Papers by Chao Yao
Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Detecting AI-generated poetry is difficult due to distinctive characteristics of modern Chinese poetry. |
| Approach: | They propose a benchmark for detecting AI-generated modern Chinese poetry . they use a high-quality dataset and systematic performance assessments . |
| Outcome: | The proposed benchmark is based on a high-quality dataset of 800 poems written by six professional poets and 41,600 poems generated by four mainstream LLMs. |
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022.coling-1)
Copied to clipboard
| Challenge: | Existing knowledge graph completion models require only a few associative triples to complete a relationship. |
| Approach: | They propose to perform data augmentation from two perspectives to solve the FKGC problem by inferring new triple facts from existing models. |
| Outcome: | The proposed framework can be applied to a number of existing models. |
Hierarchical Attention Generates Better Proofs (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown promise in formal theorem proving, but their token-level processing often fails to capture the inherent hierarchical nature of mathematical proofs. |
| Approach: | They propose a regularization method that aligns LLMs’ attention mechanisms with mathematical reasoning structures and establishes a five-level hierarchy from foundational elements to high-level concepts. |
| Outcome: | The proposed method improves proof success rates by 2.05% on miniF2F and 1.69% on ProofNet while reducing proof complexity by 23.81% and 16.50% respectively. |
Improving Large-scale Paraphrase Acquisition and Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing Twitter-based paraphrase datasets lack quality definitions for identification and generation tasks. |
| Approach: | They propose to use two separate definitions of paraphrase for identification and generation tasks in existing Twitter-based paraphrase datasets. |
| Outcome: | The proposed model achieves state-of-the-art performance of 84.2 F1 for automatic paraphrase identification compared to other models fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
Copied to clipboard
Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable. |
| Approach: | They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation. |
| Outcome: | The proposed framework achieves up to 29.2% improvement over six strong baselines for this task. |
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on summarizing news documents or structured documents. |
| Approach: | They propose to use a large-scale narrative summarization dataset to encourage research . they find there is a performance gap between humans and the models on NarraSum . |
| Outcome: | The proposed dataset shows that humans and state-of-the-art models perform poorly when summarizing a narrative . it contains 122K narratives collected from synopses of movies and TV episodes with diverse genres . |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
Copied to clipboard
| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Poller: Are LLMs Suitable for Evaluating Poetry Understanding Task? (2026.findings-acl)
Copied to clipboard
| Challenge: | Traditional methods for poetry evaluation are expensive and unsuitable for large-scale data. |
| Approach: | They propose a method leveraging Large Language Models to evaluate poetry understanding tasks using Large Language models. |
| Outcome: | The proposed method reduces the evaluation error between LLMs and humans by adopting the poet's perspective. |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
Copied to clipboard
Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
What is the Best Way for ChatGPT to Translate Poetry? (2024.acl-long)
Copied to clipboard
| Challenge: | Despite promising results, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. |
| Approach: | They propose an Explanation-Assisted Poetry Machine Translation method which leverages monolingual poetry explanation as a guiding information for the translation process. |
| Outcome: | The proposed method outperforms traditional translation methods of ChatGPT and the existing online systems in English-Chinese poetry translation. |
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU (2024.lrec-main)
Copied to clipboard
| Challenge: | Multi-intent natural language understanding (NLU) models lack the rich information between the shared intents, especially in low-data scenarios. |
| Approach: | They propose a two-stage framework for multi-intent natural language understanding to harness shared intent information by word-level pre-training and prediction-aware contrastive fine-tuning. |
| Outcome: | The proposed framework surpasses baselines on low-data and full-data scenarios. |
Can ChatGPT Really Understand Modern Chinese Poetry? (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent studies have focused on poetry generation and translation, but their scope has been limited to evaluation and analysis of experimental results without addressing fundamental issues of comprehension. |
| Approach: | They propose a framework for evaluating ChatGPT's understanding of modern poetry . they evaluated the interpretations of unpublished modern Chinese poems by different poets . |
| Outcome: | The proposed framework is based on the evaluation of unpublished poems by poets and shows that its interpretations align with the original poets’ intents in over 73% of the cases. |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
Copied to clipboard
Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)
Copied to clipboard
| Challenge: | Existing models for dialogue comprehension are not available for the pre-training of such a model. |
| Approach: | They propose a narrative-guided pre-training strategy that learns by narrating key information from a dialogue input. |
| Outcome: | The proposed model performs better on four dialogue-based tasks and is comparable to existing models. |
SGIC: A Self-Guided Iterative Calibration Framework for RAG (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies on retrieval-augmented generation (RAG) focus on extracting relevant documents or refinement of specialized instructions. |
| Approach: | They propose a framework that provides LLMs with specific cues to improve their calibration efficacy . they propose an iterative self-calibration training set that harnesses uncertainty scores . |
| Outcome: | The proposed framework significantly improves performance on both closed-source and open-source LLMs. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
Copied to clipboard
Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA (2026.findings-acl)
Copied to clipboard
Guanhua Chen, Yutong Yao, Shenghe Sun, Ci-jun Gao, Shudong Liu, Lidia S. Chao, Feng Wan, Derek F. Wong
| Challenge: | Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their capability in visual procedure question answering (VP-QA) remains largely unexplored. |
| Approach: | They propose a multimodal benchmark specifically designed for visual procedural reasoning that synergizes cross-modal procedure retrieval, context-aware step decomposition, and the next step prediction. |
| Outcome: | The proposed framework significantly outperforms baselines on visual procedure question answering (VP-QA) Experiments on six VLMs show that it performs better than baselines. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
Copied to clipboard
Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |