2 citations · 5 across the 12 of their papers we have counts for
12 papers
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking
Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou +5
Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction that consume tokens without im…
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition
Yi-Cheng Lin, Yu-Hsuan Li Liang, Hsuan Su +4
Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although pseudo-labeling offers a practical work…
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
Hua Farn, Hsuan Su, Shachi H Kumar +3
Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of originally aligned models. While some existing…
Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models
Shachi H Kumar, Saurav Sahay, Sahisnu Mazumder +6
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are sus…
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition
Hsuan Su, Hua Farn, Fan-Yun Sun +2
Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, e…
Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems
Hsuan Su, Rebecca Qian, Chinnadhurai Sankar +4
Recent works have shown considerable improvements in task-oriented dialogue (TOD) systems by utilizing pretrained large language models (LLMs) in an end-to-end manner. However, the…
Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models
Hsuan Su, Cheng-Chu Cheng, Hua Farn +4
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots…
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +5
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data…
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
Hsuan Su, Shachi H Kumar, Sahisnu Mazumder +7
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of th…
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning
Hsuan Su, Pohan Chi, Shih-Cheng Huang +4
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steeri…
Controllable Response Generation for Assistive Use-cases
Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike +2
Conversational agents have become an integral part of the general population for simple task enabling situations. However, these systems are yet to have any social impact on the di…
Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention
Hsuan Su, Jiun-Hao Jhan, Fan-yun Sun +2
Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into wha…