diff --git a/tests/fixtures/2304.02623v1.json b/tests/fixtures/2304.02623v1.json index bdda366..e0177e7 100644 --- a/tests/fixtures/2304.02623v1.json +++ b/tests/fixtures/2304.02623v1.json @@ -1,113156 +1 @@ -{ - "symbols": "Beyond Summarization:\nDesigning AI Support for Real-World Expository Writing Tasks\nZejiang Shen \u2020 , Tal August \u25a1 , Pao Siangliulue \u25a1 , Kyle Lo \u25a1 , Jonathan Bragg \u25a1\nJeff Hammerbacher \u25a1 , Doug Downey \u25a1 , \u22c4 , Joseph Chee Chang \u25a1 , David Sontag \u2020\n\u2020 Massachusetts Institute of Technology, \u25a1 Allen Institute for AI, \u22c4 Northwestern University\n{zjshen,dsontag}@mit.edu,{tala,paos,kylel,jbragg,jeffhammerbacher,dougd,josephc}@allenai.org\nABSTRACT\nLarge language models have introduced exciting new opportunities\nand challenges in designing and developing new AI-assisted writing\nsupport tools. Recent work has shown that leveraging this new tech-\nnology can transform writing in many scenarios such as ideation\nduring creative writing, editing support, and summarization. How-\never, AI-supported expository writing \u2014including real-world tasks\nlike scholars writing literature reviews or doctors writing progress\nnotes\u2014is relatively understudied.\nIn this position paper, we argue that developing AI supports for\nexpository writing has unique and exciting research challenges and\ncan lead to high real-world impacts. We characterize expository\nwriting as evidence-based and knowledge-generating: it contains\nsummaries of external documents as well as new information or\nknowledge. It can be seen as the product of authors\u2019 sensemaking\nprocess over a set of source documents, and the interplay between\nreading, reflection, and writing opens up new opportunities for\ndesigning AI support. We sketch three components for AI support\ndesign and discuss considerations for future research.\nKEYWORDS\nAI-Assisted Writing, Summarization, Expert Writing, Augmented\nWriting, Expository Writing.\n1\nINTRODUCTION\nThe advent of large language models (LLMs) [4, 27, 32] has brought\nabout a dramatic change in the design space of AI-assisted writing. 1\nThe language understanding capabilities and high-quality text gen-\neration of LLMs promise to semi-automate cognitively-demanding\nwriting tasks, i.e., help produce outlines or even generate long and\ngrammatically correct paragraphs based on a short natural language\ninput prompt. 2 As a result, there is growing interest from both the\nresearch and commercial communities in exploring new designs for\nintelligent writing support systems, including supporting creative\nstory writing [7, 21, 31], blog posts or email composition, 3 personal\nknowledge management, 4 and so on.\nWhile prior work has explored many exciting applications for\nLLMs, we argue that expository writing is a task that is understud-\nied in existing work on AI augmented writing. We define expository\nwriting pieces as articles that summarize facts and produce new\nknowledge or information. For example, this could be researchers\n1 In the remainder of this work, unless otherwise specified, AI-assisted writing refers to the use of LLMs to support writing.\n2 https://platform.openai.com/examples 3 For example, commercial tools like copy.ai, https://copy.ai and Lex, https://lex.page/. 4\nIncluding apps like mem, https://get.mem.ai and Notion-AI, https://www.notion.so/.\ncollecting and reading multiple papers to write a survey paper [23],\nor doctors reviewing clinical notes to devise a treatment plan [18].\nIn these cases, authors not only summarize the source documents\nbut also add information or bring new insights that do not exist in\nthe source, e.g., organizing the relevant papers or synthesizing the\npatient\u2019s symptoms and test results to create a differential diagnosis\nand possible treatment options.\nCompared to other types of writing that do not involve external\ndocuments, e.g., creative story or argumentative writing [21, 33],\nexpository writing requires authors to comprehend the source, gen-\nerate new insights, and faithfully reference and represent extracted\ninformation in the final article. The interaction between reading\nand writing brings new challenges for developing AI support, and\nthe design is largely unexplored for incorporating the latest genera-\ntions of language models. While summarization is often a necessary\ncomponent, expository writing is also distinguished from document\nsummarization tasks in terms of its goal to bring about new infor-\nmation that does not exist in the source.