7 papers
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
The paper studies how large language models acquire and change their alignment with human values during post‑training, analyzing the impact of supervised fine‑tuning and preference…
Would you still call this Dax? Novel Visual References in VLMs and Humans
Ada Defne Tür, Gaurav Kamath, Joyce Chai +2
Vision-language models (VLMs), like human learners, are frequently exposed to new visual concepts, but how they map novel visual references to language after exposure remains large…
Humans and LLMs Diverge on Probabilistic Inferences
Gaurav Kamath, Sreenath Madathil, Sebastian Schuster +2
Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera MarjanoviÄ, 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…
Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?
Gaurav Kamath, Sowmya Vajjala
Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area o…
Build the web for agents, not agents for the web
Xing Han Lù, Gaurav Kamath, Marius Mosbach +1
Recent advancements in Large Language Models (LLMs) and multimodal counterparts have spurred significant interest in developing web agents -- AI systems capable of autonomously nav…