3 citations · 4 across the 6 of their papers we have counts for
7 papers · 1 filter
XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
Akhila Yerukola, Jena D. Hwang, Mingqian Zheng +5
When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-prob…
Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences
Mingqian Zheng, Wenjia Hu, Patrick Zhao +5
Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience…
Tulu 3: Pushing Frontiers in Open Language Model Post-Training
Nathan Lambert, Jacob Morrison, Valentina Pyatkin +20
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag…
Diverging Preferences: When do Annotators Disagree and do Models Know?
Michael JQ Zhang, Zhilin Wang, Jena D. Hwang +6
We examine diverging preferences in human-labeled preference datasets. We develop a taxonomy of disagreement sources spanning ten categories across four high-level classes and find…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +1
As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertainties in downstream applications. In this w…