55 citations · 178 across the 19 of their papers we have counts for
13 papers · 1 filter
Demystifying Verbatim Memorization in Large Language Models
Jing Huang, Diyi Yang, Christopher Potts
Large Language Models (LLMs) frequently memorize long sequences verbatim, often with serious legal and privacy implications. Much prior work has studied such verbatim memorization…
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
Dilara Soylu, Christopher Potts, Omar Khattab
Natural Language Processing (NLP) systems are increasingly taking the form of sophisticated modular pipelines, e.g., Retrieval Augmented Generation (RAG), where each module may inv…
Mapping the Increasing Use of LLMs in Scientific Papers
Weixin Liang, Yaohui Zhang, Zhengxuan Wu +11
Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledg…
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora, Dan Jurafsky, Christopher Potts
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons).…
In-Context Learning for Extreme Multi-Label Classification
Karel D'Oosterlinck, Omar Khattab, François Remy +3
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precis…
Flexible Model Interpretability through Natural Language Model Editing
Karel D'Oosterlinck, Thomas Demeester, Chris Develder +1
Model interpretability and model editing are crucial goals in the age of large language models. Interestingly, there exists a link between these two goals: if a method is able to s…