\nExpository writing can be seen as a sensemaking process [29],\nand different types of sub-tasks are involved: typically, authors start\nwith iteratively exploring and reading multiple relevant documents\nto identify and extract key evidence , then they organize the evidence\ninto useful schema and further synthesize into coherent writing to\ncommunicate new knowledge or information [24]. Therefore, the\nrole of AI may vary during the process of writing, and we argue it\nis important to design different types and levels of AI support to\nmaximally help the authors while minimally influencing or shaping\ntheir opinions. In one approach, in the early stages of writing,\nthe writers would initiate and drive the work and AI should only\nprovide limited supporting functions. As ideas manifest and authors\nhave a better sense of the writing content, AI could assume more\nresponsibilities with authors \u201csupervising\u201d the model\u2019s work. For\nexample, when starting writing a survey paper, the authors come\nup with the query to find relevant papers first, and AI helps execute\nthe search and discover related documents; after authors read the\nretrieved papers and come up with ideas for the writing, AI can help\ngenerate the writing text based on authors\u2019 ideas, and the writers\nonly need to proofread model output. AI discovery helps the user\nlearn better, and editing support makes the writing more efficient\nand polished; most importantly, writers are in full control of the\nthinking and the ideas included in the produced articles.\nExpository writing occurs frequently in many real-world tasks,\nand we argue that real-world expository writing is a high value open\nproblem for AI augmented writing support, with many challenges\nand opportunities in the space. Many realistic expository writing\na r\nX\ni v\n: 2\n3\n0\n4 .\n0\n2 6\n2\n3\nv 1\n[ c\ns .\nC\nL ]\n5\nA\np r\n2\n0 2\n3\nShen, et al.\nSeveral studies have shown that pre-trained models usually are not good at reasoning\n[Brown et al., 2020, Smith et al., 2022, Rae et al., 2021], but its ability can be\nsubstantially increased by making them produce step-by-step reasoning, either by\n fine-tuning [Rajani et al., 2019, Cobbe et al., 2021, Zelikman et al., 2022, Nye et al.,\n2022] or few-shot prompting [Wei et al., 2022, Wang et al., 2022, Chowdhery et al.,\n2022] (See Table 6 for summary).\nUnlike most prior work, we focus on zero-shot prompting and show that a single fixed\ntrigger prompt substantially increases the zero-shot reasoning ability of LLMs across a\nvariety of tasks requiring complex multi-hop thinking...\nGrouping\nSummarizing\nContrasting\nReconciling\nE v i d e\nn c e -\nB a s e\nd\nK n o w\nl e dg\ne - G e\nn e r a t i\no n\nFigure 1: Dissecting an expository writing piece. In this example, the writing is a paragraph of a paper\u2019s related work section [20].\nWhile it contains summaries of relevant previous work, it also elects to group several papers together as they describe similar concepts, or\nreconcile different findings under a shared framework. Finally, it contrasts the current paper with the previous work. The writing is based\non evidence from existing work, but also presents new knowledge about the relationship between existing work as well as the novelty\nof the current paper, which are the final product of the authors\u2019 sensemaking process during the writing.\ntasks require domain experts, e.g., describing key events in law-\nsuits [30], explaining scientific concepts or ideas [19], and briefing\non patients\u2019 conditions and treatments [1]. Successful augmented\nwriting systems for these tasks stand to both reduce the expensive\nexpert hours required to perform the writing, and to improve the\nquality of the output (e.g. Bell et al. [3] reports that 1 in 5 patients\nfind a mistake in their clinical notes written by doctors or nurses).\nOften, supporting such real-world tasks also allows us to draw upon\nexisting rich repositories of example data and established evalu-\nation protocols, which can further the development of future AI\naugmented writing systems. In the following sections, we formally\ndefine the expository task and sketch components of the design\nthat assist the writing process rather than just the final article.