Papers by Yi-Cheng Lin
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)
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Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander Liu, Hung-yi Lee
| Challenge: | Researchers have developed a sound codec that can be used as tokenizers for preserving audio data and minimizing data transmission latency. |
| Approach: | They propose to use codec-SUPERB to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge. |
| Outcome: | The proposed codec-SUPERB model is evaluated on selected experimental settings. |
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition (2024.lrec-main)
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| Challenge: | End-to-end automatic speech recognition systems suffer from mistranscription of domain-specific phrases, such as named entities. |
| Approach: | They propose a named entity correction model that leverages phonetic con-fusion to mitigate phonetic confusion. |
| Outcome: | The proposed model outperforms the existing model on AISHELL-1 and Homophone datasets. |
On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation (2026.findings-acl)
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Chan-Jan Hsu, Liang-Hsuan Tseng, Yi-Cheng Lin, Yen-Chun Kuo, Ju-Chieh Chou, Kai-Wei Chang, Hung-yi Lee, Carlos Busso
| Challenge: | Generative spoken language models are often evaluated using global token perplexity, which overlooks fundamental differences between speech and text modalities. |
| Approach: | They propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity. |
| Outcome: | The proposed evaluations more faithfully reflect perceived generation quality, as evidenced by stronger correlations with human-rated mean opinion scores (MOS). |
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition (2026.findings-acl)
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| Challenge: | ASR models can be used to correct accent-specific errors without ground truth . pseudo-labels inherit the teacher model's systematic biases, authors say . |
| Approach: | They propose a parameter-space correction technique that captures pseudo-label biases . they propose achieving up to 35% relative WER reduction on a pseudo-labeled target model . |
| Outcome: | The proposed model achieves 35% relative WER reduction on ten African accents with the Whisper tiny model. |
MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation (2026.acl-long)
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| Challenge: | Existing RAG solutions for large language models are limited by context windows limiting their ability to process long-form, domain-specific content. |
| Approach: | They propose a multimodal knowledge graph-based RAG that enables cross-modal reasoning . their method incorporates visual cues into the construction of knowledge graphs, retrieval phase, and answer generation process . |
| Outcome: | Experimental results show that the proposed approach outperforms existing approaches on textual and multimodal benchmarks. |
Creativity in LLM-based Multi-Agent Systems: A Survey (2025.emnlp-main)
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Yi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu, Tzu-Hsuan Wu, Kuan-Yu Chen, Hung-yi Lee, Yun-Nung Chen
| Challenge: | Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. |
| Approach: | They present a taxonomy of agent proactivity and persona design and an overview of generation techniques. |
| Outcome: | The proposed framework and roadmap offers a roadmap for advancing the development, evaluation, and standardization of creative MAS. |