Papers by Jiahao Cao
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. |
Dual-Path Counterfactual Integration for Multimodal Aspect-Based Sentiment Classification (2025.emnlp-main)
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| Challenge: | Existing methods for multimodal aspect-based sentiment classification rely on superficial correlations and spurious cues. |
| Approach: | They propose a Dual-Path Counterfactual Integration framework that explicitly models counterfactual reasoning in multimodal contexts. |
| Outcome: | The proposed framework improves model robustness by explicitly modeling counterfactual reasoning in multimodal contexts. |
Intuitive or Dependent? Investigating LLMs’ Behavior Style to Conflicting Prompts (2024.acl-long)
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| Challenge: | Extensive experiments with seven Large Language Models reveal their varying behaviors. |
| Approach: | They investigate the behaviors of Large Language Models when faced with conflicting prompts versus their internal memory. |
| Outcome: | Extensive experiments with seven LLMs reveal their varying behaviors. |
Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)
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| Challenge: | Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language. |
| Approach: | They propose a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of large language models by decomposing evaluation along dimensions of linguistic medium and cultural context. |
| Outcome: | The proposed framework allows for a nuanced analysis of LLMs’ ability to process questions within both native and cross-cultural contexts cross-lingually. |
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)
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| Challenge: | Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors. |
| Approach: | They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models. |
| Outcome: | The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks. |
Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework (2026.findings-acl)
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| Challenge: | Visual Document Retrieval (VDR) is of importance in multimodal retrieval applications. |
| Approach: | They propose a two-stage pruning and merging frameworks that combine pruning and merge techniques to achieve higher compression rates. |
| Outcome: | The proposed framework outperforms existing methods on 29 visual document retrieval datasets. |
TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)
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| Challenge: | Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed . |
| Approach: | They propose a method that explicitly defines attribute relations as inhibtory for multi-attribute CTG . they propose 'tara' which employs token-level attribute relation adaptation and representation to generate text with the balanced multi-attribut . |
| Outcome: | The proposed method generates text with the balanced multi-attribute control. |
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential (2024.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models. |
| Approach: | They propose a "generate-then-read" pipeline to replace retrieval stage with generation from the LLM itself. |
| Outcome: | The proposed framework outperforms single models in the base and chat versions and addresses safety and helpfulness post-adaptation challenges. |
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis (2022.naacl-main)
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| Challenge: | Existing approaches to classify aspects with aspect sentiment bias are hard to find . |
| Approach: | They propose a no-aspect differential sentiment framework for the ABSA task that eliminates aspect sentiment bias and uses differential sentiment loss instead of cross-entropy loss to better classify the sentiments. |
| Outcome: | The proposed framework can be combined with almost all traditional ABSA methods. |
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion (2020.lrec-1)
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| Challenge: | Recent studies show that pre-trained models can learn unsupervised language representations by self-supervised tasks on large-scale corpora. |
| Approach: | They propose a pre-training task called Sentence Insertion for Chinese query-passage pairs NLP tasks . they propose 'word segmentation' method to enhance Chinese Bert performance . |
| Outcome: | The proposed task improves Chinese pre-trained models significantly. |
When Slower Isn’t Truer: Inverse Scaling Law of Truthfulness in Multimodal Reasoning (2026.findings-acl)
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Sitong Fang, Wenjing Cao, Jiahao Li, Xuyao Wang, Chi-Min Chan, Sirui Han, Juntao Dai, Yike Guo, Yaodong Yang, Jiaming Ji
| Challenge: | a study of slow reasoning models for multimodal reasoning finds that they are more prone to fabricating plausible yet false details when confronted with incomplete or misleading visual inputs. |
| Approach: | They conduct the first systematic study of the inverse scaling law in slow-thinking paradigms for multimodal reasoning. |
| Outcome: | The findings suggest that slower reasoning models are more prone to fabricating false details . the study analyzed 5,000-sample hierarchical prompt dataset by 50 participants . |
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism (2024.findings-emnlp)
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| Challenge: | Existing methods for processing large textual content face insufficient adaptation to task-specific needs and missing multi-segmentation relationships. |
| Approach: | They propose a question then reflection memory mechanism which integrates a dual-structured memory pool and a structured graph guidance to facilitate a reflective trial-and-error approach for navigating and identifying relevant segments. |
| Outcome: | The proposed model achieves superior performance on multiple-choice questions and multi-doc QA. |
EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)
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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao
| Challenge: | Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge. |
| Approach: | They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states. |
| Outcome: | The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |