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Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
This work revisits the role of prompt engineering in personalized recommendation tasks. The paper comprehensively evaluates various …
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LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries
The paper investigates limitations of LLM-based query expansion methods when facing ambiguous or unfamiliar queries. Through …
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Relevance, Diversity, and Exclusivity: Designing Keyword-augmentation Strategy for Zero-shot Classifiers
This paper explores how to balance relevance, diversity, and exclusivity in keyword-based zero-shot classifiers. The authors propose an …
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Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
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Low-resource Taxonomy Enrichment with Pretrained Language Models
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Low-resource Taxonomy Enrichment with Pretrained Language Models
Quality Control for Hierarchical Classification with Incomplete Annotations
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Learning with Unsure Responses
We propose a novel alogrithm for utilizing unsure labels for instances from annotators to find optimal parameters quickly.
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Learning with Unsure Responses
Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables
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Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables