9 citations · 11 across the 11 of their papers we have counts for
14 papers
Beyond Next-Screen Prediction: The Decision Value of GUI World Models
Sizhe Tang, Hanhan Zhou, Jeffrey Svajlenko +1
GUI world models are increasingly used to improve action selection by predicting the consequences of candidate actions, but it is unclear how much of the resulting gain comes from…
BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval
Eylon Caplan, Shamik Roy, Shib Sankar Dasgupta +2
Open-ended queries in modern Retrieval-Augmented Generation (RAG) are increasingly "diffuse," requiring a large set of documents to be assembled into a finite LLM context window. T…
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan +2
Agent trajectories record what an agent does and what happens next. Yet standard supervised fine-tuning (SFT) applies loss only to agent-authored action tokens, using environment o…
Probing the Prompt KV Cache: Where It Becomes Dispensable
Vinayshekhar Bannihatti Kumar, Manoj Ghuhan Arivazhagan, Disha Makhija +1
Prior KV cache compression schemes empirically demonstrate that the prompt cache is partially redundant during decoding, dropping or summarising entries with little accuracy loss.…
Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Vinayshekhar Bannihatti Kumar, Disha Makhija, Manoj Ghuhan Arivazhagan +1
Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from pretraining remains poorly u…
When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
Nishanth Sridhar Nakshatri, Shamik Roy, Manoj Ghuhan Arivazhagan +3
LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon thr…
When Users Are Happy but Agents Are Wrong: Multi-Dimensional Evaluation of Tool-Augmented Dialogue
Tanya Shourya, Yingfan Wang, Zhaoyi Joey Hou +3
Evaluating conversational AI systems that use external tools is challenging, as errors can arise from complex interactions among user, agent, and tools. While existing evaluation m…
Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis
Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar +1
Membership inference attacks (MIAs) reveal whether specific data was used to train machine learning models, serving as important tools for privacy auditing and compliance assessmen…
FairGen: Controlling Sensitive Attributes for Fair Generations in Diffusion Models via Adaptive Latent Guidance
Mintong Kang, Vinayshekhar Bannihatti Kumar, Shamik Roy +4
Text-to-image diffusion models often exhibit biases toward specific demographic groups, such as generating more males than females when prompted to generate images of engineers, ra…
Constrained Decoding with Speculative Lookaheads
Nishanth Nakshatri, Shamik Roy, Rajarshi Das +3
Constrained decoding with lookahead heuristics (CDLH) is a highly effective method for aligning LLM generations to human preferences. However, the extensive lookahead roll-out oper…
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
Dhruv Agarwal, Rajarshi Das, Sopan Khosla +1
We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within…
Privacy Adhering Machine Un-learning in NLP
Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, Dan Roth
Regulations introduced by General Data Protection Regulation (GDPR) in the EU or California Consumer Privacy Act (CCPA) in the US have included provisions on the \textit{right to b…
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters
Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, Dan Roth
Research has shown that personality is a key driver to improve engagement and user experience in conversational systems. Conversational agents should also maintain a consistent per…
Exploring the Role of Logically Related Non-Question Phrases for Answering Why-Questions
Niraj Kumar, Rashmi Gangadharaiah, Kannan Srinathan +1
In this paper, we show that certain phrases although not present in a given question/query, play a very important role in answering the question. Exploring the role of such phrases…