Papers by Ming Xiang
Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)
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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data. |
| Approach: | They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse. |
| Outcome: | The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures. |
AnRe: Analogical Replay for Temporal Knowledge Graph Forecasting (2025.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are vital for event prediction, yet current methods face limitations. |
| Approach: | They propose a training-free Analogical Replay reasoning framework that uses LLMs to extract historical contexts and generate analogical reasoning examples as contextual inputs. |
| Outcome: | The proposed model outperforms existing training-free methods on four benchmarks. |
Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation (2023.findings-emnlp)
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| Challenge: | Pre-trained code models have made significant strides in the field of neural code intelligence, but they are susceptible to adversarial attacks that subtly modify the input sequence and can impair generalization. |
| Approach: | They propose a set of novel robustness evaluation methods based on the intrinsic structure of the code to explore the impact of imperceptible perturbation. |
| Outcome: | The proposed methods have demonstrated their effectiveness across a wide range of models and tasks, and are able to predict the performance of perturbed models. |
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)
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| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
| Approach: | They propose to use syntactically simple sentences to test the inference ability of NLI models. |
| Outcome: | The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair. |
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have shown increasing power on NLP tasks. however, tuning these models for downstream tasks usually requires exorbitant costs. |
| Approach: | They propose a black-box tuning technique that optimizes task-specific prompts without accessing gradients and hidden representations. |
| Outcome: | The proposed method improves performance under few-shot learning scenarios. |
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020.findings-emnlp)
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| Challenge: | Recent studies show that pre-trained language models perform well on commonsense-reasoning benchmark datasets, but building machines with commonsence to compose plausible sentences remains challenging. |
| Approach: | They propose a constrained text generation task for generative commonsense reasoning that generates a coherent sentence using common concepts. |
| Outcome: | The proposed task generates a coherent sentence describing an everyday scenario using common concepts over 35k concept-sets. |
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)
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| Challenge: | Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. |
| Approach: | They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. |
| Outcome: | The proposed approach maintains exceptional performance in imbalanced label distributions. |
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models (2023.emnlp-main)
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| Challenge: | Chain-of-Thought prompting has improved the reasoning capabilities of Large Language Models (LLMs) but it is ineffective or detrimental to the performance on reasoning tasks in Smaller Language Model (SLMs) with less than 10 billion parameters. |
| Approach: | They propose a Dialogue-guided Chain-of-Thought method to improve the reasoning capabilities of Large Language Models (LLMs) by generating intermediate reasoning steps in a dialogue format to guide the model to the final answer. |
| Outcome: | The proposed method can achieve significant performance gains over state-of-the-art competitors on four arithmetic reasoning datasets. |
From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)
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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin
| Challenge: | Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization. |
| Approach: | They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. |
| Outcome: | ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks . |
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors (2022.acl-long)
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| Challenge: | Existing studies for understanding programs do not take human behaviors as reference. |
| Approach: | They propose a graph neural network model that takes human behaviors as reference in understanding programs. |
| Outcome: | The proposed model performs better on code summarization and code clone detection tasks. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a fundamental information extraction task that focuses on extracting entities from a given text and classifying them using pre-defined categories. |
| Approach: | They propose to use “entity triggers” to facilitate label-efficient learning of NER models. |
| Outcome: | The proposed model is significantly more cost-effective than the traditional neural NER frameworks. |
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization (2025.naacl-long)
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| Challenge: | Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly. |
| Approach: | They propose a method for optimizing the RAG generator from multiple preference perspectives to align with RAG requirements comprehensively. |
| Outcome: | The proposed method improves the performance of RAG generators by incorporating retrieved documents into the prompt. |
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills (2024.lrec-main)
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| Challenge: | Existing methods to fine-tune code intelligence models to individual tasks are costly and require large data sets. |
| Approach: | They propose a Transferable fine-tuning strategy for Code representation learning that uses a tunable prefix encoder to capture cross-task and cross-language transferable knowledge and apply it to downstream adaptation. |
| Outcome: | The proposed method can lead to superior performance on code-related tasks and encourage mutual reinforcement. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting (2024.acl-long)
