WebCiteS addresses limitations in attribution evaluation by providing a dataset with human-annotated summaries and citations. The work emphasizes the importance of accurate citations and contextual grounding in improving model performance.
Enhancing attribution in large language models is crucial for credibility. Existing datasets lack high-quality citation annotations, hindering model training. The study introduces WebCiteS to address these limitations and evaluates models on both open-source and proprietary platforms.
The research focuses on attributed query-focused summarization, presenting a comprehensive evaluation framework that distinguishes between groundedness errors and citation errors. The study reveals challenges faced by models in correctly citing sources and emphasizes the necessity for further optimization.
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arxiv.org
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