5 citations · 6 across the 6 of their papers we have counts for
6 papers
Pretrained Persona Mixture Models and Tandem Models for Human Simulation
Minwoo Kang, Téa Wright, Seun Eisape +6
We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, is inaccurate and produces st…
Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions
Minwoo Kang, Suhong Moon, Seung Hyeong Lee +4
Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses to various surveys and polls. However, t…
ALOHa: A New Measure for Hallucination in Captioning Models
Suzanne Petryk, David M. Chan, Anish Kachinthaya +4
Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not prese…
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta +2
Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we pr…
Moral Foundations of Large Language Models
Marwa Abdulhai, Gregory Serapio-Garcia, Clément Crepy +3
Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/deg…
CLAIR: Evaluating Image Captions with Large Language Models
David Chan, Suzanne Petryk, Joseph E. Gonzalez +2
The evaluation of machine-generated image captions poses an interesting yet persistent challenge. Effective evaluation measures must consider numerous dimensions of similarity, inc…