Incorporating Task-specific Concept Knowledge into Script Learning
원문 초록
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes not only the goal but also additional user context, including preferences and history. To address this problem, we propose a novel approach, which uses two techniques to improve performance: (1) concept prompting, and (2) script-oriented contrastive learning that addresses step repetition and hallucination problems. On our WikiHow-based dataset, we find that both methods improve performance. The dataset, repository, and models will be publicly available to facilitate further research on this new task.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Mitigating Object Hallucinations via Sentence-Level Early Intervention
RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
UQLM: A Python Package for Uncertainty Quantification in Large Language Models
CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.