\n2\nCHARACTERIZING EXPOSITORY\nWRITING\nIn contrast to other writing tasks, expository writing has two unique\ncharacteristics: it is evidence-driven and knowledge-generating as\nillustrated in Figure 1. Formally, given a set of source documents\nand a collection of writing objectives , expository writing aims to\ncompose a piece that synthesizes the information from the source\nand produces new information in accordance with the objectives.\nThere are different ways for authors to synthesize the source, in-\ncluding but not limited to the selection , grouping , contrasting , or\nreconciliation of multiple pieces of similar or different (even conflict-\ning) statements, and the authors\u2019 synthesis brings information not\npresent in the source documents. For example, a doctor might select\nand group a set of relevant observations from clinical notes, and\nreconcile with a possible condition to achieve the goal of devising a\ntreatment plan. The writing objectives guide both the reading and\nsynthesis process, and they can also be updated given new insights\ngenerated during the course of writing. Unlike a summarization\ntask, here, it is not necessary to involve all source documents in the\nfinal writing: in fact, the choice to not include a document also con-\nstitutes new information in the writing product (i.e., by indicating\nthe document\u2019s relative importance to the goal).\n3\nDESIGNING AI SUPPORTS FOR\nEXPOSITORY WRITING\nTo optimally involve LLMs to help expository writing, we argue\nthat there are three components: 1) supporting the reading and\nevidence-seeking for correct and efficient understanding of the\nsource, 2) assisting information synthesis and the production of\nnew knowledge and ideas, and 3) facilitating text composition to\ncommunicate relevant evidence and insights. Expository writing\npieces aim at bringing about new knowledge or perspectives, but it\ntakes significant effort for authors to comprehend the source and\nconvert the thinking into writing. By reducing the cognitive load\nand the interaction costs for information extraction and sensemak-\ning during the reading, it can help authors focus on the reasoning\nand idea generation [16, 23]. On the other hand, while LLMs have\ndemonstrated strong performance in document understanding and\ntext generation, they currently suffer from hallucination [15] and\nare limited in reasoning [4, 32] and long-form generation capabili-\nties. As such, it is sub-optimal to use LLMs to generate the whole\nwriting piece altogether and post-edit [6]. A modular design that\nprovides varying AI support at different stages of writing can be\nmost helpful, and we detail the components as follows.\nAugmenting Reading and Collecting Evidence. Reading relevant\ndocuments to gather evidence is a crucial early step for exposi-\ntory writing. While there have been significant research efforts on\ndocument discovery and comprehension (e.g., for scientific docu-\nments [8, 9, 14, 17, 25]), AI tools for supporting reading for writing\nis relatively sparse [12, 16, 26]. Experts typically expend significant\nmanual effort reading through many source documents to identify\nkey relevant information to help them synthesize new knowledge.\nBeyond Summarization: Designing AI Support for Real-World Expository Writing Tasks\nThis is referred as \u201cestablishing the working memory\u201d [2, 13], a\nkey step during the cognitive process of writing. One existing ap-\nproach to facilitate reading is using language models to automati-\ncally generate summaries for long documents [4, 34], but they can\nalso be prone to hallucination [15]. More importantly, Ziegler et al.\n[35] shows that recent instruction-tuned models are merely \u201csmart\ncopiers\u201d when performing summarization, and overly trusting the\nmodel outputs risks biasing authors\u2019 views and may lead to authors\ncollecting inaccurate evidence.\nAs such, authors\u2019 reading and understanding the source still\nplays a central role, and designing for reading for expository writing\nshould focus on the augmentation of reading and extraction of\nkey information from source documents [16], but at the same time\nproviding support for verifying the extracted evidence. For example,\nthe system can automatically generate a list of the key facts relevant\nto an author\u2019s goal from a set of source documents, and guide the\nauthors to examine and check the facts\u2019 correctness by linking each\none back to relevant parts of the original source documents.