Papers by Ziyi Yang
DentalGPT: Incentivizing Multimodal Reasoning in Dentistry (2026.findings-acl)
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Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng, Yanchao Li, Liang Jingyi, Junying Chen, Yunjin Yang, Jiajun You, Shuzhi Deng, null Xieruiqiii, Yuanting Chen, Xiangyi Feng, Jianquan Li, Liangyi Chen, Junwen Wang, Shan Jiang, Benyou Wang
| Challenge: | Current multimodal large language models (MLLMs) show limited understanding of dental images. |
| Approach: | They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning. |
| Outcome: | The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks. |
Mutual-Taught for Co-adapting Policy and Reward Models (2025.acl-long)
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Tianyuan Shi, Canbin Huang, Fanqi Wan, Longguang Zhong, Ziyi Yang, Weizhou Shen, Xiaojun Quan, Ming Yan
| Challenge: | Experimental results show that this iterative approach leads to consistent improvements in both the policy model and reward model. |
| Approach: | They propose a method that iteratively improves both the policy model and reward model without requiring additional human annotation. |
| Outcome: | The proposed method improves both the policy model and reward model without human annotation. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
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| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge (2021.findings-acl)
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| Challenge: | a riddle-style commonsense questions require complex commonsensense reasoning and figurative language skills . there is currently no dataset aimed at testing these abilities . authors propose a new multiple-choice question answering task . |
| Approach: | They propose a new multiple-choice question answering task that uses a large dataset for riddlestyle commonsense questions. |
| Outcome: | The proposed task comes with the first large dataset for answering riddlestyle commonsense questions. |
ThinkSwitcher: When to Think Hard, When to Think Fast (2025.findings-emnlp)
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| Challenge: | Large reasoning models excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. |
| Approach: | They propose a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity. |
| Outcome: | The proposed framework reduces computational cost by 20-30% while maintaining high accuracy on complex tasks. |
Parameter-free Sentence Embedding via Orthogonal Basis (D19-1)
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| Challenge: | Existing methods to build sentence embeddings are parameterized and require training to optimize their parameters. |
| Approach: | They propose a non-parameterized method to combine pre-trained word embeddings into sentence representations using an orthogonal basis of the word vector subspace and its surrounding context. |
| Outcome: | The proposed method shows superior performance on 11 downstream NLP tasks and is competitive to other methods relying on large amounts of labelled data or prolonged training time. |
A Unified Supervised and Unsupervised Dialogue Topic Segmentation Framework Based on Utterance Pair Modeling (2025.naacl-long)
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| Challenge: | Unsupervised methods for dialogue topic segmentation are difficult to surpass due to short sentences, serious references and non-standard language. |
| Approach: | They propose a method to divide a dialogue into different topic paragraphs to better understand its structure and content. |
| Outcome: | The proposed method achieves the best results on multiple benchmark datasets across different scenarios. |
Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media (2025.acl-long)
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| Challenge: | Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs) however, the misuse of AIGTs could have profound implications for public opinion . |
| Approach: | They collect a dataset with 2.4M posts from 3 major social media platforms . they then construct a diverse dataset to train and evaluate AIGT detectors . |
| Outcome: | The proposed dataset analyzes 2.4M posts from 3 major social media platforms from 2022 to 2024 . it finds that Medium and Quora show marked increases in AAR . |
A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)
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| Challenge: | Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models . |
| Approach: | They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning . |
| Outcome: | The proposed framework outperforms existing reasoning-based baselines on KGQA datasets. |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
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| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |
Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)
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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang
| Challenge: | Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE. |
| Approach: | They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four widely used datasets. |
Self-contradictory reasoning evaluation and detection (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive reasoning ability, but many downstream reasoning tasks focus on performance-wise evaluation. |
| Approach: | They define and assess the Self-Contra rate across three datasets and delve into finer-grained categories of Self-contra reasoning. |
| Outcome: | The proposed model can detect self-contra reasoning with a 52.2% F1 score, much lower than for humans. |
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning (2023.acl-long)
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Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren
| Challenge: | Existing methods to improve logical reasoning skills require complex data processing. |
| Approach: | They propose an adaptive pretraining approach to improve logical reasoning over text . they use a subset of Wikipedia sentences for pretraining and a sentence-level classification loss . |
| Outcome: | The proposed model outperforms baselines on LogiQA and ReClor. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Tree-of-Code: A Self-Growing Tree Framework for End-to-End Code Generation and Execution in Complex Tasks (2025.findings-acl)
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| Challenge: | Effectively and efficiently handling complex realworld problems has become a key focus across industry and academia. |
| Approach: | They propose a tree-of-code framework that generates nodes through self-supervision and combines prompt and model exploration in a GT-free setting. |
