Papers by Luyao Shi
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization (2026.eacl-industry)
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Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi, Charles Mackin, Ashutosh Jadhav, David Beymer, Ehsan Degan, Vandana Mukherjee
| Challenge: | Existing prompting and retrieval-augmented generation methods lack symbolic planning . rigid HDL syntax, limited supervision, and weak alignment with natural language hinder RTL synthesis and summarization. |
| Approach: | SYMDIREC decomposes RTL tasks into symbolic subgoals and assembles verified outputs . a neuro-symbolic framework supports both Verilog and VHDL without LLM fine-tuning . |
| Outcome: | SYMDIREC achieves higher Pass@1 rates for synthesis and 15–20% ROUGE-L improvements for summarization over prompting and RAG . synthesis, summarizing require preserving strict HDL syntax, modular structure, and precise functional semantics, authors show . |
Improving Neural Models for Radiology Report Retrieval with Lexicon-based Automated Annotation (2022.naacl-main)
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| Challenge: | Conventional exact or approximate termbased retrieval methods lack the ability of semantic understanding of the clinical as well as language context. |
| Approach: | They combine clinical finding detection with supervised query match learning to train a model . findings are used as queries to train the Sentence-BERT model using triplet loss . |
| Outcome: | The proposed method outperforms existing methods on multiple retrieval benchmarks. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |