984 citations · 1.7k across the 12 of their papers we have counts for
Showing 2023 · cs.CLShow all
3 papers · 2 filters
cs.CL2023
The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP
Julian Michael
I propose a paradigm for scientific progress in NLP centered around developing scalable, data-driven theories of linguistic structure. The idea is to collect data in tightly scoped…
cs.CL2023★ 90 cited
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
Miles Turpin, Julian Michael, Ethan Perez +1
Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought re…
cs.CL2023★ 3 cited
We're Afraid Language Models Aren't Modeling Ambiguity
Alisa Liu, Zhaofeng Wu, Julian Michael +6
Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators…