Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024)

Abstract: Teaching large language models (LLMs) to generate text with attribution to evidence sources can reduce hallucinations, improve verifiability in question answering systems (QA), and increase reliability of retrieval augmented LLMs. Despite gaining increasing popularity for usage in QA systems and search engines, current LLMs struggle with attribution for long-form responses which require reasoning over multiple evidence sources. To address this, in this paper we aim to improve the attribution capability of LLMs for long-form answer generation to multiple sources, with multiple citations per sentence. However, data for training multi-source attributable QA systems is difficult and expensive to annotate, and therefore scarce. To overcome this challenge, we transform existing QA datasets for this task (MultiAttr), and empirically demonstrate, on a wide range of attribution benchmark datasets, that fine-tuning on MultiAttr provides significant improvements over training only on the target QA domain. Lastly, to fill a gap in existing benchmarks, we present a multi-source attribution dataset containing multi-paragraph answers, PolitiICite, based on PolitiFact articles that discuss events closely related to implementation statuses of election promises.

## introduction large language models (llms) are one of the most popular tools for a variety of nlp tasks, including question answering (qa). however, a number of issues have arisen as research in llms continues, notably their tendency to hallucinate facts despite otherwise fluent generations @xcite . several strategies have been proposed to eliminate this shortcoming, and in this work, we focus on retrieval-augmented generation (rag), and particularly an important adjacent area of attributed qa @xcite , i.e., to cite the sources used for writing the llm's responses. the ability to cite evidence sources accurately can help to improve model development and debugging, explainability of generations, and more importantly the end-user trust on the llm outputs @xcite @xcite . while the attribution problem in general is nontrivial @xcite , the multi-source attribution task is particularly challenging as multiple retrieval sources are necessary to support statements in a long response (see figure 1 for an example). this is exemplified by the citation quality evaluations done by @xcite , where they show that even the popular generative search engines (e.g., bing and perplexity.ai) which support multi-source reasoning, have just 52% of statements to be fully supported by their citations, and only 75% of citations supporting the statement. so far, existing works @xcite for this task have either utilized a multi-task learning setup by jointly predicting the response and in-line citations @xcite @xcite or perform source attribution post generating the responses @xcite . one shortcoming of the former is that they predominantly deal with commercial search engines, and are primarily for single-source attribution per statement. the shortcoming of the latter is that while they perform well on singlesource attribution, they fail to perform well on attribution prediction in human-annotated multi-source attribution datasets. a major challenge for multi-source attribution is lack of availability of rag training data with citations, as data-efficient methods like few-shot prompting underperform @xcite . in this paper, we address these challenges by instead using few-shot prompting to automatically transform existing qa datasets to generate training data for multi-source attribution. finally, all the existing human-annotated longform attribution datasets @xcite @xcite focus primarily on single source attribution, or at most, contain answers that are one paragraph in length. to address this, we present politicite, a multi-paragraph and multi-source attribution qa benchmark dataset sourced from politifact, foot_0 with 428 long, expertwritten articles that analyze to what extent a politician has kept a promise. politics is not just a domain with rich text-data, but also where text forms a crucial data-source to improve accountability in a society @xcite , and the dataset lies at the core of this purpose. we ask human annotators to retrieve evidence snippets from the cited sources and indicate the degree to which the evidence supports a statement. as politicite provides gold evidences, it allows the study of attribution independent of the retrieval quality. improving attribution quality is important in the political domain, which is rampant with misinformation, and can directly improve the public trust on llm-generated text. we summarize our paper's contributions as follows: ## multi-source attribution in qa first, we introduce some terminology we use for the remainder of the paper. a source (s i ) is a text segment (boundaries dependent on the chunking strategies employed), obtained using any suitable retrieval approach, which we assume to be true. in the retrieval augmented generation (rag) setting @xcite , given a query q, a generative qa model generates a response r which is made up of one or more verifiable statements (r i , @xmath0, grounded on the top-k evidence sources. for multi-source attributed qa, we want the individual statements (r i ) to be attributed to one or more sources, i.e., we define a function ϕ(s, r i ) → {0, 1} which takes a set of sources s (@xmath1, s g , which entail r i , and 0 otherwise. we denote the generated response with in-line attributions as r c . ## multiattr data in this section, we describe our method for creating multiattr by transforming existing qa datasets with attributions, targeting multi-source attribution in long-form answers. there can be two possible scenarios in multi-source attribution: an answer may require multiple sources to support it or multiple sources can support a claim independently. we consider both scenarios in this work. our proposed strategies