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| Challenge: | Recent studies have shown that Chain-of-Thought (CoT) prompting can be effective on complex reasoning tasks but generates unfaithful and unfactual reasoning chains. |
| Approach: | They propose a chain-of-knowledge prompting that elicits Large Language Models to generate explicit pieces of knowledge evidence in the form of structure triple. |
| Outcome: | The proposed method improves commonsense, factual, symbolic, and arithmetic reasoning tasks by estimating the reliability of the reasoning chains in terms of factuality and faithfulness. |
How do autoregressive transformers solve full addition? (2025.emnlp-main)
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| Challenge: | Large pre-trained language models have demonstrated impressive capabilities, but there is still much to learn about how they operate. |
| Approach: | They investigate the ability of the autoregressive transformer to perform basic addition operations by using causal analysis to find that a few different attention heads in the middle layers control the addition carry . they found that due to the lack of global focus on the sequence within these attention heads, the model struggles to handle long-sequence addition tasks. |
| Outcome: | The model performs basic addition tasks, but it still faces challenges with length generalization. |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated. |
| Approach: | They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty. |
| Outcome: | The proposed framework improves performance and efficiency over multiple tasks over multiple datasets. |
Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation (2026.findings-eacl)
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| Challenge: | Existing large language models (LLMs) fail to identify information gaps across diverse symptoms. |
| Approach: | They propose a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 5% - 8% on relevant benchmarks. |
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules. |
| Approach: | They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs. |
| Outcome: | The proposed framework can be flexibly combined with existing mainstream PLMs. |
Exploring Continual Learning for Code Generation Models (2023.acl-short)
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Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022.acl-short)
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Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth
| Challenge: | Empirical analyses show that pre-trained sequence-to-sequence models can achieve a 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts. |
| Approach: | They propose to distill and quantize pre-trained sequence-to-sequence models to reduce memory and latency requirements. |
| Outcome: | Empirical results show that the proposed model achieves 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts on multiple summarization and QA datasets. |
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins. |
| Approach: | They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment. |
| Outcome: | The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin. |
Structure-aware Fine-tuning for Code Pre-trained Models (2024.lrec-main)
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| Challenge: | Existing CodePTMs are mainly structure-free and structurebased, but how to fine-tune them remains a challenge. |
| Approach: | They propose a plug-and-play fine-tuning method that incorporates structural knowledge into pre-trained code models. |
| Outcome: | The proposed method can benefit CodePTMs more with limited training data. |
Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025.naacl-long)
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| Challenge: | Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. |
| Approach: | They propose a knowledge-enhanced approach that incorporates 'label-level knowledge' to enhance the representation of case facts for each task and 'task-level' knowledge to improve synergy. |
| Outcome: | The proposed method is effective in comparison to state-of-the-art (SOTA) baselines. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
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Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
| Challenge: | Existing studies show that causal language models lack expressiveness due to poor discrimination ability. |
| Approach: | They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models. |
| Outcome: | The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks. |
Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning (2023.findings-emnlp)
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| Challenge: | Code pre-trained models have been proposed and widely applied in the domain of code intelligence. |
| Approach: | They propose a method that uses a plug-and-play graph neural network module as a tunable prefix to exploit structural information of source code. |
| Outcome: | The proposed method exploits structural information of source code and could replace full fine-tuning. |
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022.findings-emnlp)
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| Challenge: | Existing code pre-trained models fail to consider inherent characteristics of codes . Existing methods to interpret code pretrained model fail to take into account inherent characteristics . |
| Approach: | They propose a probing method to quantitatively interpret how CodePTMs attend code structure. |
| Outcome: | The proposed method denoises input code sequences and measures commonality between token-level attention scores and pair-wise distances between corresponding AST nodes. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario (2023.findings-acl)
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| Challenge: | Large pre-trained language models (PLMs) are expensive and may not be open-sourced due to commercial considerations and potential risks of misuse. |
| Approach: | They propose to introduce gradient descent into black-box tuning scenario . they propose a method which integrates gradient descent and derivative-free optimization . |
| Outcome: | The proposed method achieves significant performance gains over previous state-of-the-art methods. |