activity
20232026
most citedArtificial Intelligence Index Report 2023

189 citations · 230 across the 8 of their papers we have counts for

collaborators

8 papers

cs.IR2026

Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA

Sunah O, Jay-Yoon Lee

In multimodal multi-hop question answering, we focus on the initial retrieval stage via two distinct tasks: (1) evidence set completion, retrieving missing evidence given context,…

cs.CV2025

LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model

Tao Sun, Oliver Liu, JinJin Li +1

Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring res…

cs.CL2025

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning

Erxin Yu, Jing Li, Ming Liao +7

Although large language models demonstrate strong performance across various domains, they still struggle with numerous bad cases in mathematical reasoning. Previous approaches to…

cs.CL2025★ 2 cited

SEA-LION: Southeast Asian Languages in One Network

Raymond Ng, Thanh Ngan Nguyen, Yuli Huang +28

Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages. However, the majority…

cs.CL2024

Naive Bayes-based Context Extension for Large Language Models

Jianlin Su, Murtadha Ahmed, Wenbo +3

Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations…

cs.CL2024

Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks

R. Patrick Xian, Alex J. Lee, Satvik Lolla +4

The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…

cs.CR2024★ 39 cited

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Evan Hubinger, Carson Denison, Jesse Mu +36

Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when giv…

cs.AI2023★ 189 cited

Artificial Intelligence Index Report 2023

Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson +11

Welcome to the sixth edition of the AI Index Report. This year, the report introduces more original data than any previous edition, including a new chapter on AI public opinion, a…