Papers by Zeyang Liu
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
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| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)
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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin
| Challenge: | Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs. |
| Approach: | They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony. |
| Outcome: | The proposed model performs poorly on Flames, particularly in safety and fairness dimensions. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
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| Challenge: | a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots. |
| Approach: | They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains . |
| Outcome: | The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively. |
Conditional Semantic Textual Similarity via Conditional Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods to assess similarity between sentences encounter over-estimation problem . compared to fuzzy representations, similarity is comparatively lower in terms of "The person's age". |
| Approach: | They propose a conditional contrastive learning framework that constructs positive and negative samples from two perspectives. |
| Outcome: | The proposed method achieves state-of-the-art performance with five models based on bi-encoder and tri-encoding architectures. |
E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)
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| Challenge: | Existing factuality verification methods follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency. |
| Approach: | They propose a Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space. |
| Outcome: | The proposed paradigm shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space . |
Improving Efficiency in Large Language Models via Extendable Block Floating Point Representation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are becoming more and more resource-intensive as their size increases. |
| Approach: | They propose a block floating-point (BFP) arithmetic representation that extends the exponent bit width to capture a wider dynamic range. |
| Outcome: | Extendable Exponent Sharing (EES) outperforms representative baselines in accuracy and computational efficiency. |