| Large language models for data annotation and synthesis: A survey Z Tan, D Li, S Wang, A Beigi, B Jiang, A Bhattacharjee, M Karami, J Li, ... Proceedings of the 2024 Conference on Empirical Methods in Natural Language …, 2024 | 140* | 2024 |
| Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning Y Tong, D Li, S Wang, Y Wang, F Teng, J Shang ACL 2024, 2024 | 27 | 2024 |
| Contextualization distillation from large language model for knowledge graph completion D Li, Z Tan, T Chen, H Liu EACL 2024 (Findings), 2024 | 27 | 2024 |
| C3kg: A chinese commonsense conversation knowledge graph D Li, Y Li, J Zhang, K Li, C Wei, J Cui, B Wang ACL 2022 (Findings), 2022 | 25 | 2022 |
| DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature D Li, S Yang, Z Tan, JY Baik, S Yun, J Lee, A Chacko, B Hou, ... EMNLP 2024 (Findings), 2024 | 23 | 2024 |
| From generation to judgment: Opportunities and challenges of llm-as-a-judge D Li, B Jiang, L Huang, A Beigi, C Zhao, Z Tan, A Bhattacharjee, Y Jiang, ... arXiv preprint arXiv:2411.16594, 2024 | 20 | 2024 |
| Multi-level contrastive learning for script-based character understanding D Li, H Zhang, Y Li, S Yang EMNLP 2023, 2023 | 15 | 2023 |
| Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking Y Tong, Y Wang, D Li, S Wang, Z Lin, S Han, J Shang arXiv preprint arXiv:2310.12342, 2023 | 12 | 2023 |
| Assisting language learners: Automated trans-lingual definition generation via contrastive prompt learning H Zhang, D Li, Y Li, C Shang, C Shi, Y Jiang ACL-BEA 2023, 2023 | 11 | 2023 |
| Fine-grained contrastive learning for definition generation H Zhang, D Li, S Yang, Y Li AACL-IJCNLP 2024 (Oral), 2022 | 11 | 2022 |
| Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model H Zhang, Y Wu, D Li, Z Yang, R Zhao, Y Jiang, F Tan ACL 2024 (Findings), 2024 | 9 | 2024 |
| DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase D Li, Y Li, D Mekala, S Li, X Wang, W Hogan, J Shang arXiv preprint arXiv:2311.03319, 2023 | 7 | 2023 |
| Optimizing Language Model's Reasoning Abilities with Weak Supervision Y Tong, S Wang, D Li, Y Wang, S Han, Z Lin, C Huang, J Huang, J Shang arXiv preprint arXiv:2405.04086, 2024 | 6 | 2024 |
| A question-centric multi-experts contrastive learning framework for improving the accuracy and interpretability of deep sequential knowledge tracing models H Zhang, Z Liu, C Shang, D Li, Y Jiang TKDD, 2024 | 6 | 2024 |
| Lrq-fact: Llm-generated relevant questions for multimodal fact-checking A Beigi, B Jiang, D Li, T Kumarage, Z Tan, P Shaeri, H Liu arXiv preprint arXiv:2410.04616, 2024 | 4 | 2024 |
| Exploring large language models for feature selection: A data-centric perspective D Li, Z Tan, H Liu SIGKDD Exploration, 2024 | 4 | 2024 |
| Bpo: Towards balanced preference optimization between knowledge breadth and depth in alignment S Wang, Y Tong, H Zhang, D Li, X Zhang, T Chen arXiv preprint arXiv:2411.10914, 2024 | 2 | 2024 |
| Smoa: Improving multi-agent large language models with sparse mixture-of-agents D Li, Z Tan, P Qian, Y Li, KS Chaudhary, L Hu, J Shen arXiv preprint arXiv:2411.03284, 2024 | 2 | 2024 |
| Preference Leakage: A Contamination Problem in LLM-as-a-judge D Li, R Sun, Y Huang, M Zhong, B Jiang, J Han, X Zhang, W Wang, H Liu arXiv preprint arXiv:2502.01534, 2025 | 1 | 2025 |
| Assessing the Impact of Conspiracy Theories Using Large Language Models B Jiang, D Li, Z Tan, X Zhou, A Rao, K Lerman, HR Bernard, H Liu arXiv preprint arXiv:2412.07019, 2024 | 1 | 2024 |
Zhen TanPh.D. at Arizona State UniversityVahvistettu sähköpostiosoite verkkotunnuksessa asu.edu