Learning to Plan and Generate Text with Citations (2024)

Abstract: The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence. In this paper, we explore the attribution capabilities of plan-based models which have been recently shown to improve the faithfulness, grounding, and controllability of generated text. We conceptualize plans as a sequence of questions which serve as blueprints of the generated content and its organization. We propose two attribution models that utilize different variants of blueprints, an abstractive model where questions are generated from scratch, and an extractive model where questions are copied from the input. Experiments on long-form question-answering show that planning consistently improves attribution quality. Moreover, the citations generated by blueprint models are more accurate compared to those obtained from LLM-based pipelines lacking a planning component.

## introduction large language models (llms) have demonstrated remarkable abilities to engage in creative conversations @xcite , summarize information from contextual cues @xcite , and deliver zeroshot performance on a wide range of previously unseen predictive and generative tasks @xcite . they are also becoming increasingly useful in information-seeking scenarios, ranging from answering simple questions @xcite to generating responses to search-like queries @xcite . the increasing demand for the deployment of llms in information-seeking scenarios has further spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence. the evidence can take the form of a url pointing to a short segment of text which supports an answer @xcite , an attribution report with evidence snippets @xcite , quotes cited verbatim from pages retrieved from a search engine @xcite , and references to passages extracted while browsing @xcite . in fact, this last type of evidence has been recently adopted in the form of in-line citations by commercial search engines such as bingchat foot_0 and perplexity.ai foot_1 . regardless of how the evidence is presented, recent approaches tend to rely on a retrieval system (e.g., a commercial search engine) to obtain passages relevant to a query, while an llm conditions on them to generate a response @xcite @xcite . other work generates an answer to the input query first and subsequently retrieves relevant evidence in a post-processing step @xcite . alternatively, the retrieved evidence can be used to further revise the generated response rendering it more consistent with the evidence @xcite . despite recent efforts, it remains an open question how to best develop models with a built-in mechanism for attribution to external evidence. a related question is whether said mechanism contributes to generating more factually faithful output. large-scale evaluation studies paint a worrying picture. @xcite find that long-form responses from existing search engines frequently contain unsupported statements or inaccurate citations, while @xcite show that model performance on attribution varies greatly (between 46% and 71%) across different architectures for the simpler question answering task. in this paper, we explore the attribution capabilities of plan-based models which have been 🔍 figure 1 : query (top), followed by most relevant (abridged) passages, and summaries (bottom) with in-line citations. @xcite is the output of a vanilla sequence-to-sequence model trained to generate long answers with citations. summaries (b) and (c) are the output of models with abstractive and extractive plans, respectively. citations for plan-based models can have different formats (e.g., references to the question plan; see section 4.2). our models conceptualize text plans as a sequence of questions operating as blueprints for generation, determining what to say and in which order @xcite @xcite . questions as a planning mechanism are ideally suited to attribution, since they provide a natural link between retrieved passages and their summaries. we define two models which differ on whether the question-based plan is generated (see figure 1(b) ) or copied from input passages (figure 1(c) ) and explore whether explicit planning has any bearing on citation quality. our contributions can be summarized as follows: ## related work llms have made significant advances in recent years in generating high quality natural language responses @xcite . despite impressive capabilities, they also demonstrate high levels of opacity: it is unclear where the information they generate comes from, which can undermine their trustworthiness @xcite . a variety of techniques aim to remedy this by building language models that provide references to support generated text. the majority of existing work has focused on models which learn to generate citations. for example, @xcite and @xcite use reinforcement learning from human preferences to train language models to answer questions and provide supporting evidence (in the form of retrieved snippets). @xcite generate both an answer and a url to a web page from which a paragraph is subsequently selected as support for the answer. they also experiment with attribution as a post-processing step where no learning is involved and use retrieval to select sources supporting their model's output. along similar lines, @xcite propose a two-stage approach where generated text is first post-edited to be made attributable (to web content) and then revised accordingly. in addition to modeling, efforts have been also directed to the creation of evaluation protocols and benchmarks for assessing whether a statement is supported by provided evidence @xcite @xcite . our work focuses on plan-based models which have been shown to be less opaque and more ## problem formulation we follow a formulation of query-focused summarization common in the literature @xcite @xcite . let q denote an information-seeking request (e.g., "what is so special about the pulmonary arteries and veins?" in figure 1 ) and @xmath0, . . . , p n } be a set of passages most relevant to q (see top left in figure 1 ). we adopt a two-step approach where k most relevant passages are first retrieved and possibly reranked based on the query alone, and then fed to a secondary model which synthesizes the passages into a final summary @xmath1, . . . , s m } consisting of sentences s i . an attributed summary further includes supporting evidence. for the sake of simplicity, we assume this is expressed by embedded in-line citations (see bottom left in figure 1 ). each s i has a set of citations c @xmath2,1 , . . . , c i,k } where c i,j is a passage id signifying that sentence i is citing said passage. for the summary (a) in figure 1 , c 1 = {[1]}, and c 2 = {[2]}. in the following we first explain how to train a sequence-to-sequence model to generate citations, and then describe the proposed plan-based models. ## conclusion in this paper we focused on retrieval augmented generation with citations. we explored the attribution capabilities of plan-based models and proposed different variants depending on how the plans are created (i.e., abstractively or extractively). our experiments revealed several interesting find-ings. summary quality improves for blueprint models when these learn to generate citations, both in terms of relevance and faithfulness. conversely, this is not the case for models without an intermediate planning stage, in fact attribution slightly hurts performance (see second line in