Papers by Yiyang Li
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)
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| Challenge: | Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. |
| Approach: | They propose a bidirectional information decoupling network as a universal dialogue encoder which explicitly incorporates both the past and future contexts. |
| Outcome: | The proposed model incorporates past and future contexts and can be generalized to a wide range of dialogue-related tasks. |
S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models (2026.acl-long)
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Feng Jiang, Zhiyu Lin, Yiyang Liu, Liumeng Xue, Fan Bu, Yuhao Du, Xiangying Chen, Benyou Wang, Haizhou Li
| Challenge: | Existing benchmarks rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits. |
| Approach: | They propose a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression. |
| Outcome: | The proposed system enables more natural, robust, and human-aligned speech agents. |
Model Composition for Multimodal Large Language Models (2024.acl-long)
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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use (2026.findings-eacl)
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Yiyang Li, Zehong Wang, Zhengqing Yuan, Zheyuan Zhang, Keerthiram Murugesan, Chuxu Zhang, Yanfang Ye
| Challenge: | Illicit drug use among teens and young adults remains a public health concern . existing models ignore latent and interconnected structures among survey variables . |
| Approach: | They propose a joint graph-language modeling framework to detect illicit drug use among TYAs . they use large-scale surveys such as the Youth Risk Behavior Survey and the National Survey on Drug Use and Health to analyze data . |
| Outcome: | The proposed framework outperforms baseline models on YRBS and NSDUH datasets in predictive accuracy. |
Universal Semantic Tagging for English and Mandarin Chinese (2021.naacl-main)
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| Challenge: | Existing approaches to generating semantic annotations for different languages are attracting more and more interest. |
| Approach: | They propose to extend Universal Semantic Tagging to Mandarin Chinese and evaluate its performance. |
| Outcome: | The proposed scheme is only tested in four Indo–European languages . accuracies are 92.7% and 94.6% for Chinese and English respectively . |
Revisiting De-Identification of Electronic Medical Records: Evaluation of Within- and Cross-Hospital Generalization (2023.emnlp-main)
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| Challenge: | De-identification is a natural language processing task to detect and remove the protected health information (PHI) from electronic medical records (EMRs). |
| Approach: | They propose a de-identification dataset comprising EMRs from three hospitals in China and use it to establish a benchmark for evaluating both within- and cross-hospital generalization. |
| Outcome: | The proposed model with almost perfect within-hospital performance struggles when transferred across hospitals. |
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)
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Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
| Challenge: | despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions. |
| Approach: | They propose a Chinese benchmark built exclusively on real human speech to fill this gap . they assess LALMs across three complementary axes: instruction following, knowledge understanding, robustness . |
| Outcome: | VCB Bench assesses LALMs across three complementary axes: instruction following, knowledge understanding, and robustness . VCBM Bench provides reproducible and fine-grained framework for Chinese voice chat bots . results show significant performance disparities and offer tangible insights for future improvements . |
Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)
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| Challenge: | Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings. |
| Approach: | They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance. |
| Outcome: | The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization. |
SAPGraph: Structure-aware Extractive Summarization for Scientific Papers with Heterogeneous Graph (2022.aacl-main)
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Siya Qi, Lei Li, Yiyang Li, Jin Jiang, Dingxin Hu, Yuze Li, Yingqi Zhu, Yanquan Zhou, Marina Litvak, Natalia Vanetik
| Challenge: | Abstractive and extractive methods are used to condense long text into concise summaries while retaining essential information. |
| Approach: | They propose to use paper structure to extract paper summaries from long text . they provide a large-scale dataset of COVID-19-related papers . |
| Outcome: | The proposed framework generates more comprehensive and valuable summaries compared to previous work on COVID-19-related papers. |
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning (2025.acl-long)
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Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
| Challenge: | Diet plays a critical role in human health, but tailoring dietary reasoning to individual health conditions remains a challenge. |
| Approach: | a new benchmark evaluates dietary reasoning using a national health survey data set. |
| Outcome: | The NGQA benchmark evaluates dietary reasoning across three tasks using a set of question complexity settings and baseline models. |
Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation (2026.acl-long)
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| Challenge: | Existing approaches to sign language translation (SLT) assume video segments are directly mappable to spoken-language words. |
| Approach: | They propose a reasoning-driven SLT framework that uses an ordered sequence of latent thoughts as an explicit middle layer between video and generated text. |
| Outcome: | The proposed model improves coherence and faithfulness over existing gloss-free methods. |
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)
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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang
| Challenge: | TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites . |
| Approach: | They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs . |
| Outcome: | The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification . |
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)
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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang
| Challenge: | Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases. |
| Approach: | They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. |
| Outcome: | The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)
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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao
| Challenge: | Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning. |
| Approach: | They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis. |
| Outcome: | The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. |
Deep Span Representations for Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing span-based NER systems shallowly aggregate token representations to span representations, causing performance degradations. |
| Approach: | They propose a method that shallowly aggregates token representations to span representations . DSpERT produces span representation of deep semantics with weight initialization . |
| Outcome: | The proposed model performs better than or competitive with state-of-the-art systems on six NER benchmarks. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
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| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for multi-party dialogue machine reading comprehension focus on how to incorporate speaker information into the model, which is usually rare in real scenarios. |
| Approach: | They propose to model speaker and key-utterances using self-supervised prediction tasks and capture salient clues in a long dialogue. |
| Outcome: | The proposed method outperforms baseline models and state-of-the-art models on two benchmark datasets. |
EM Pre-training for Multi-party Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to pretrain large language models for dialogue response generation are difficult due to the lack of annotated addressee labels in multi-party dialogue datasets. |
| Approach: | They propose an Expectation-Maximization approach that iteratively performs expectation steps to generate addressee labels and maximize a response generation model. |
| Outcome: | The proposed method is based on two-party dialogues and multi-party dialogs. |
Semantic-Preserving Adversarial Code Comprehension (2022.coling-1)
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| Challenge: | Existing studies on improving PrLMs for source code comprehension have not found a way to improve both sides of the trade-off between the two aspects. |
| Approach: | They propose to use semantic-preserving code embeddings to find worst-case attacks while forcing the model to predict the correct labels under these worst cases. |
| Outcome: | The proposed model can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code. |
Removal of Hallucination on Hallucination: Debate-Augmented RAG (2025.acl-long)
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| Challenge: | erroneous or biased retrieval can mislead generation, compounding hallucinations. |
| Approach: | They propose a framework that integrates multi-agent debates into retrieval and generation stages to improve retrieval reliability. |
| Outcome: | The proposed framework improves retrieval reliability, reduces hallucinations and significantly improves overall factual accuracy. |