9 papers
Voice "Cloning" is Style Transfer
Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds +3
Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completi…
Greater accessibility can amplify discrimination in generative AI
Carolin Holtermann, Minh Duc Bui, Kaitlyn Zhou +3
Hundreds of millions of people rely on large language models (LLMs) for education, work, and even healthcare. Yet these models are known to reproduce and amplify social biases pres…
"Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most
Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi +1
Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study…
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
Yongchan Kwon, Shang Zhu, Federico Bianchi +2
The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in…
Attention to Non-Adopters
Kaitlyn Zhou, Kristina GligoriÄ, Myra Cheng +7
Although language model-based chat systems are increasingly used in daily life, most Americans remain non-adopters of chat-based LLMs -- as of June 2025, 66% had never used ChatGPT…
Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs
Jeffrey Basoah, Daniel Chechelnitsky, Tao Long +5
As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles o…