Papers by Ruichu Cai
SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition (2026.findings-acl)
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| Challenge: | Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions misalign with a large language model’s intrinsic semantic organization. |
| Approach: | They propose a framework that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. |
| Outcome: | Experiments on the CrossNER benchmark show that SAM-NER consistently outperforms strong prior ZS-NER baselines in cross-domain settings. |
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL (2025.naacl-long)
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| Challenge: | Existing approaches to generative language models struggle to handle the increasing complexity of multi-turn Text-to-SQL tasks. |
| Approach: | They propose a framework which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SparC and CoSQL datasets and significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55%. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |
S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024.acl-long)
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| Challenge: | Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures. |
| Approach: | They propose a graph-based framework that incorporates semantic and syntactic information simultaneously within global structures. |
| Outcome: | The proposed framework removes irrelevant contexts and syntactic dependencies and achieves complementarity across diverse structures. |
TAG : Type Auxiliary Guiding for Code Comment Generation (2020.acl-main)
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| Challenge: | Existing code comment generation approaches ignore type information of interpretation of the code, e.g., operator, string, etc. Existing approaches ignore the type information due to the hierarchical dependence among the type. |
| Approach: | They propose an encoder-decoder framework which considers the source code as an N-ary tree with type information associated with each node. |
| Outcome: | The proposed framework is based on a Type Auxiliary Guiding encoder-decoder framework and a type-restricted Decoder to resolve training difficulties. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |
𝒮2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction (2025.findings-naacl)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) is an extractive task that focuses on predicting tuples of sentiment-related elements from a given text. |
| Approach: | They propose a stepwise syntax integration tuning framework that integrates syntactic structure knowledge into LLMs through a multi-step tuning process. |
| Outcome: | The proposed framework integrates syntactic structure knowledge into large language models . it decomposes the quadruple generation task into two stages . the proposed framework significantly improves state-of-the-art performance across multiple datasets . |
Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation (2025.naacl-long)
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| Challenge: | Zero-shot Named Entity Recognition (ZS-NER) aims to recognize entities in unseen domains without specific annotated data. |
| Approach: | They propose a novel two-stage framework leveraging large language model techniques to improve the ZS-NER’s recall rate. |
| Outcome: | The proposed framework improves the ZS-NER’s recall rate and accuracy by incorporating a large language model. |
GenLink: Generation-Driven Schema-Linking via Multi-Model Learning for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Experimental results on BIRD and Spider benchmarks validate the effectiveness of GenLink. |
| Approach: | They propose a generation-driven schema-linking framework based on multi-model learning . experimental results validate the effectiveness of GenLink . |
| Outcome: | Experimental results show that GenLink improves schema-linking recall rate and cross-domain adaptability. |