25 citations · 31 across the 10 of their papers we have counts for
14 papers
How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling
Dhananjay Ashok, Shantanu Agarwal, Vivek Datla +2
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Langu…
GameBoyWorlds: A Testbed for Self-Improvement in Embodied Video Games
Dhananjay Ashok, Adam Shen, Aslan Huo Feng +10
Powered by expert guidance, agents can operate in interactive environments; however, it is unclear whether they can learn autonomously from their own experience. To evaluate such s…
Toward Interactive Understanding of Code APIs
Dhananjay Ashok, Jesse Thomason, Jonathan May
Empowered by advances in Language Model agents, systems have made substantial strides in code generation and understanding. However, these approaches often rely on read access to t…
Seamless Deception: Larger Language Models Are Better Knowledge Concealers
Dhananjay Ashok, Ruth-Ann Armstrong, Jonathan May
Language Models (LMs) may acquire harmful knowledge, and yet feign ignorance of these topics when under audit. Inspired by the recent discovery of deception-related behaviour patte…
A Representation Sharpening Framework for Zero Shot Dense Retrieval
Dhananjay Ashok, Suraj Nair, Mutasem Al-Darabsah +3
Zero-shot dense retrieval is a challenging setting where a document corpus is provided without relevant queries, necessitating a reliance on pretrained dense retrievers (DRs). Howe…
Can VLMs Recall Factual Associations From Visual References?
Dhananjay Ashok, Ashutosh Chaubey, Hirona J. Arai +2
Through a controlled study, we identify a systematic deficiency in the multimodal grounding of Vision Language Models (VLMs). While VLMs can recall factual associations when provid…