4 citations · 10 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
What Gets Activated: Uncovering Domain and Driver Experts in MoE Language Models
Guimin Hu, Meng Li, Qiwei Peng +3
Most interpretability work focuses on layer- or neuron-level mechanisms in Transformers, leaving expert-level behavior in MoE LLMs underexplored. Motivated by functional specializa…
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…