Papers by Jintao Cheng
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
Copied to clipboard
Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
Copied to clipboard
Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
Copied to clipboard
Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
Anchoring the Affective Manifold: Learning Canonical and Disentangled Representations via Generative Cross-Modal Alignment (2026.acl-long)
Copied to clipboard
| Challenge: | Dominant multimodal emotion recognition paradigms neglect the intrinsic geometric structure of affect, resulting in representations heavily entangled with non-affective factors. |
| Approach: | They propose a Canonical Disentangled Multimodal Generative Framework that decomposes the latent space into a canonical Shared Affective Subspace and a private Modality Subspace. |
| Outcome: | The proposed model disentangles affect from private attributes while enabling controllable emotion generation. |