Papers by Kuo Lin
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)
Copied to clipboard
| Challenge: | Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. |
| Approach: | They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references. |
| Outcome: | The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR). |
A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)
Copied to clipboard
Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin
| Challenge: | Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities. |
| Approach: | They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning. |
| Outcome: | The proposed model achieves superior performance and strong practical value in an industrial search engine. |
AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)
Copied to clipboard
Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang
| Challenge: | Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs. |
| Approach: | They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure. |
| Outcome: | The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic. |
On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation (2026.findings-acl)
Copied to clipboard
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). |
Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks (2025.findings-emnlp)
Copied to clipboard
Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao, Hou-I Liu, Ya-Hsin Yeh, Yu-Kai Chan, Wen-Sheng Lien, Po-Yen Kuo, Philip S. Yu, Hong-Han Shuai
| Challenge: | Large language models (LLMs) are used to review academic papers, but are susceptible to textual adversarial attacks. |
| Approach: | They evaluate the robustness of large language models as automated reviewers in the presence of adversarial attacks. |
| Outcome: | The proposed model is robust against textual adversarial attacks, the authors argue . their findings highlight the importance of addressing adversarials to ensure integrity of scholarly communication. |
An Exploration of Mamba for Speech Self-Supervised Models (2026.acl-long)
Copied to clipboard
Tzu-Quan Lin, Heng-Cheng Kuo, Tzu-Chieh Wei, Hsi-Chun Cheng, Chun Wei Chen, Hsien-Fu Hsiao, Yu Tsao, Hung-yi Lee
| Challenge: | Mamba-based SSL models are promising for long-sequence modeling, speech unit extraction, and speech self-supervised learning. |
| Approach: | They propose to use Mamba-based HuBERT models as an alternative to Transformer-based SSL architectures. |
| Outcome: | The proposed models outperform Transformer-based models in language modeling tasks while showing superior performance on streaming ASR. |