Papers by Chengxiang Zhuo

2 papers
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)

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Challenge: In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL.
Approach: They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition.
Outcome: The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches.
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation (2026.acl-industry)

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Challenge: a new approach to news recommendation grounds each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable keywords.
Approach: They propose an intent-driven Semantic ID generation paradigm to address these challenges . they map diverse intents to hierarchical SID prefixes and then fuzzy-match them to current news pool .
Outcome: The proposed model achieves 0% hallucination and 12.4% L1 match on a mainstream Chinese news platform.

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