2 citations · 2 across the 6 of their papers we have counts for
6 papers
Do LLMs Benefit From Their Own Words?
Jenny Y. Huang, Leshem Choshen, Wei Sun +5
In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant responses. We revisit this design choi…
LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users
Almog Hilel, Riddhi Bhagwat, Idan Shenfeld +2
We describe a vulnerability in language models (LMs) trained with user feedback, whereby a single user can persistently alter LM knowledge and behavior given only the ability to pr…
Can Gradient Descent Simulate Prompting?
Eric Zhang, Leshem Choshen, Jacob Andreas
There are two primary ways of incorporating new information into a language model (LM): changing its prompt or changing its parameters, e.g. via fine-tuning. Parameter updates incu…
Unforgettable Generalization in Language Models
Eric Zhang, Leshem Choshen, Jacob Andreas
When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have bee…
Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models
Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin +17
The ability to build and reason about models of the world is essential for situated language understanding. But evaluating world modeling capabilities in modern AI systems -- espec…
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Afra Feyza Akyürek, Ekin Akyürek, Leshem Choshen +2
While language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims, these generally do not reflect a globally coherent, manipu…