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cs.CL2024

Distilling an End-to-End Voice Assistant Without Instruction Training Data

William Held, Ella Li, Michael Ryan +3

Voice assistants, such as Siri and Google Assistant, typically model audio and text separately, resulting in lost speech information and increased complexity. Recent efforts to add…

cs.CL2024

Unintended Impacts of LLM Alignment on Global Representation

Michael J. Ryan, William Held, Diyi Yang

Before being deployed for user-facing applications, developers align Large Language Models (LLMs) to user preferences through a variety of procedures, such as Reinforcement Learnin…

cs.CL2023

Revisiting non-English Text Simplification: A Unified Multilingual Benchmark

Michael J. Ryan, Tarek Naous, Wei Xu

Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been don…

cs.CL2023

ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment

Tarek Naous, Michael J. Ryan, Anton Lavrouk +2

We present a comprehensive evaluation of large language models for multilingual readability assessment. Existing evaluation resources lack domain and language diversity, limiting t…

cs.CL2023

Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

Tarek Naous, Michael J. Ryan, Alan Ritter +1

As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial. Despite advancements in multilingual capabilities…