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Works9 from public data
- Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery29
This work proposes SynCID, which harnesses the profound semantic comprehension of LLMs alongside the operational agility of SLMs for CID, and leverages the in-context learning strengths of LLMs to generate labels for new intents.
- Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery27
This work innovatively employs uncertainty propagation to select data samples from high-uncertainty regions, which are then labeled using LLMs through a comparison-based prompting scheme, which enhances accuracy in identifying new categories and introduces a soft feedback propagation mechanism to minimize the spread of inaccurate feedback.
- ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery21
This paper presents a novel approach called Cluster Semantic Enhanced Prompt Learning (CsePL) for discovering new intents that leverages two-level contrastive learning with label semantic alignment to learn meaningful representations of intent clusters.
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- Weaken Grammatical Error Influence in Chinese Grammatical Error Correction2
A Grammatical Error Weakening Module (GEWM) is proposed to impair the negative influence of grammatical errors in CGEC task by using learnable error weakening factors to control the proportion of contextual features and word features in the final representation of each word.
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- Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control1
This work constructs a large, high-quality dataset of formal and informal sentences, annotated across six linguistic aspects, and proposes a novel multi-agent framework where large language models (LLMs) act as expert agents for each linguistic aspect.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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