4 citations · 17 across the 14 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…