Papers by Huan Lin
Rationalizing Medical Relation Prediction from Corpus-level Statistics (2020.acl-main)
Copied to clipboard
| Challenge: | Existing work on predicting relations based on text corpus has focused on analyzing raw texts mentioning two entities. |
| Approach: | They propose a framework that can be used to rationalize medical relation prediction . they recall contexts associated with the target entities and recognize relational interactions between them . |
| Outcome: | The proposed framework can achieve competitive predictive performance against a comprehensive list of neural baseline models, and present rationales to justify its prediction. |
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)
Copied to clipboard
Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang
| Challenge: | Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus. |
| Approach: | They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval. |
| Outcome: | The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks. |
T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data. |
| Approach: | They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies. |
| Outcome: | The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks. |
LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Approach: | They propose to use large language models to test their security against jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Outcome: | The proposed model is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. |
WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document . |
| Approach: | They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document. |
| Outcome: | The proposed model over-estimates tokens and makes it well-calibrated on common datasets. |
Defending Against Social Engineering Attacks in the Age of LLMs (2024.emnlp-main)
Copied to clipboard
Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee, Zizhou Liu, Zheng Hui, Michael Davinroy, James Cook, Laura Cassani, Kirill Trapeznikov, Matthias Kirchner, Arslan Basharat, Anthony Hoogs, Joshua Garland, Huan Liu, Julia Hirschberg
| Challenge: | Existing research has developed frameworks to understand human-to-human CSE attacks. |
| Approach: | They propose a modular defense pipeline that improves detection at both the message and conversation levels. |
| Outcome: | The proposed model can be exploited to facilitate chat-based social engineering attacks and generate high-quality CSE content, but their detection capabilities are suboptimal, leading to increased operational costs for defense. |
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)
Copied to clipboard
Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang
| Challenge: | Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses. |
| Approach: | They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses. |
| Outcome: | The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
Copied to clipboard
Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval (2021.emnlp-main)
Copied to clipboard
| Challenge: | 16K FAQ items scraped from 55 credible websites . 32 human-annotated FAQ items for each query. |
| Approach: | They present a large, challenging dataset for FAQ retrieval for COVID-19 . they use a FAQ bank, Query Bank and Relevance Set to evaluate the dataset . |
| Outcome: | The proposed model achieves 48.8 under P@5 and is compared with other datasets. |
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
Copied to clipboard
Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing cross-lingual transfer are limited by parallel resources and lack linguistic and domain coverage. |
| Approach: | They propose a cross-lingual in-context pre-training approach that leverages semantically related bilingual Wikipedia documents to enhance cross-linguistic transfer. |
| Outcome: | The proposed approach improves multilingual performance on three models across six target languages. |
LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)
Copied to clipboard
Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan
| Challenge: | Existing LLMs are difficult to achieve satisfactory results in table-related tasks. |
| Approach: | They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks. |
| Outcome: | The proposed model achieves state-of-the-art on a Logic2Text dataset. |
Towards User-Driven Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | a good translation should implicitly mirror user traits rather than translate the original content semantically. |
| Approach: | They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion . |
| Outcome: | The proposed framework can capture user traits from historical inputs under zero-shot learning fashion. |