Papers by Mingda Zhang
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
| Outcome: | The proposed framework is highly consistent with human evaluations and better than the state-of-the-art models. |
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Large Language Models (RALMs) do not consistently outperform the original retrieval-free Language Model (LM). |
| Approach: | They propose a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. |
| Outcome: | The proposed framework significantly improves performance over the RALM with a single retriever by significantly reducing inconsistent behaviors. |
Instruction Data Selection via Answer Divergence (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for instruction tuning use data-centric methods, but they do not explicitly reflect what a particular base model is missing. |
| Approach: | They propose a method for instruction tuning that uses geometric structure of multi-sample outputs to select instruction data. |
| Outcome: | The proposed approach outperforms strong selectors on six benchmarks spanning reasoning, knowledge, and coding. |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
Copied to clipboard
Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
ImpRAG: Retrieval-Augmented Generation with Implicit Queries (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) systems treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. |
| Approach: | They propose a query-free RAG system that integrates retrieval and generation into a unified model. |
| Outcome: | The proposed system can achieve 3.6-11.5 accuracy improvements on unseen tasks . it allows models to express their information needs without human-specified queries . |
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation. |
| Approach: | They propose a retrieval-augmented generation model that embeds retrieval control directly into generation. |
| Outcome: | The proposed model surpasses strong RAG baselines and uses substantially fewer parameters. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
Copied to clipboard
Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |