1 citations · 1 across the 10 of their papers we have counts for
10 papers
Function-to-Style Guidance of LLMs for Code Translation
Longhui Zhang, Bin Wang, Jiahao Wang +7
Large language models (LLMs) have made significant strides in code translation tasks. However, ensuring both the correctness and readability of translated code remains a challenge,…
Optimizing Speech Multi-View Feature Fusion through Conditional Computation
Weiqiao Shan, Yuhao Zhang, Yuchen Han +7
Recent advancements have highlighted the efficacy of self-supervised learning (SSL) features in various speech-related tasks, providing lightweight and versatile multi-view speech…
Investigating Numerical Translation with Large Language Models
Wei Tang, Jiawei Yu, Yuang Li +5
The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made…
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
Jiawei Yu, Xiang Geng, Yuang Li +8
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, ann…
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Jiawei Yu, Yuang Li, Xiaosong Qiao +6
Text-to-speech (TTS) models have been widely adopted to enhance automatic speech recognition (ASR) systems using text-only corpora, thereby reducing the cost of labeling real speec…
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Yuang Li, Xiaosong Qiao, Xiaofeng Zhao +4
Large language models can enhance automatic speech recognition systems through generative error correction. In this paper, we propose Pinyin-enhanced GEC, which leverages Pinyi, th…
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators
Xinglin Lyu, Junhui Li, Yanqing Zhao +4
Generally, the decoder-only large language models (LLMs) are adapted to context-aware neural machine translation (NMT) in a concatenating way, where LLMs take the concatenation of…
Using Large Language Model for End-to-End Chinese ASR and NER
Yuang Li, Jiawei Yu, Min Zhang +6
Mapping speech tokens to the same feature space as text tokens has become the paradigm for the integration of speech modality into decoder-only large language models (LLMs). An alt…
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
Yilun Liu, Shimin Tao, Xiaofeng Zhao +11
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions. The quality of instruction pairs used for tuning greatly affects the…
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
Yuang Li, Min Zhang, Chang Su +7
The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not freque…