From the 1 of 19 linked papers with an AI index.
19 papers
Informal Learning Emerges in Everyday Human-LLM Interaction
Zixin Chen, Haotian Li, Ziang Xiao +2
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities th…
Can We Trust Item Response Theory for AI Evaluation?
Han Jiang, Sunbeom Kwon, Jinwen Luo +2
The paper investigates how well item response theory (IRT) works for evaluating large language model benchmarks, highlighting challenges when benchmark data differ from traditional…
AI-Mediated Negotiation: Design Reflections and Lessons
Veda Duddu, Jash Rajesh Parekh, Andy Mao +4
Conversational AI promises a new kind of preparation for high-stakes workplace negotiations -- personalized, interactive, and capable of simulating realistic resistance. That promi…
On Defining Erasure Harms for NLP
Yu Lu Liu, Arnav Goel, Jackie Chi Kit Cheung +3
The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one suc…
PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization
Han Jiang, Dongyao Zhu, Xiaoyuan Yi +3
In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and accommodate diverse preferences withou…
AI Evaluation Should Require Standardized Item-Level Data Releases
Han Jiang, Susu Zhang, Dongyao Zhu +6
This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified it…