\nSupporting Information Synthesis. As an important middle step\nbetween reading and writing, the writer typically takes a long time\nto inspect and reason over the evidence collected and produce new\nideas to be written, e.g., finding the similarities and differences\nbetween papers and reconciling them under a shared framework\nas illustrated in Figure 1. Synthesizing a large of collection of in-\nformation gathered across source documents can be challenging,\nand different interfaces and techniques have been developed to\nsupport this step [5, 12, 26]. Language models can be involved to\nimprove the efficiency and accuracy of these approaches by, e.g.,\nautomatic grouping based on the textual representation [28] or\nenabling semantic search. Beyond better organizing evidence to\nreflect an author\u2019s mental model, a system could also leverage LLMs\nfor inspiration during synthesis [11]. One example is proposing\nconnections between evidences and suggesting relevant/different\ntopics to discover, though it is important to also provide mech-\nanisms so that authors can verify and ground model-generated\nideas with relevant evidence. The involvement of AI models is not\nintended to be a replacement for authors\u2019 thinking and contempla-\ntion but enhancement for producing new ideas, and discovering\notherwise unseen connections or findings.\nFacilitating Text Composition. The translation of ideas into lan-\nguage has been considered to be equally (if not more) cognitively\ndemanding and challenging as the other components: as Flower\nand Hayes [10] puts, it needs \u201cjuggle all the special demands of\nwritten English\u201d and \u201cthe process can interfere with ... planning\u201d.\nThe latest language models have demonstrated strong capabilities\nin generating fluent and high-quality text, and some studies suggest\nthe generations are even on par with freelance writers for certain\nsummarization tasks [34]. It is promising to incorporate language\nmodels to support text composition, but several design choices\nneed to be considered to reduce the risk of generation errors and\nbiasing authors\u2019 opinions [22]. For example, it may be important\nfor language models in this case to be evidence-aware and explicit\u2014\nthe generation should only be invoked when the authors call it,\nand relevant evidence and a short prompt about the writing intent\nneeds to be provided. An instance of model generation could be rel-\natively short, e.g., one or two sentences, with the authors calling it\nin multiple turns. Compared to generating long completions at one\ntime, e.g., a whole paragraph, we speculate that this could decrease\nthe possibility of erroneous and biased generation, and decrease\nthe cost of verification by the authors.\n4\nCONCLUSION\nExpository writing is genre of evidence-driven and knowledge-\ngenerating writing that takes place in many real-world settings. In\nthis paper, we argue that the unique characteristics in expository\nwriting open up new opportunities for designing AI support. We\nsketch the components of the design and highlight considerations\nand challenges for future implementation.\nACKNOWLEDGMENTS\nWe thank the anonymous reviewers for their insightful comments\nand suggestions. We appreciate the helpful discussion and advice\nfrom Oren Etzioni, Arvind Satyanarayan, Dennis Wei, Prasanna\nSattigeri, Subhro Das, Barbara Lam, Lauren Yu, and Ruochen Zhang.\nREFERENCES\n[1] Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, and No\u00e9mie El- hadad. 2021. What\u2019s in a Summary? Laying the Groundwork for Advances in\nHospital-Course Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Hu-\nman Language Technologies . Association for Computational Linguistics, Online, 4794\u20134811. https://doi.org/10.18653/v1/2021.naacl-main.382\n[2] Alan Baddeley. 1992. Working memory. Science 255, 5044 (1992), 556\u2013559. 