea Sgobbi, Gabriel Tavernini +3
A large language model's (LLM's) out-of-distribution (OOD) generalisation ability is crucial to its deployment. Previous work assessing LLMs' generalisation performance, however, t…
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to…
Understanding the Influence of Synthetic Data for Text Embedders
Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy +2
Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLM-generated data. Nonetheless, no publicly available…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
Not All Data Are Unlearned Equally
Aravind Krishnan, Siva Reddy, Marius Mosbach
Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context of large language models (LLMs), unlear…