Papers by Zhen Yan
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)
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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister
| Challenge: | Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations. |
| Approach: | They propose a mechanism that integrates forward- and backward-looking reflections into a personalized memory bank for effective future retrieval. |
| Outcome: | The proposed mechanism outperforms state-of-the-art benchmarks on a long-term dialogue memory model. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
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Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)
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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai
| Challenge: | RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images . |
| Approach: | They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a . |
| Outcome: | The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images. |
Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)
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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He
| Challenge: | Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps. |
| Approach: | They propose a method to identify critical reasoning steps using perplexity as a measure of their importance. |
| Outcome: | The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT. |
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels (2024.naacl-short)
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| Challenge: | Existing pointwise LLMs provide noisy or biased answers for documents that are partially relevant to the query. |
| Approach: | They propose to incorporate fine-grained relevance labels into the LLM prompt . they propose to better differentiate between documents with different levels of relevance . |
| Outcome: | The proposed model can differentiate between documents with different levels of relevance to the query and derive a more accurate ranking. |
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)
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| Challenge: | Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels. |
| Approach: | They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences . |
| Outcome: | The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences . |
Rethinking the Word-level Quality Estimation for Machine Translation from Human Judgement (2023.findings-acl)
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| Challenge: | Word-level Quality Estimation (QE) of Machine Translation aims to detect potential translation errors in the translated sentence without reference. |
| Approach: | They propose to use a human-generated translation judgment to generate a word-level quality estimate (QE) using a translation error rate toolkit to detect translation errors without reference. |
| Outcome: | The proposed dataset is more consistent with human judgment and confirms the effectiveness of the proposed tag-correcting strategies. |
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024.findings-naacl)
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Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
| Challenge: | Existing methods to rank documents using large language models do not understand these challenging ranking formulations. |
| Approach: | They propose to use Pairwise Ranking Prompting to improve ranking performance . they propose to outperform fine-tuned baseline rankers on benchmark datasets . |
| Outcome: | The proposed technique outperforms supervised baselines on benchmark datasets and outperformed other LLM-based solutions by over 10% on average. |
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels (D19-1)
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| Challenge: | Using BiPaR, we build monolingual, multilingual and cross-lingual MRC on novels. |
| Approach: | They propose a bilingual parallel novel-style machine reading comprehension dataset BiPaR . they collect 3,667 bilingual parallel paragraphs from Chinese and English novels . |
| Outcome: | The proposed dataset supports multilingual and cross-lingual reading comprehension. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
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Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |