4 citations · 17 across the 14 of their papers we have counts for
15 papers
Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
Yiyou Sun, Yu Gai, Lijie Chen +3
Large language models (LLMs) frequently generate hallucinations-content that deviates from factual accuracy or provided context-posing challenges for diagnosis due to the complex i…
Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen +6
High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data…
HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
Abhilasha Ravichander, Shrusti Ghela, David Wadden +1
Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: statements that are misaligned with…
RESTOR: Knowledge Recovery in Machine Unlearning
Keivan Rezaei, Khyathi Chandu, Soheil Feizi +3
Large language models trained on web-scale corpora can memorize undesirable data containing misinformation, copyrighted material, or private or sensitive information. Recently, sev…
Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement
Jaehun Jung, Faeze Brahman, Yejin Choi
We present a principled approach to provide LLM-based evaluation with a rigorous guarantee of human agreement. We first propose that a reliable evaluation method should not uncriti…
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
Jaeyoung Lee, Ximing Lu, Jack Hessel +5
Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims. Larg…
WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences
Yujie Lu, Dongfu Jiang, Wenhu Chen +3
Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions. To address this gap, we launc…
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
Yutaro Yamada, Khyathi Chandu, Yuchen Lin +3
Diffusion-based image generation models such as DALL-E 3 and Stable Diffusion-XL demonstrate remarkable capabilities in generating images with realistic and unique compositions. Ye…
Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Siyuan Wang, Zhongyu Wei, Yejin Choi +1
Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls shor…
STEER: Unified Style Transfer with Expert Reinforcement
Skyler Hallinan, Faeze Brahman, Ximing Lu +3
While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setti…
MacGyver: Are Large Language Models Creative Problem Solvers?
Yufei Tian, Abhilasha Ravichander, Lianhui Qin +6
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting o…
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Wenting Zhao, Justin T Chiu, Jena D. Hwang +6
Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences a…
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
Huihan Li, Yuting Ning, Zeyi Liao +7
To effectively use large language models (LLMs) for real-world queries, it is imperative that they generalize to the long-tail distribution, i.e. rare examples where models exhibit…
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
Da Yin, Faeze Brahman, Abhilasha Ravichander +4
Closed-source agents suffer from several issues such as a lack of affordability, transparency, and reproducibility, particularly on complex interactive tasks. This motivates the de…
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin +7
Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrati…