23 citations · 51 across the 11 of their papers we have counts for
11 papers
Warning labels shift perceptions of sycophantic AI, but not its influence
Lujain Ibrahim, Myra Cheng, Cinoo Lee +4
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to wa…
Cognitive offloading and the speedup illusion in human-AI interaction
Sunny Yu, Myra Cheng, Ahmad Jabbar +4
Large language models (LLMs) have the potential to boost human productivity by speeding up task completion -- provided users know when to offload cognitive work to them. But we do…
Evaluating Commercial AI Chatbots as News Intermediaries
Mirac Suzgun, Emily Shen, Federico Bianchi +5
AI chatbots are rapidly shaping how people encounter the news, yet no prior study has systematically measured how accurately these systems, with their proprietary search integratio…
The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
Sunny Yu, Myra Cheng, Ahmad Jabbar +4
People are increasingly turning to AI assistance for simple tasks, e.g., arithmetic, spell-check, and answering simple questions. But does AI assistance actually save users time an…
Advancing Science- and Evidence-based AI Policy
Rishi Bommasani, Sanjeev Arora, Jennifer Chayes +17
AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium o…
Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi +2
Despite their impressive performance on complex tasks, current language models (LMs) typically operate in a vacuum: Each input query is processed separately, without retaining insi…
Rethinking Word Similarity: Semantic Similarity through Classification Confusion
Kaitlyn Zhou, Haishan Gao, Sarah Chen +3
Word similarity has many applications to social science and cultural analytics tasks like measuring meaning change over time and making sense of contested terms. Yet traditional si…
Can Unconfident LLM Annotations Be Used for Confident Conclusions?
Kristina Gligorić, Tijana Zrnic, Cinoo Lee +2
Large language models (LLMs) have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. In comp…
h4rm3l: A language for Composable Jailbreak Attack Synthesis
Moussa Koulako Bala Doumbouya, Ananjan Nandi, Gabriel Poesia +5
Despite their demonstrated valuable capabilities, state-of-the-art (SOTA) widely deployed large language models (LLMs) still have the potential to cause harm to society due to the…
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
Federico Bianchi, Patrick John Chia, Mert Yuksekgonul +3
Negotiation is the basis of social interactions; humans negotiate everything from the price of cars to how to share common resources. With rapidly growing interest in using large l…
KTO: Model Alignment as Prospect Theoretic Optimization
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff +2
Kahneman & Tversky's tells us that humans perceive random variables in a biased but well-defined manner (1992); for example, humans are famously loss-ave…