WebCiteS introduces attributed query-focused summarization (AQFS) for Chinese web search results. The dataset features human-annotated summaries with citations derived from real-world user queries and search results. Evaluation metrics distinguish groundedness errors and citation errors, highlighting the challenge of explicit attribution in large language models. Models struggle with accurate citations, but supervised fine-tuning improves both summarization utility and attribution quality. Long-context settings reduce model performance, especially in accurately pinpointing supporting evidence within the context.
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