Papers by Zhihao Yang
Learning from Near-Misses: Error-Aware Contrastive Few-Shot Learning for NL2Formula (2026.acl-long)
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| Challenge: | Existing spreadsheet formulas often produce near-miss outputs due to an incorrect function, operator, or reference. |
| Approach: | They propose an abstract syntax tree-based error taxonomy that organizes common error modes by the kind of decision that goes wrong in the parse tree. |
| Outcome: | The proposed framework improves Exact Match (EM) by 6.4 points over supervised fine-tuning and matches self-consistency (SC@5) accuracy. |
Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)
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| Challenge: | YManga dataset is the first specifically designed for yonkoma manga understanding . |
| Approach: | They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels. |
| Outcome: | The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations. |
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)
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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified. |
| Approach: | They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
| Outcome: | Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)
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| Challenge: | Existing pipelines for relational triple extraction are underutilizing regional information of triple. |
| Approach: | They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection . |
| Outcome: | The proposed framework could extract all types of triples on two widely used datasets. |
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)
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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
| Challenge: | Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. |
| Approach: | They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions. |
| Outcome: | The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses. |
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)
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| Challenge: | Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online . |
| Approach: | They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain . |
| Outcome: | The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework (2025.emnlp-main)
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| Challenge: | Existing methods for detecting pre-training data in large language models rely on superficial features like prediction confidence and loss, resulting in mediocre performance. |
| Approach: | They propose a new algorithm to analyze neuron activation patterns between training and non-training data in large language models to improve their performance. |
| Outcome: | The proposed algorithm outperforms existing methods across three benchmarks and multiple LLMs. |
Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)
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| Challenge: | Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples. |
| Approach: | They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus . |
| Outcome: | Experimental results show that the proposed approach outperforms baseline models on few-shot tasks. |
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks. |
| Approach: | They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités. |
| Outcome: | The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them. |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Tool-integrated reasoning (TIR) enables large language models to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse. |
| Approach: | They propose an algorithm that uses a composite reward to model tool costs and tool efficiency. |
| Outcome: | The proposed algorithm models heterogeneous tool costs and encourages more cost-effective tool-use strategies. |
DAGCN: Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| Challenge: | Recent advances in sentiment analysis tend to interference from local factors such as irrelevant words and edges, hindering the precise identification of opinion words. |
| Approach: | They propose a distance-based syntactic weight and Aspect-Fusion Attention to solve this problem. |
| Outcome: | The proposed model outperforms state-of-the-art models on three public datasets and verify its effectiveness. |
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)
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| Challenge: | Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks. |
| Approach: | They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics. |
Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
| Approach: | They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. |
| Outcome: | Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition. |
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities. |
| Approach: | They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese. |
| Outcome: | The proposed model can learn the interaction of bilinguals and dependent information within Chinese. |
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task (D19-57)
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| Challenge: | Existing methods for natural language processing are labor-intensive and skill-dependent . Currently, most biomedical natural language tasks focus on English documents . |
| Approach: | They introduce a BERT benchmark to facilitate the research of PharmaCoNER task . they evaluate two baselines based on Multilingual BERT and BioBERT on the corpus . |
| Outcome: | The proposed task is based on multilingual BERT and BioBERT on the PharmaCoNER corpus. |
ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification (2023.findings-eacl)
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Wangjie Jiang, Zhihao Ye, Bang Liu, Ruihui Zhao, Jianguang Zheng, Mengyao Li, Zhiyong Li, Yujiu Yang, Yefeng Zheng
| Challenge: | Existing methods for relation classification suffer from the scarcity of manually annotated data. |
| Approach: | They propose a novel relation classification model that incorporates query representation into the encoding of novel prototypes and utilizes iteratively to achieve more interaction. |
| Outcome: | The proposed model outperforms the state-of-the-art model on two benchmark datasets. |
Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on the repair generation capability of LLMs, lacking fine-grained evaluation of reflection. |
| Approach: | They propose a benchmark with oracle reflections and a dual-task protocol to decouple evaluation of reflection from repair. |
| Outcome: | The proposed benchmarks show that underperforming reflection capabilities remain a bottleneck for code repair. |
PAM: Enhancing General Alignment of Large Reasoning Models through Priority-Aware Metacognition (2026.acl-long)
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| Challenge: | Existing studies indicate that System-2 thinking alone does not transfer to the general alignment domain. |
| Approach: | They propose to use priority-aware metacognition to help LRMs understand human preferences and monitor and regulate their thinking process. |
| Outcome: | The proposed model improves general alignment performance by 10 points on helpfulness and harmless benchmarks. |
SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)
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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang
| Challenge: | Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors . |
| Approach: | They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details. |
| Outcome: | The proposed method improves accuracy, inference efficiency, and real-time processing capabilities. |
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
| Approach: | They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them. |
| Outcome: | The proposed model can distinguish between homographic pun and non-homographic pun texts. |