Papers by Luyang Kong
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics (2021.findings-acl)
Copied to clipboard
| Challenge: | Current approaches to medical entity retrieval generalize poorly to unseen sub-specialties . zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora . |
| Approach: | They propose a set of learning tasks designed to train efficient zero-shot entity retrieval models. |
| Outcome: | The proposed architecture outperforms common zero-shot benchmarks with 7% to 30% higher recall across multiple major medical ontologies. |
Learning from Natural Language Explanations for Generalizable Entity Matching (2024.emnlp-main)
Copied to clipboard
| Challenge: | Entity matching is the task of linking records from different sources that refer to the same real-world entity. |
| Approach: | They propose to "distill" LLM reasoning into smaller entity matching models via natural language explanations. |
| Outcome: | The proposed model distillation approach achieves strong performance on out-of-domain generalization tests (10.85% F-1). |
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings (2025.naacl-industry)
Copied to clipboard
Xuanqing Liu, Luyang Kong, Wei Niu, Afshin Khashei, Belinda Zeng, Steve Johnson, Jon Jay, Davor Golac, Matt Pope
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. |
| Approach: | They propose a framework that combines the scalability of LLM-generated labels with the precision of human annotations to achieve higher speed and accuracy comparable to larger models. |
| Outcome: | The proposed framework significantly improves accuracy across utterance-level dialogue tasks, including sentiment detection (over 2%), dialogue act classification (over 1.5%), etc. |
Textual Dataset Distillation via Language Model Embedding (2024.findings-emnlp)
Copied to clipboard
| Challenge: | prevailing methods for dataset distillation generate distilled data as embedding vectors, which are not human-readable. |
| Approach: | They propose a model-agnostic, data-efficient method that leverages Language Model embeddings . their method offers enhanced flexibility and improved transferability . |
| Outcome: | The proposed method achieves comparable performance with faster processing times compared to other methods . it offers enhanced flexibility and improved transferability, expanding the range of potential applications . |
CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositories (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated significant advances in computer science research . current agent-related applications include code writing, code base generation, code correction and more. |
| Approach: | They propose a benchmark to assess the effectiveness of Large Language Models (LLMs) they propose GitHub agent framework that automates deployment of GitHub repositories . |
| Outcome: | The proposed framework improves the deployment of GitHub code repositories and thereby boosts developer productivity. |
BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)
Copied to clipboard
Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac
| Challenge: | Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. |
| Approach: | They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches. |
| Outcome: | The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals. |