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} - } - ] - }, - "relations": {} -} \ No newline at end of file +{"symbols": "Beyond Summarization:\nDesigning AI Support for Real-World Expository Writing Tasks\nZejiang Shen \u2020 , Tal August \u25a1 , Pao Siangliulue \u25a1 , Kyle Lo \u25a1 , Jonathan Bragg \u25a1\nJeff Hammerbacher \u25a1 , Doug Downey \u25a1 , \u22c4 , Joseph Chee Chang \u25a1 , David Sontag \u2020\n\u2020 Massachusetts Institute of Technology, \u25a1 Allen Institute for AI, \u22c4 Northwestern University\n{zjshen,dsontag}@mit.edu,{tala,paos,kylel,jbragg,jeffhammerbacher,dougd,josephc}@allenai.org\nABSTRACT\nLarge language models have introduced exciting new opportunities\nand challenges in designing and developing new AI-assisted writing\nsupport tools. Recent work has shown that leveraging this new tech-\nnology can transform writing in many scenarios such as ideation\nduring creative writing, editing support, and summarization. How-\never, AI-supported expository writing \u2014including real-world tasks\nlike scholars writing literature reviews or doctors writing progress\nnotes\u2014is relatively understudied.\nIn this position paper, we argue that developing AI supports for\nexpository writing has unique and exciting research challenges and\ncan lead to high real-world impacts. We characterize expository\nwriting as evidence-based and knowledge-generating: it contains\nsummaries of external documents as well as new information or\nknowledge. It can be seen as the product of authors\u2019 sensemaking\nprocess over a set of source documents, and the interplay between\nreading, reflection, and writing opens up new opportunities for\ndesigning AI support. We sketch three components for AI support\ndesign and discuss considerations for future research.\nKEYWORDS\nAI-Assisted Writing, Summarization, Expert Writing, Augmented\nWriting, Expository Writing.\n1\nINTRODUCTION\nThe advent of large language models (LLMs) [4, 27, 32] has brought\nabout a dramatic change in the design space of AI-assisted writing. 1\nThe language understanding capabilities and high-quality text gen-\neration of LLMs promise to semi-automate cognitively-demanding\nwriting tasks, i.e., help produce outlines or even generate long and\ngrammatically correct paragraphs based on a short natural language\ninput prompt. 2 As a result, there is growing interest from both the\nresearch and commercial communities in exploring new designs for\nintelligent writing support systems, including supporting creative\nstory writing [7, 21, 31], blog posts or email composition, 3 personal\nknowledge management, 4 and so on.\nWhile prior work has explored many exciting applications for\nLLMs, we argue that expository writing is a task that is understud-\nied in existing work on AI augmented writing. We define expository\nwriting pieces as articles that summarize facts and produce new\nknowledge or information. For example, this could be researchers\n1 In the remainder of this work, unless otherwise specified, AI-assisted writing refers to the use of LLMs to support writing.\n2 https://platform.openai.com/examples 3 For example, commercial tools like copy.ai, https://copy.ai and Lex, https://lex.page/. 4\nIncluding apps like mem, https://get.mem.ai and Notion-AI, https://www.notion.so/.\ncollecting and reading multiple papers to write a survey paper [23],\nor doctors reviewing clinical notes to devise a treatment plan [18].\nIn these cases, authors not only summarize the source documents\nbut also add information or bring new insights that do not exist in\nthe source, e.g., organizing the relevant papers or synthesizing the\npatient\u2019s symptoms and test results to create a differential diagnosis\nand possible treatment options.\nCompared to other types of writing that do not involve external\ndocuments, e.g., creative story or argumentative writing [21, 33],\nexpository writing requires authors to comprehend the source, gen-\nerate new insights, and faithfully reference and represent extracted\ninformation in the final article. The interaction between reading\nand writing brings new challenges for developing AI support, and\nthe design is largely unexplored for incorporating the latest genera-\ntions of language models. While summarization is often a necessary\ncomponent, expository writing is also distinguished from document\nsummarization tasks in terms of its goal to bring about new infor-\nmation that does not exist in the source.