| Outcome: | Experiments on two datasets with ten popular zero-shot LLMs show that Tree-of-Code boosts accuracy by nearly 20% over CodeAct with fewer than 1/4 turns. |
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into agentic frameworks to assist individual users in completing diverse tasks. |
| Approach: | They propose a simulation environment with a plug-and-play proactive AI mediator . they use a socio-cognitive evaluation framework to measure consensus changes, intervention latency, mediator effectiveness and intelligence. |
| Outcome: | The proposed model outperforms a generic baseline in multi-party negotiation scenarios while being 77% faster in response. |
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)
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| Challenge: | a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets. |
| Approach: | They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task . |
| Outcome: | The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation. |
FuseChat: Knowledge Fusion of Chat Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are costly and require significant computational resources and time. |
| Approach: | They propose a fuse-and-merge framework for the knowledge fusion of chat LLMs . they conduct pairwise knowledge fusing on source chat LRMs to create multiple target LLM . |
| Outcome: | The proposed framework is superior to baselines of various sizes. |
MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)
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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
| Challenge: | Existing work on controllable summarization with mixed attributes lacks designated annotations. |
| Approach: | They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources . |
| Outcome: | The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks . |
Language Model as Planner and Formalizer under Constraints (2026.acl-long)
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| Challenge: | Large language models (LLMs) have been widely used in planning but lack interpretability and control. |
| Approach: | They propose to augment widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories. |
| Outcome: | The proposed model outperforms existing models in 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets. |
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL (2026.acl-long)
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Yaxun Dai, Wenxuan Xie, Xialie Zhuang, Tianyu Yang, Ziyi Liu, Haiqin Yang, Yiying Yang, Yuhang Zhao, Pingfu Chao, Wenhao Jiang
| Challenge: | Current Text-to-SQL reasoning models lack integrated execution feedback during generation. |
| Approach: | They propose a text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback. |
| Outcome: | The proposed framework achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale. |
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)
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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen
| Challenge: | Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody . |
| Approach: | They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis. |
| Outcome: | The proposed datasets provide richer contextual information, which is lacking in existing datasets. |
MM-Reasoner: A Multi-Modal Knowledge-Aware Framework for Knowledge-Based Visual Question Answering (2023.findings-emnlp)
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| Challenge: | Recent knowledge-based visual question answering approaches miss visual information captured by captions and cannot fully utilize the visual information required to answer the question. |
| Approach: | They propose a framework that extracts visual information from an image and prompts an LLM to extract query-specific knowledge from the extracted textual information. |
| Outcome: | Empirical results show that MM-Reasoner achieves state-of-the-art performance on several KVQA datasets. |
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations (2021.emnlp-main)
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| Challenge: | Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. |
| Approach: | They propose a method that factors out language identity information from semantic related components in multilingual representations pre-trained on monolingual data. |
| Outcome: | The proposed method improves cross-lingual transfer performance on weak alignment models. |
Embedding Imputation with Grounded Language Information (P19-1)
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| Challenge: | Existing approaches to embedding imputation use vector space properties or subword information to learn representations for rare or unseen words. |
| Approach: | They propose an online method to construct a knowledge graph from grounded information and an algorithm to map from the resulting graph to the space of the pre-trained embeddings. |
| Outcome: | The proposed method improves on a card-660 task by 11% and 17.8% respectively using GloVe embeddings. |
P-QuASAR: A Unified Probabilistic Framework for Holistic Patent Quality Assessment and Refinement (2026.findings-acl)
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| Challenge: | Existing methods for assessing patent quality rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions. |
| Approach: | They propose a probabilistic framework that represents patent specifications as Quality Graphs. |
| Outcome: | The proposed framework outperforms existing methods on 500 patents against seven baselines. |
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising (2020.findings-emnlp)
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| Challenge: | Existing abstractive summarization models ignore abundant unlabeled corpora resources . TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets . |
| Approach: | They propose a transformer-based unsupervised text summarization system with pretraining on large-scale data. |
| Outcome: | The proposed system outperforms baseline models on NYT, CNN/DM and English Gigaword datasets with various document styles. |
i-Code Studio: A Configurable and Composable Framework for Integrative AI (2024.emnlp-demo)
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Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang
| Challenge: | Existing frameworks for Integrative AI lack flexibility and composability to handle multimodal tasks. |
| Approach: | They propose a configurable framework for Integrative AI that orchestrates multiple pre-trained models to conduct complex multimodal tasks. |
| Outcome: | The proposed framework achieves impressive results on zero-shot multimodal tasks . it can communicate and personalize for users, and it can be used in a multimodal agent . |