131 citations · 319 across the 29 of their papers we have counts for
8 papers · 1 filter
Turning Speech Language Models into Multilingual Listeners
Tolúlopé Ògúnrèmí, Dan Jurafsky, Chris Manning +2
Speech Language Models (SLMs) that understand spoken language questions support only a few high-resource languages, limiting access to millions of people worldwide. This gap stems…
LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions
Myra Cheng, Lujain Ibrahim, Grace Liu +5
We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develo…
string2string Studio: An Interactive, In-Browser Platform for String-to-String Algorithms
Mirac Suzgun, James Zou, Stuart M. Shieber +1
We present string2string Studio, an interactive in-browser platform for string-to-string analysis across natural language processing, computational biology, and the digital humanit…
You Talkin to Me?: A Network Analysis of Gendered Speaker-Addressee Patterns in Film Screenplays
Samin Khan, Camilla Griffiths, Shrikanth Narayanan +2
Objective: This paper investigates the gendered structure of speaker addressee relationships in film dialogue, asking not merely who speaks, but who is spoken to and how conversati…
Algorithmic Monocultures in Hiring
Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel +2
Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the same individuals and members…
Verbalizing LLMs' assumptions to explain and control sycophancy
Myra Cheng, Isabel Sieh, Humishka Zope +7
LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing genuine assessment. We hypothesize that this behavior aris…
Beyond Tokens: Concept-Level Training Objectives for LLMs
Laya Iyer, Pranav Somani, Alice Guo +2
The next-token prediction (NTP) objective has been foundational in the development of modern large language models (LLMs), driving advances in fluency and generalization. However,…
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
Myra Cheng, Robert D. Hawkins, Dan Jurafsky
Large language models (LLMs) frequently fail to challenge users' harmful beliefs in domains ranging from medical advice to social reasoning. We argue that these failures can be und…