Demo Data from EDBT 2026 / reviewers in the wild / expert
Weidong Han 0002
dblp:37/7130-2
· DBLP profile ↗
4ranked-venue papers
1as first author
4since 2021 · last 2024
0009-0000-1352-4944ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Vision and language · 44% Information extraction and text analysis · 44% Language models and text generation · 13% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Vision and language › vision-language model
prompt learning |
0.7 | 1 | 2023 | What Makes Pre-trained Language Models Better Zero-shot Learners? · ACL (1) 2023 |
Natural language and speech › Information extraction and text analysis › text classification › transfer learning for text classification
zero-shot text classification |
0.7 | 1 | 2023 | What Makes Pre-trained Language Models Better Zero-shot Learners? · ACL (1) 2023 |
Natural language and speech › Language models and text generation
pre-trained language model |
0.2 | 1 | 2023 | What Makes Pre-trained Language Models Better Zero-shot Learners? · ACL (1) 2023 |
Methods — techniques the papers use, named apart from their topics
perplexity selection · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction OptimizationabstractDongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. Dongsheng Zhu, Daniel Tang, Weidong Han 0002, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang 0009, Dawei Yin 0001 |
NAACL-HLT | 3 |
| 2023 | What Makes Pre-trained Language Models Better Zero-shot Learners?abstractCurrent methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori.This is not ideal because in a real-world zero-shot scenario of practical relevance, no labelled data is available.Thus, we propose a simple yet effective method for screening reasonable prompt templates in zero-shot text classification: Perplexity Selection (Perplection).We hypothesize that language discrepancy can be used to measure the efficacy of prompt templates, and thereby develop a substantiated perplexity-based scheme allowing for forecasting the performance of prompt templates in advance.Experiments show that our method leads to improved prediction performance in a realistic zero-shot setting, eliminating the need for any labelled examples.PPL Acc.(%) PPL Acc.(%) PPL Acc.(%) PPL Acc.(%) DOUBAN 24.61 57.12 40.93 50.98 28.80 56.68 71.01 51.31 WEIBO 19.78 61.79 30.37 51.16 22.34 58.35 44.45 50.92WAIMAI 16.44 67.80 23.34 53.15 19.68 69.72 36.07 48.49ECOMMERCE 14.07 73.12 18.45 55.68 16.88 67. Jinghui Lu, Dongsheng Zhu, Weidong Han 0002, Brian Mac Namee, Fei Tan 0002 |
ACL (1) | 3 |
| 2023 | Question Answering over Knowledge Graphs via Machine Reading Comprehension
Weidong Han 0002, Zhaowu Ouyang, Weiguo Zheng |
DASFAA (2) | 1 |
| 2021 | Towards combinational relation linking over knowledge graphs
Weiguo Zheng, Deqing Yang, Weidong Han 0002 |
World Wide Web | 5 |