4 citations · 6 across the 9 of their papers we have counts for
10 papers
LLMs in the Real World: Evaluating "AI" in Emergency Contexts
Sara Court, Lara Downing, Micha Elsner
This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the public. To illustrate the sta…
Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance
Jingyi Chen, Zhimeng Guo, Jiyun Chun +3
Understanding emotion from speech requires sensitivity to both lexical and acoustic cues. However, it remains unclear whether large audio language models (LALMs) genuinely process…
Fine-Tuning Text-to-Speech Diffusion Models Using Reinforcement Learning with Human Feedback
Jingyi Chen, Ju Seung Byun, Micha Elsner +2
Diffusion models produce high-fidelity speech but are inefficient for real-time use due to long denoising steps and challenges in modeling intonation and rhythm. To improve this, w…
Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages
Micha Elsner, David Liu
Partly automated creation of interlinear glossed text (IGT) has the potential to assist in linguistic documentation. We argue that LLMs can make this process more accessible to lin…
Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem
Sara Court, Micha Elsner
This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resou…
DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models
Jingyi Chen, Ju-Seung Byun, Micha Elsner +1
Recent advancements in generative models have sparked a significant interest within the machine learning community. Particularly, diffusion models have demonstrated remarkable capa…
Exploring How Generative Adversarial Networks Learn Phonological Representations
Jingyi Chen, Micha Elsner
This paper explores how Generative Adversarial Networks (GANs) learn representations of phonological phenomena. We analyze how GANs encode contrastive and non-contrastive nasality…
Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach
Moniba Keymanesh, Micha Elsner, Srinivasan Parthasarathy
Existing work on making privacy policies accessible has explored new presentation forms such as color-coding based on the risk factors or summarization to assist users with conscio…
Fairness-aware Summarization for Justified Decision-Making
Moniba Keymanesh, Tanya Berger-Wolf, Micha Elsner +1
In consequential domains such as recidivism prediction, facility inspection, and benefit assignment, it's important for individuals to know the decision-relevant information for th…
The Paradigm Discovery Problem
Alexander Erdmann, Micha Elsner, Shijie Wu +2
This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evalua…