41 citations · 174 across the 34 of their papers we have counts for
6 papers · 1 filter
The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung +5
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination…
Revisiting the Past: Data Unlearning with Model State History
Keivan Rezaei, Mehrdad Saberi, Abhilasha Ravichander +1
Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, or data that degrades model perfo…
What Has Been Lost with Synthetic Evaluation?
Alexander Gill, Abhilasha Ravichander, Ana Marasović
Large language models (LLMs) are increasingly used for data generation. However, creating evaluation benchmarks raises the bar for this emerging paradigm. Benchmarks must target sp…
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…