Attribute First, then Generate: Locally-attributable Grounded Text Generation (2024)

Abstract: Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. Yet, these citations often point to entire documents or paragraphs, burdening users with extensive verification work. In this paper, we introduce a locally-attributable text generation approach, prioritizing concise attributions. Our method, named “Attribute First, then Generate“, breaks down the conventional end-to-end generation process into three intuitive steps: content selection, sentence planning, and sequential sentence generation. By initially identifying relevant source segments (“select first“) and then conditioning the generation process on them (“then generate“), we ensure these segments also act as the output’s fine-grained attributions (“select“ becomes “attribute“). Tested on Multi-document Summarization and Long-form Question-answering, our method not only yields more concise citations than the baselines but also maintains - and in some cases enhances - both generation quality and attribution accuracy. Furthermore, it significantly reduces the time required for fact verification by human assessors.

## introduction grounded text generation, which includes tasks like summarization and question-answering, aims to produce content that is derived from specific sources, either user-provided or retrieved via retrieval mechanisms (e.g., rag; @xcite . to facilitate verification of models' adherence to these sources, recent years have seen a growing interest in attributed text generation @xcite , which aims to create text alongside supporting evidence. this method enhances models' credibility by enabling factuality verification. it also aids in detecting and addressing factual errors, a critical need given the observed frequency of such errors, termed "hallucinations", in model outputs compared to source texts @xcite . while these attributions are aimed to facilitate factuality evaluation by focusing people's attention on relevant supporting texts, current approaches often yield rather coarse attributions, pointing back to whole documents or paragraphs. such attributions, though better than having none, require human assessors to exhaustively sift through many irrelevant details in the cited content, resulting in a time-consuming and somewhat ineffective factchecking process. can we do better? in this work, we extend attributed text generation to also consider attribution conciseness, reformalizing the task as locally-attributable text generation (see section 2). we introduce a criterion for attributions to be precise, targeting only the most relevant text snippets, from specific sentences down to subsentence spans, while avoiding non-essential details. this criterion complements the full coverage requirement, which stipulates that every generated fact be backed by a cited snippet. as illustrated in fig. 1 , given a query and several documents, the model output includes citations to relevant spans within the source documents for each generated sentence instead of broadly attributing the entire documents @xcite . these spans represent the minimal set of relevant source snippets needed to cover the content of the output sentences. following our localization criterion, we propose a novel attribution-driven generation approach, aimed to ensure both conciseness and full coverage, termed "attribute first, then generate". rather than jointly producing text with citations @xcite or getting the attribution post-generation @xcite , we eavestroughs and gutters are essentially the same thing, with eavestroughs being a specific type of gutter that is installed at the edge of a roof [1a, 2a, 3a]. an eavestrough is a trough that is connected to the eaves of a building, which are the sections of the roof that hang over the wall, and it is used to direct rainwater away from the roof and towards a drainage system [2b, 3b]. what's the difference between eavestroughs and rain gutters? model output prompt … [a] they are more or less the same thing-gutters are simply a catch-all term for anything performing a similar function. that's why you have gutters near sidewalks and in bowling alleys. they collect water (or bowling balls) and move it elsewhere. in short, "eavestroughs" is just a word for a very specific type of gutter: the ones on your building's eaves. … ## locally-attributable grounded text generation the task of locally-attributable grounded text generation involves processing a given set of documents d to produce an output text y. the set of documents may either be provided by the output of a retrieval system or by the user. the output, consisting of sentences s 1 , . . . , s n , should both fulfill the base underlying task (e.g., summarizing for mds or generating a response for lfqa) and also include attributions c 1 , . . . , c n pointing back to supporting text from d. for these attributions to effectively facilitate factuality verification by human 2 3310 ## modeling this work aims to integrate source attribution with the content-grounded generation process, to achieve more targeted attribution. our approach is based on uncovering the inherent decision-making mechanisms involved in text generation. we first describe our suggested attribute first, then generate scheme in section 3.1. then, we propose two strategies for this framework's application: a prompt-based in-context learning approach (section 3.2) and fine-tuning designated components (section 3.3). for each approach, we provide a brief overview of our implementation strategy for the framework's phases, while a more in-depth elaboration can be found in appendix c. ## results and analyses we provide results of our automated evaluation in tables 1 and 2 and human evaluation in tables 3 and 4 . considering the resource-intensive aspects of human evaluation, we limit this assessment to the alce baseline and our paradigm's most efficient model, the icl variant attr. first cot , chosen for its optimal balance between task performance and attribution accuracy, as indicated by the automatic evaluation. we first note that our approach, which extracts focused spans from the input documents, leads to 6 3314 method fluency↑ ais↑ time (sec)↓ alce 4.7 89.3 47 attr. firsticl-cot 4.9 79.9 22 in terms of attribution accuracy, our method surpasses the alce baseline in the lfqa domain (both on the automatic autoais and the human ais metrics), while in mds, although alce shows higher accuracy, it leads to twice as many unattributed sentences. we also note that while in the mds setting our method may decrease attribution accuracy compared to alce's full-document citations, readers can always fall back to examining the full document if the provided highlights are insufficiently supporting. indeed, analyses of the human annotations revealed that around 42% of the 109 sentences deemed unsupported were partially supported, with the remainder of the attributing details frequently found in immediate proximity to the highlighted sections (usually within the same or adjacent sentences). 9 this observation indicates that while our sentence fusion step occasionally includes slightly more information than the selected highlights (later used as fine attributions), it mostly adds details from the surrounding context. 10 hence, while suggesting room for improvement in strict adherence to highlights, this does not significantly hinder fact-checking effectiveness. importantly, the promising results on attribution do not compromise the quality of outputs. our automatic analyses indicate minimal impact on summary quality in mds and even an improvement in lfqa, as confirmed by the rouge l and bertscore metrics. these results are further evident in our human evaluation, both in terms of the output's fluency and (for lfqa) in helpfulness for answering the query. notably, our in-context variants tend to produce higher-quality outputs than fine-tuned models. this apparent advantage, observed also in attribution accuracy, is likely due to gemini's stronger ability to generalize to longcontext settings, as well as its larger model size. overall, our experiments show that our approach leads to orders of magnitude shorter attributions, with little impact on the attribution accuracy and the output's quality. we leave further improvements in fine-tuning the individual designated components to future research. ## conclusion in this work, we present "attribute first, then generate", a novel scheme for attributable grounded text generation, comprised of separating the generation process into multiple steps, such that attribution is achieved as a by-product of the generation. to the best of our knowledge, we are the first to provide such fine-grained attributions in both the generated text and the references. through automatic and manual evaluations, we showed that our decomposed generation pipeline either matches or outperforms existing baselines in terms of taskspecific metrics. importantly, our approach yields orders of magnitude shorter attributions, shortening the fact-checking time by almost 50%, with little impact on the attribution accuracy. while this work focused on a particular implementation of content selection and generation, we encourage future explorations to extend our "attribute first, then generate" paradigm to other configurations. we hope that our research will motivate future work in this direction, improving the quality of text generation while also providing helpful concise attributions to external sources, thereby increasing models' trustworthiness and safe downstream use.