Papers by Zecheng Wang
Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information (2024.lrec-main)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting and large language models (LLMs) have shown great potential in improving performance on challenging reasoning tasks. |
| Approach: | They propose a new metric which extends the concept of pointwise V-information to black-box models and quantifies label-relevant new information introduced by CoT prompting. |
| Outcome: | The proposed metric extends the concept of pointwise V-information to black-box models, quantifying label-relevant new information introduced by CoT prompting beyond pre-existing label information. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models for text generation use a discrete data embedding module to map the data into the continuous space. |
| Approach: | They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space. |
| Outcome: | The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
Copied to clipboard
Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)
Copied to clipboard
Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui
| Challenge: | Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters. |
| Approach: | They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance. |
| Outcome: | The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios. |
CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality. |
| Approach: | They propose a text detoxification framework that pays attention to both context and detoxification process. |
| Outcome: | Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines. |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |