Papers by Xuanting Cai
A Web Scale Entity Extraction System (2021.findings-emnlp)
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| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
FaithLM: Towards Faithful Explanations for Large Language Models (2026.eacl-long)
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Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Andrew Wen, Mengnan Du, Xuanting Cai, Vladimir Braverman, Xia Hu
| Challenge: | Large language models (LLMs) produce natural language explanations, but they lack faithfulness and do not reflect the evidence the model uses to decide. |
| Approach: | They propose a model-agnostic framework that evaluates and improves the faithfulness of LLM explanations without token masking or task-specific heuristics. |
| Outcome: | The proposed framework improves faithfulness of large language models without masking or heuristics. |
A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025.findings-emnlp)
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| Challenge: | Existing models for text style transfer struggle with complex styles . existing models perform well on simple styles like sentiment and formality . |
| Approach: | They propose a multi-agent self-check framework that includes a large language model as a planner for disentangling subtasks and expert agents for executing the subtask. |
| Outcome: | The proposed framework significantly improves style strength and content preservation on simple and complex style datasets. |
Secure Your Model: An Effective Key Prompt Protection Mechanism for Large Language Models (2024.findings-naacl)
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| Challenge: | Recent years have seen an unprecedented surge in the development and application of large language models (LLMs) however, the development of LLMs is a complex endeavor, requiring substantial investments in terms of financial and computational resources. |
| Approach: | They propose a mechanism wherein a unique key prompt is embedded within the LLM to protect it from unauthorized access and potential theft. |
| Outcome: | The proposed protection can protect the model without significantly impacting its original function. |