no code implementations • SLPAT (ACL) 2022 • Shachi H. Kumar, Hsuan Su, Ramesh Manuvinakurike, Max Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle.
no code implementations • ACL 2022 • Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
Intelligent conversational assistants have become an integral part of our lives for performing simple tasks.
no code implementations • 3 Feb 2025 • Yu-Ling Hsu, Hsuan Su, Shang-Tse Chen
Large language models (LLMs) have seen rapid development in recent years, revolutionizing various applications and significantly enhancing convenience and productivity.
no code implementations • 27 Dec 2024 • Hua Farn, Hsuan Su, Shachi H Kumar, Saurav Sahay, Shang-Tse Chen, Hung-Yi Lee
In this paper, we address the question: How can we improve downstream task performance while preserving safety in LLMs without relying on additional safety data?
no code implementations • 7 Aug 2024 • Shachi H Kumar, Saurav Sahay, Sahisnu Mazumder, Eda Okur, Ramesh Manuvinakurike, Nicole Beckage, Hsuan Su, Hung-Yi Lee, Lama Nachman
However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text.
no code implementations • 5 Jun 2024 • Hsuan Su, Hua Farn, Fan-Yun Sun, Shang-Tse Chen, Hung-Yi Lee
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
no code implementations • 11 Nov 2023 • Hsuan Su, Rebecca Qian, Chinnadhurai Sankar, Shahin Shayandeh, Shang-Tse Chen, Hung-Yi Lee, Daniel M. Bikel
In this paper, we propose a diagnosis method to attribute bias to each component of a TOD system.
no code implementations • 17 Oct 2023 • Hsuan Su, Cheng-Chu Cheng, Hua Farn, Shachi H Kumar, Saurav Sahay, Shang-Tse Chen, Hung-Yi Lee
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4.
no code implementations • 18 Sep 2023 • Hsuan Su, Ting-yao Hu, Hema Swetha Koppula, Raviteja Vemulapalli, Jen-Hao Rick Chang, Karren Yang, Gautam Varma Mantena, Oncel Tuzel
In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
no code implementations • 12 Feb 2023 • Hsuan Su, Shachi H Kumar, Sahisnu Mazumder, Wenda Chen, Ramesh Manuvinakurike, Eda Okur, Saurav Sahay, Lama Nachman, Shang-Tse Chen, Hung-Yi Lee
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
no code implementations • 8 Jun 2022 • Hsuan Su, PoHan Chi, Shih-Cheng Huang, Chung Ho Lam, Saurav Sahay, Shang-Tse Chen, Hung-Yi Lee
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model.
no code implementations • 4 Dec 2021 • Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Saurav Sahay, Lama Nachman
We build models that can suggest relevant cues in the dialog response context which is used to control response generation and can speed up communication.
no code implementations • NAACL 2021 • Hsuan Su, Jiun-Hao Jhan, Fan-Yun Sun, Saurav Sahay, Hung-Yi Lee
Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans.