Papers by Lichao Zhang
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture (2025.findings-emnlp)
Copied to clipboard
Xidong Wang, Dingjie Song, Shunian Chen, Junying Chen, Zhenyang Cai, Chen Zhang, Lichao Sun, Benyou Wang
| Challenge: | Long-context Large Language Models (MLLMs) are critical for video understanding and image analysis. |
| Approach: | They propose a hybrid architecture that integrates Mamba and Transformer blocks . they introduce data construction methods that capture both temporal and spatial dependencies . |
| Outcome: | The proposed model achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption. |
FinLLM-B: When Large Language Models Meet Financial Breakout Trading (2025.naacl-industry)
Copied to clipboard
| Challenge: | Existing methods for financial breakout detection are subpar, despite large data and knowledge required. |
| Approach: | They propose a financial breakout dataset and introduce FinLLM-B, a large language model for financial breakout detection. |
| Outcome: | The proposed model outperforms GPT-3.5 in the field of financial breakout detection. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
Copied to clipboard
Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to speech-to-singing voice conversion are difficult to learn in text-free situations. |
| Approach: | They propose an STS model which views speech variance as different modalities . it uses a novel rhythm adaptor to predict the target rhythm representation . they also use the predicted rhythm representation to re-align the content . |
| Outcome: | The proposed model achieves superior performance in terms of objective and subjective metrics. |
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown significant potential in code generation, but they also present challenges regarding the protection of Intellectual Property (IP) related to model architectures, weights, and training data. |
| Approach: | They propose a multi-bit watermarking technique that embeds additional information to preserve provenance details, such as the vendor ID of an LLM. |
| Outcome: | The proposed technique preserves provenance details while maintaining syntactical correctness of generated code. |
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)
Copied to clipboard
Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
| Challenge: | Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT. |
| Approach: | They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness. |
| Outcome: | The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage. |
| Approach: | They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE. |
| Outcome: | The proposed framework can be used to infer whether a specific relation exists in a private client. |
AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)
Copied to clipboard
Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao
| Challenge: | Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech. |
| Approach: | They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness. |
| Outcome: | The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines . |