Generating Biographies on W ikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies (2022)

Abstract: Generating factual, long-form text such as Wikipedia articles raises three key challenges: how to gather relevant evidence, how to structure information into well-formed text, and how to ensure that the generated text is factually correct. We address these by developing a model for English text that uses a retrieval mechanism to identify relevant supporting information on the web and a cache-based pre-trained encoder-decoder to generate long-form biographies section by section, including citation information. To assess the impact of available web evidence on the output text, we compare the performance of our approach when generating biographies about women (for which less information is available on the web) vs. biographies generally. To this end, we curate a dataset of 1,500 biographies about women. We analyze our generated text to understand how differences in available web evidence data affect generation. We evaluate the factuality, fluency, and quality of the generated texts using automatic metrics and human evaluation. We hope that these techniques can be used as a starting point for human writers, to aid in reducing the complexity inherent in the creation of long-form, factual text.

## introduction wikipedia has become one of the major sources of dissemination of knowledge across the globe. however, the knowledge contained in wikipedia is not neutral -it is biased in various ways @xcite . many studies, including those from the wikimedia foundation itself, have emphasized that biographies in particular are overwhelmingly written about men. this leads to many subtle yet far-reaching effects, from students not writing their first book reports on a woman to bias in models trained on wikipedia, as wikipedia has long been used as a source of data. many existing efforts, such as the wikipedia women in red project, focus on encouraging article creation to mitigate this gender gap. however, wikipedia articles remain painstakingly written and edited primarily by a network of human contributors. despite advances in text generation and modeling architectures that retrieve information, the automatic creation of wikipedia articles is incredibly challenging @xcite . even the functionality of tools that aid human editors are limited. in this work, we strive to create a system that could write an entire wikipedia article in english, focusing on the biography domain. we confront several major challenges. first, this is fundamentally a long-form generation task. improvements driven by pretraining @xcite have improved generation fluency at the level of multiple sentences. however, wikipedia biographies contain multiple paragraphs in a structured form with headings, as well as citations to indicate where the information originated from. second, the task confronts obstacles around the factuality @xcite of generated content, as articles must be factually accurate. third, wikipedia articles are written using reference material, often found on the web @xcite . thus, models need to find and ingest web searches as a pre-requisite to writing accurate biographies. we develop a method for english wikipedia that starts with the subject and occupation of the biography, then leverages web search to find relevant evidence. given search results, we employ a retrievalaugmented generation architecture @xcite based on large-scale pretraining to identify relevant information and write the biography. we generate section by section, using a caching mechanism similar to transformer-xl @xcite to reference previous sections and achieve greater document-level context. finally, after each section, we append a citation based on which web searches were retrieved. we quantify the quality of generation using several automatic metrics such as rouge-l @xcite , entailment, and named entity coverage. further, we study the strong dependency of our method on accurate retrieval, and design a specific evaluation dataset that highlights this challenge. the dataset consists of 1,527 wikipedia biographies about women, where information on the internet is not as easily retrieved. we use this dataset to analyze the gap between model quality when retrieval is challenging (our novel evaluation dataset with biographies about women) and model quality when retrieval is more accurate (a random set of evaluation biographies). finally, we conduct a large-scale human evaluation to measure the factuality and coverage of our generated biographies. we hope that our techniques can eventually be used as a starting point for human wikipedia writers, for biographies and beyond. ## task given a person's name, one or more occupation(s), and commoncrawl as a source of evidence, the task is to generate a wikipedia biography and to associate each generated section with adequate bibliographic references. we model this task by generating a biography section by section using section headers as additional information. a special section header called toplevel is used as the start of the article. the subsequent headers are automatically generated at the end of each section as input for the next. thus for each section, the input includes a name, one or more occupations, a section header, and commoncrawl as a retrieval corpus. ## method wikipedia biographies begin with an introductory paragraph followed by various subsections foot_2 . to account for this structure and generate long-form text based on retrieved web evidence, our system, illustrated in figure 1 , generates a biography section by section. based on the subject, their occupation(s), and the section heading, the model first identifies a subset of relevant evidence from a set of web search results found using that triplet (retrieval module). it then conditions upon