3 citations · 5 across the 4 of their papers we have counts for
4 papers
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
Siyan Zhao, Mingyi Hong, Yang Liu +2
Large Language Models (LLMs) are increasingly used as chatbots, yet their ability to personalize responses to user preferences remains limited. We introduce PrefEval, a benchmark f…
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues
Yu Li, Devamanyu Hazarika, Di Jin +2
Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematica…
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Taha Aksu, Devamanyu Hazarika, Shikib Mehri +4
Instruction-based multitasking has played a critical role in the success of large language models (LLMs) in multi-turn dialog applications. While publicly available LLMs have shown…
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Di Jin, Shikib Mehri, Devamanyu Hazarika +4
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF)…