Papers by Hongying Liu
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
FLASH: Focused Layer Attention Sink Hijacking (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) remain vulnerable to jailbreaking attacks despite advances in safety alignment . |
| Approach: | They propose a new diagnostic auditing framework that dismantles the model's internal safety anchor by precisely scaling attention scores in these vulnerable layers. |
| Outcome: | The proposed framework achieves a state-of-the-art Attack Success Rate of over 77% with an unprecedented efficiency of 1.53 queries on average. |
Task-aware Contrastive Mixture of Experts for Quadruple Extraction in Conversations with Code-like Replies and Non-opinion Detection (2025.emnlp-main)
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| Challenge: | Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure. |
| Approach: | They propose a novel LLM-based multi-task approach to extract sentiment quadruples from conversations by integrating expert-level contrastive loss within task-oriented mixture of experts layer. |
| Outcome: | The proposed method outperforms existing fine-tuning techniques in terms of accuracy and computational efficiency. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
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| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning for Emotion Recognition in Conversations (2025.coling-main)
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| Challenge: | Existing ERC methods fail to handle emotional cues from both visual sources and discourse structures due to the complexity of visual scenes and contextual dependencies in conversations. |
| Approach: | They propose a framework for Emotion Recognition in conversations that utilizes multi-task instruction tuning to enhance the model's understanding of multi-modal dialogue scenes. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on three benchmark ERC datasets and is based on a video-language connector and a chain-of thought strategy. |