that evidence to generate the section, using a sequence-to-sequence model (generation module) which can access previous sections using a caching mechanism. finally, the model indicates which evidence documents it used and outputs those as citations, mimicking a standard wikipedia article (citation module). we focus on generation in english. ## creating an evaluation dataset a possible failure point for our method is the retrieval step as good biography generation requires access to sufficient relevant information. to study the impact of accurate retrieval on generation quality, we design a specific evaluation dataset that pushes this problem to the forefront. specifically, we create a novel evaluation dataset which consists exclusively of biographies about women. ongoing efforts to write biographies about women in the wikipedia editor community, such as the women in red project, have identified insufficient online evidence as a major challenge for writing wikipedia biographies about women. to study the importance of retrieval on model quality, we therefore create an evaluation dataset where the target wikipedia articles are women bios. we collate candidate biographies, retrieve information about their occupation, and gather web sources using web search. the resulting dataset, summarized in ## experimental details we describe our training data, baselines, and automatic and human evaluation metrics. training data. we utilize the wikisum @xcite dataset of wikipedia articles paired with web references. we filter to biographies using a combination of querying for occupations in wikidata and using named entity recognition foot_6 to recognize names. we query each article title in the wikisum dataset to attempt to find an occupation and see the title is recognized as a named entity, to identify the bibliographical subset of wikisum. this produces 677,085 biographies, each associated with a set of web articles. evaluation data. we utilize the wikisum @xcite dataset, filtered to biographies, for evaluation. similar to the training dataset, we query to identify occupational information. to study the impact of retrieval and available evidence on model baseline. we compare our method described in section 4 to a pretraining and finetuning generation baseline. we use the bart model @xcite and finetune on the biography subset of the wikisum data. note that bart has a token limit of 1024, thus the entirety of the web retrieval is not available to this model. we take the web search hits ordered by the search engine, and provide the first 1000 available tokens. to compare this baseline with our method equitably, the baseline is also trained to generate section by section. however, it does not use the retrieval module (all evidence is given), the caching mechanism, or the citation module (as described in section 4), meaning citations are not added to the generated text. additional training details are in the appendix. ## results and discussion we describe our main results and analyze the importance of retrieval on model quality. an example generation is shown in figure 2 . next, we examine the impact of our modeling choices using ablation ( we also generate the biographical articles section by section, rather than an entire article at once. this allows the retrieval mechanism to be focused on the section information. as shown in human evaluation. next, we examine quality with human evaluation, as shown in figure 3 . models generating nonfactual or hallucinated content is an ongoing area of study @xcite @xcite . our goal is to understand how much information in the generated text is present in the reference text or the web evidence, as a proxy for factuality and coverage. overall, 68% of the information in generated sections is not present in the reference text. conversely, 71% of information in the reference text is not in the generated text. this indicates that the generated text has far from perfect coverage. however, we found that 17% of the added information can be validated by examining the web evidence, which shows that some information added by the generative model is valid biographical information. we examine why there is low information overlap between the generated and reference text. first, information in the reference biography may not be available on the web foot_8 or may not be retrieved. in a manually examined subset of 250 sentences taken from reference biographies, we found that about 50% of the information was not contained in the web evidence. the other 50% was partially present in the web evidence but were not retrieved by the model. second, annotators must compare sentences, but sentences contain partial information. for example, if person is was born in chicago in 1968 was in the generated text and person was born in chicago was in the reference text, this would count as the generation having information not in the reference. annotators were very precise in sticking to the requested standard that the entire sentence should be factual to count as fully factual, which is reflected by annotators marking partial in summary, our investigation suggests two explanations for the low coverage reported by human annotators: lack of information in the web evidence and difficulty assessing whether two sentences contain the same core knowledge. ## conclusion we developed a novel retrieval and cacheaugmented generative model to generate long-form biographies based on evidence from the web. experimental evidence reveals that an enriched query including occupations, caching, and backpropagation through the retrieval module contributes to improved performance. we investigate the dependency on high-quality web evidence, which manifests strongly in our constructed evaluation dataset of biographies about women. we discuss this challenge and possible mitigations.