261 citations · 743 across the 25 of their papers we have counts for
12 papers · 1 filter
STEER: Assessing the Economic Rationality of Large Language Models
Narun Raman, Taylor Lundy, Samuel Amouyal +3
There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it…
Tradeoffs Between Alignment and Helpfulness in Language Models with Steering Methods
Yotam Wolf, Noam Wies, Dorin Shteyman +3
Language model alignment has become an important component of AI safety, allowing safe interactions between humans and language models, by enhancing desired behaviors and inhibitin…
Generating Benchmarks for Factuality Evaluation of Language Models
Dor Muhlgay, Ori Ram, Inbal Magar +7
Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing methods f…
Fundamental Limitations of Alignment in Large Language Models
Yotam Wolf, Noam Wies, Oshri Avnery +2
An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by…
The Learnability of In-Context Learning
Noam Wies, Yoav Levine, Amnon Shashua
In-context learning is a surprising and important phenomenon that emerged when modern language models were scaled to billions of learned parameters. Without modifying a large langu…
In-Context Retrieval-Augmented Language Models
Ori Ram, Yoav Levine, Itay Dalmedigos +4
Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significa…