15 citations · 31 across the 8 of their papers we have counts for
8 papers
Learning Next Action Predictors from Human-Computer Interaction
Omar Shaikh, Valentin Teutschbein, Kanishk Gandhi +8
Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reason…
Just-In-Time Objectives: A General Approach for Specialized AI Interactions
Michelle S. Lam, Omar Shaikh, Hallie Xu +5
Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user's in-the-mo…
Knoll: Creating a Knowledge Ecosystem for Large Language Models
Dora Zhao, Diyi Yang, Michael S. Bernstein
Large language models are designed to encode general purpose knowledge about the world from Internet data. Yet, a wealth of information falls outside this scope -- ranging from per…
Creating General User Models from Computer Use
Omar Shaikh, Shardul Sapkota, Shan Rizvi +4
Human-computer interaction has long imagined technology that understands us-from our preferences and habits, to the timing and purpose of our everyday actions. Yet current user mod…
Personalization of Large Language Models: A Survey
Zhehao Zhang, Ryan A. Rossi, Branislav Kveton +18
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most exist…
Aligning Language Models with Demonstrated Feedback
Omar Shaikh, Michelle S. Lam, Joey Hejna +4
Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. Steering LLMs away from generic output is possible t…
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated out…
Grounding Gaps in Language Model Generations
Omar Shaikh, Kristina Gligorić, Ashna Khetan +3
Effective conversation requires common ground: a shared understanding between the participants. Common ground, however, does not emerge spontaneously in conversation. Speakers and…