WebCiteS: Chinese Web Search Summarization with Citations

Core Concepts

The author introduces WebCiteS, a dataset for attributed query-focused summarization, highlighting the challenges faced by large language models in correctly citing sources and emphasizing the need for improvement.

Abstract

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.

Stats

WebCiteS features 7k human-annotated summaries with citations. Citation F1 score of top-performing model: 76.1%. Models perform worse on full web page content than snippets. Making documents more fine-grained leads to poorer attribution results.

Quotes

"Enhancing the attribution in large language models is a crucial task." "Our comprehensive evaluation highlights the challenge LLMs face in correctly citing sources."

Key Insights Distilled From

WebCiteS

by Haolin Deng,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01774.pdf

Deeper Inquiries

How can accurate citations enhance the credibility of generative search engines?

Accurate citations play a crucial role in enhancing the credibility of generative search engines by providing external evidence to support the claims made by the models. When a generative search engine generates responses with accurate citations, it allows users and developers to verify the information against the cited sources. This transparency not only increases trustworthiness but also helps in reducing hallucinations and factual errors that may occur in generated content. Accurate citations demonstrate that the information provided is grounded in reliable sources, making the generated content more trustworthy and credible.

What are the implications of different document settings on model performance?

Different document settings, such as using snippets versus full content of web pages, can have significant implications on model performance in attributed query-focused summarization tasks. When models are provided with snippets instead of full content, they may struggle with synthesizing pertinent information accurately due to limited context. On the other hand, using full content provides more comprehensive information but challenges models' ability to pinpoint exact supporting evidence within lengthy documents. Moreover, varying document granularity by chunking web pages into smaller or larger segments can impact attribution accuracy. Smaller chunks may lead to better citation precision but could result in lower citation recall and overall attribution quality. Larger chunks might improve contextual grounding but make it harder for models to identify specific supporting evidence accurately. In summary, different document settings affect how well models synthesize information from multiple sources and attribute their generations correctly. Finding a balance between context length and granularity is essential for optimizing model performance in attributed query-focused summarization tasks.

How can fine-grained verification improve attribution accuracy?

Fine-grained verification plays a critical role in improving attribution accuracy by allowing for detailed analysis of how well model-generated sentences align with their cited sources. By decomposing sentences into sub-claims through claim-splitting techniques and verifying each sub-claim against source documents individually, fine-grained verification enables detection of partial support instances where only part of a sentence is supported by references. This approach enhances attribution accuracy by distinguishing between groundedness errors (contextual support) and citation errors (inaccurate or missing citations). Models that undergo fine-grained verification are better equipped to identify when specific claims lack proper backing from external sources or when there are discrepancies between what was generated and what should be supported by citations. Overall, fine-grained verification ensures that attributions are precise, thorough, and aligned with source materials—ultimately leading to more reliable generative search engine outputs with enhanced credibility through accurate referencing.