Verifiable Generation with Subsentence-Level Fine-Grained Citations (2024)

Abstract: Verifiable generation requires large language models (LLMs) to cite source documents supporting their outputs, thereby improve output transparency and trustworthiness. Yet, previous work mainly targets the generation of sentence-level citations, lacking specificity about which parts of a sentence are backed by the cited sources. This work studies verifiable generation with subsentence-level fine-grained citations for more precise location of generated content supported by the cited sources. We first present a dataset, SCiFi, comprising 10K Wikipedia paragraphs with subsentence-level citations. Each paragraph is paired with a set of candidate source documents for citation and a query that triggers the generation of the paragraph content. On SCiFi, we evaluate the performance of state-of-the-art LLMs and strategies for processing long documents designed for these models. Our experiment results reveals key factors that could enhance the quality of citations, including the expansion of the source documents’ context accessible to the models and the implementation of specialized model tuning.

## introduction large language models (llms) have demonstrated remarkable capabilities in seeking and synthesizing information from given documents @xcite , and many llmpowered tools are available to the general public. however, concerns have emerged regarding their outputs' factual accuracy, faithfulness to the source documents, and trustworthiness in general @xcite . to address these concerns, recent research has introduced a new generation paradigm, verifiable generation, where llms are required to include citations to source documents in the model outputs, to support their statements @xcite . verifiable generation enables users to trace the information back to its source and verify its correctness, enhancing the transparency and reliability of models. nonetheless, existing work typically provides sentence-level citations. they are unable to indicate which part of the sentence is supported by each referenced source document, leaving the effort to users to infer the connection between the content and its citation @xcite . this ambiguity hinders the user's ability to verify the information efficiently and understand the scope of the supporting documents. we argue that verifiable generation with a finer granularity is critical for further improving the transparency and trustworthiness of llms. compared to sentence-level citations, fine-grained citations, such as subsentence-level annotations, allow for more precise localization of the informa-tion that is sourced from the referenced document and support easier assessment of its accuracy, as shown in figure 1 . furthermore, the importance of fine-grained citations to readers is quantitatively evinced by their prevalence in the popular wikipedia pages. for instance, pages that are more frequently viewed are more likely to use citations with finer granularity, as demonstrated in figure 2 . in this work, we aim to investigate llms' ability of producing output with fine-grained citations. specifically, we focus on subsentence-level citations, which are commonly used in informationrich documents like encyclopedias and research papers. to facilitate the study, we first collect scifi, containing subsentence-level citation of fine granularity based on 10k paragraphs from wikipedia, where rich citation information is available. for each paragraph in scifi, documents that are cited in the wikipedia page where the paragraph belongs to are provided as the citation candidates. we also create a query based on the content of each paragraph to guide llms to generate relevant content. we benchmark state-of-the-art llms on our dataset, including openai gpt @xcite , llama2 @xcite , @xcite , and mistral @xcite . to consume the lengthy source documents, we explore three document reading strategies, that respectively target leading context of all source documents, a large portion of the context of all source documents, and full context of selected source documents. for open-source llms, we also examine the effect of fine-tuning with the training samples in our dataset. for evaluation, we assess the citation behavior, citation quality, and answer quality of model output. we find that: ( 1 ) complete source document context improves citation quality in llms; (2) larger model sizes increase answer quality but not citation quality; (3) fine-grained citation generation requires supervised fine-tuning. our contributions are summarized as follows: (1) we collect scifi which consists of 10k queries paired with reference answers that are rich in subsentence-level fine-grained citations to the source documents, enabling training models for verifiable generation with finer granularity. (2) we analyze performance of state-of-the-art llms augmented with various document processing strategies on scifi, highlighting directions that could advance model development for verifiable generation. fact-based evaluation. evaluation of outputs produced by llms is challenging due to their openended nature. recent work resorts to fact-based evaluation, where an output is first decomposed into independent facts and compared against facts in the reference output @xcite . to circumvent data collection for fact decomposition model training, llms have been instructed to extract facts from the output to be evaluated @xcite . furthermore, @xcite leverage llms' strong capability of identifying content with similar semantics and design an llm-based fact comparison module for more accurate evaluation. while previous work utilizes fact-based framework for evaluation of factual entailment and precision, we assess both answer coverage and citation quality with fact-based evaluation. ## scifi to benchmark llms on verifiable generation with finer granularity, we first collect scifi, a dataset containing questions that require synthesizing information from multiple source documents to answer and demand more fine-grained citations to precisely locate information supported by different source documents. scifi is based on wikipedia, as wikipedia articles may densely include citations to support their content. before data collection, we examine how prevalent fine-grained citations are used in wikipedia articles. out of 100k randomly picked wikipedia articles, 85% feature subsentence-level citations. we further sort these articles based on their pageview statistics and divide them into 20 bins. 2 for each article, we tallied the total number of views over a five-year period to minimize the impact of transient trends. as can be seen from figure 2 , bins with higher pageview counts show a greater tendency of fine-grained citations, highlighting their importance to readers. ## experiment setups task setup. given a sample in scifi, the llm to be tested takes as input the query and candidate source documents to generate a paragraph with fine-grained citations. due to the sheer volume and excessive length of all available source documents, it is impractical to input them into the llm simultaneously. therefore, we provide the llm with positive source documents-those cited by the reference paragraph, and 5 randomly selected negative source documents that are not cited by the reference paragraph. we shuffle these source documents before feeding them into the llm. ## results source documents with more complete context benefit citation quality, as indicated by the factlevel entailment scores of citations produced by different document reading strategies with in-context learning ( ## conclusions we study verifiable generation with subsentencelevel fine-grained citations. scifi, a benchmark containing 10k subsentence-level citation-rich paragraphs together with candidate cited sources and queries, is collected to support the training and evaluation of models on this task. on scifi, experiments with state-of-the-art llms and various processing strategies demonstrate the importance of source document context and training with citation-rich data.