activity
20232026
most citedTrack-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

2 citations · 6 across the 25 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition

Ruichu Cai, Juntao Gan, Miao Mai +2

Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model's (LLM's) intr…

cs.CL2026

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

Zhifeng Hao, Zhongjie Chen, Junhao Lu +5

Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) hav…

cs.CL2026

IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction

Bingfeng Chen, Chenjie Qiu, Yifeng Xie +3

Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabilities of large language model…

cs.CL20262 cited

Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

Bingfeng Chen, Shaobin Shi, Yongqi Luo +3

Generative language models have shown significant potential in single-turn Text-to-SQL. However, their performance does not extend equivalently to multi-turn Text-to-SQL. This is p…

cs.CL2025

Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation

Zhifeng Hao, Qibin Song, Ruichu Cai +1

Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Model…

cs.CL2025

CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model

Qingwen Lin, Boyan Xu, Guimin Hu +4

This paper introduces the Constrained Monte Carlo Tree Search (CMCTS) framework to enhance the mathematical reasoning capabilities of Large Language Models (LLM). By incorporating…