Summary of Llms As Function Approximators: Terminology, Taxonomy, and Questions For Evaluation, by David Schlangen
LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluationby David SchlangenFirst submitted to arxiv…
LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluationby David SchlangenFirst submitted to arxiv…
Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Databy Charles Jin,…
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Modelsby Zhuo Chen, Jiawei Liu,…
Do LLMs have Consistent Values?by Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, Ella DanielFirst…
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Modelsby Alexander R. Pelletier,…
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalby Hongjin Su, Howard Yen, Mengzhou Xia,…
DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusionby Huiguo He, Huan Yang, Zixi Tuo,…
Halu-J: Critique-Based Hallucination Judgeby Binjie Wang, Steffi Chern, Ethan Chern, Pengfei LiuFirst submitted to arxiv…
Temporal Label Hierachical Network for Compound Emotion Recognitionby Sunan Li, Hailun Lian, Cheng Lu, Yan…
A Three-Stage Algorithm for the Closest String Problem on Artificial and Real Gene Sequencesby Alireza…