Papers by Zihao Wen

3 papers
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)

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Challenge: Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation .
Approach: They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy .
Outcome: The proposed method improves performance in schema linking and grammar correctness . it also establishes new state-of-the-art on the WikiSQL benchmark .
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)

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Challenge: Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored.
Approach: They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning.
Outcome: The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%.
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)

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Challenge: Existing methods for retrieving historical messages are based on similarity-based mechanisms.
Approach: They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection.
Outcome: The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S.

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