4 citations · 6 across the 9 of their papers we have counts for
9 papers
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur +1
Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increas…
Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?
Yurii Halychanskyi, Nimet Beyza Bozdag, Mark Hasegawa-Johnson +2
Synthetic accented speech is a promising way to improve automatic speech recognition (ASR) when real accented recordings are scarce. We ask what makes such data useful for ASR fine…
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah +5
Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in singl…
DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference
Parisa Rabbani, Priyam Sahoo, Ruben Mathew +4
LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims dif…
From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue Systems
Parisa Rabbani, Nimet Beyza Bozdag, Dilek Hakkani-Tür
LLMs are increasingly employed as judges across a variety of tasks, including those involving everyday social interactions. Yet, it remains unclear whether such LLM-judges can reli…
Language Specific Knowledge: Do Models Know Better in X than in English?
Ishika Agarwal, Nimet Beyza Bozdag, Nisval Patel +1
Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a n…