From the 1 of 6 linked papers with an AI index.
6 papers
Pigeonholing: how bad prompts hurt models, causing collapse and mistakes
Hyunji Nam, Keertana Chidambaram, Dorottya Demszky +1
The paper studies how poorly chosen prompts and conversation contexts cause large language models to repeat mistakes, narrow their output diversity, and change stances—a problem th…
Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data
Hyunji Nam, Haoran Li, Natasha Jaques
While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existi…
EduCoder: An Open-Source Annotation System for Education Transcript Data
Saad Ashraf, James Malamut, Vishal Kumar +7
We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text annotation tools for NLP and qua…
Learning to summarize user information for personalized reinforcement learning from human feedback
Hyunji Nam, Yanming Wan, Mickel Liu +3
As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to align to different users' prefere…
Netflix Artwork Personalization via LLM Post-training
Hyunji Nam, Sejoon Oh, Emma Kong +2
Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment…
IDEAlign: Comparing Ideas of Large Language Models to Domain Expert
Hyunji Nam, Lucia Langlois, James Malamut +2
Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of idea-level similarity to expert a…