are: (a) given a question, multiple retrieved sources and a short response as in the case of hotpotqa @xcite , we use few-shot prompting similar to @xcite to expand the short responses into long answers, and (b) given a attributed qa dataset with singlesource citations, as found in the naturalquestions @xcite , we propose a few-shot prompting strategy to augment additional positive and negative evidence sources. in this paper, we demonstrate this approach focusing on two datasets, but this can easily be extended to more qa datasets using a combination of these prompts, to generate large-scale repurposed multi-source attribution datasets, which we leave for future work. expanding short to long answers each example in hotpotqa consists of a question, a short answer, and ten source paragraphs, of which two contain information necessary to correctly answer the question. we use a few-shot prompting approach (similar to yue et al. ( 2023 )) with llama-2 chat (13b), to generate longer responses with attributions. see §a.1 for the exact prompt used for generation and example outputs. in addition, the responses often incorporate additional information from the sources to create a more informative answer as shown in figure 1 . ## experiments in this section, we describe our experimental settings, followed by a discussion of the results. ## politicite annotations we collected 1412 truth-o-meter promise articles from politifact, and filtered out articles having fewer than five citations, which yields 428 articles. we split articles by paragraphs, using those with at least one cited source, and select each sentence as a claim (4,839 sentences out of 10,685 total). we employed crowdworkers from amazon mechanical turk to visit the cited sources and retrieve an evidence snippet which supports the claim. many cited sources are not necessary for support, but rather to provide context or background information on a topic. to address this, we additionally ask annotators to indicate whether the retrieved evidence (1) completely supports, (2) partially supports, or (3) is relevant to but doesn't support the claim. annotators were allowed to add up to five evidences snippets per claim. each example was annotated by three annotators with conflicts resolved by a majority vote. annotations had a bleu score of 37.2 between annotators, indicating a high agreement in the choice of evidence snippets. overall statistics of the dataset is given in sources: [1] https://www.consumerfinance.gov/about-us the consumer financial protection bureau is a 21st century agency that implements and enforces federal financial law and ensures that markets for consumer [...] [8] https://www.cato.org/people/dan-quan dan quan is an adjunct scholar at the cato institute's center for monetary and financial alternatives, [...] question (promise): what is the status of biden's promise to create a public credit reporting agency? answer (article): president joe biden's campaign proposal to create a new agency within the consumer financial protection bureau to provide credit scores has not been successful. the consumer financial protection bureau [1], which implements and enforces federal consumer financial law, told politifact it has "taken significant actions" to help consumers combat coercion through increased guidance to the consumer reporting industry [...] prediction models perform poorly on this dataset, with the former achieving 9.3% f1 and the latter at 11.3% f1 at best. similarly, we find that our best-performing in-line generation models are very poor on this dataset, in large part due to their preference for short answers, at an average of just 92 words (compared to 553 for gold answers). this is apparent in the claim recall and article-level f1, which both require longer answers to improve, and are currently (at best) just 6.1% and 4.4% respectively. even zero-shot inference using chatgpt only has 15% claim recall and 12% article-level f1, and achieves comparable sentence-level f1 to our fine-tuned models. the low performance of best-performing existing models on this dataset highlights the gap area in attribution performance, and positions politicite as a challenging benchmark for this task. moreover, this emphasizes the need for target-domain training data, and training the attribution models suitably, which we leave for future work. we believe politicite can prove to be a useful resource for benchmarking newer improved models and research in the field of multi-source attribution of long-form answers in the qa community. ## conclusion in this paper we have studied the increasingly important problem of attribution in long-form qa, specifically for multiple sources in a rag setting. we show that single-source nli models, despite otherwise being strong baselines, under perform on multi-source attribution qa datasets. we investigate fine-tuning multi-source attribution prediction models, and address the lack of training data by automatically transforming existing qa datasets for this task via few-shot prompting. we show pre-training on the multiattr significant performance improvements on the target domain, in both post-generation attribution prediction and in-line answer-citation generation settings. finally, we observe a limitation in existing attributed qa benchmarks, in that they deal with at most one paragraph, and have a limited number of citations. to fill this gap, we present politicite, the first multi-paragraph, multi-source attributed qa dataset, in which expert-written articles from politifact analyze the extent to which a politician has kept a promise. our best models perform very poorly on politicite, indicating that the length and high-citation counts pose a significant but important challenge. improving performance on politicite will require both target domain training data and improvements in long-form attributed qa including models' ability to understand paragraph structure and use dependencies between cited sources.