\nExpository writing can be seen as a sensemaking process [29],\nand different types of sub-tasks are involved: typically, authors start\nwith iteratively exploring and reading multiple relevant documents\nto identify and extract key evidence , then they organize the evidence\ninto useful schema and further synthesize into coherent writing to\ncommunicate new knowledge or information [24]. Therefore, the\nrole of AI may vary during the process of writing, and we argue it\nis important to design different types and levels of AI support to\nmaximally help the authors while minimally influencing or shaping\ntheir opinions. In one approach, in the early stages of writing,\nthe writers would initiate and drive the work and AI should only\nprovide limited supporting functions. As ideas manifest and authors\nhave a better sense of the writing content, AI could assume more\nresponsibilities with authors \u201csupervising\u201d the model\u2019s work. For\nexample, when starting writing a survey paper, the authors come\nup with the query to find relevant papers first, and AI helps execute\nthe search and discover related documents; after authors read the\nretrieved papers and come up with ideas for the writing, AI can help\ngenerate the writing text based on authors\u2019 ideas, and the writers\nonly need to proofread model output. AI discovery helps the user\nlearn better, and editing support makes the writing more efficient\nand polished; most importantly, writers are in full control of the\nthinking and the ideas included in the produced articles.\nExpository writing occurs frequently in many real-world tasks,\nand we argue that real-world expository writing is a high value open\nproblem for AI augmented writing support, with many challenges\nand opportunities in the space. Many realistic expository writing\na r\nX\ni v\n:\n2304\n.\n02623v1\n[ c\ns .\nC\nL ]\n5\nA\np r\n2023\nShen, et al.\nSeveral studies have shown that pre-trained models usually are not good at reasoning\n[Brown et al., 2020, Smith et al., 2022, Rae et al., 2021], but its ability can be\nsubstantially increased by making them produce step-by-step reasoning, either by\n fine-tuning [Rajani et al., 2019, Cobbe et al., 2021, Zelikman et al., 2022, Nye et al.,\n2022] or few-shot prompting [Wei et al., 2022, Wang et al., 2022, Chowdhery et al.,\n2022] (See Table 6 for summary).\nUnlike most prior work, we focus on zero-shot prompting and show that a single fixed\ntrigger prompt substantially increases the zero-shot reasoning ability of LLMs across a\nvariety of tasks requiring complex multi-hop thinking...\nGrouping\nSummarizing\nContrasting\nReconciling\nE v i d e\nn c e -\nB a s e\nd\nK n o w\nl e dg\ne - G e\nn e r a t i\no n\nFigure 1: Dissecting an expository writing piece. In this example, the writing is a paragraph of a paper\u2019s related work section [20].\nWhile it contains summaries of relevant previous work, it also elects to group several papers together as they describe similar concepts, or\nreconcile different findings under a shared framework. Finally, it contrasts the current paper with the previous work. The writing is based\non evidence from existing work, but also presents new knowledge about the relationship between existing work as well as the novelty\nof the current paper, which are the final product of the authors\u2019 sensemaking process during the writing.\ntasks require domain experts, e.g., describing key events in law-\nsuits [30], explaining scientific concepts or ideas [19], and briefing\non patients\u2019 conditions and treatments [1]. Successful augmented\nwriting systems for these tasks stand to both reduce the expensive\nexpert hours required to perform the writing, and to improve the\nquality of the output (e.g. Bell et al. [3] reports that 1 in 5 patients\nfind a mistake in their clinical notes written by doctors or nurses).\nOften, supporting such real-world tasks also allows us to draw upon\nexisting rich repositories of example data and established evalu-\nation protocols, which can further the development of future AI\naugmented writing systems. In the following sections, we formally\ndefine the expository task and sketch components of the design\nthat assist the writing process rather than just the final article.\n2\nCHARACTERIZING EXPOSITORY\nWRITING\nIn contrast to other writing tasks, expository writing has two unique\ncharacteristics: it is evidence-driven and knowledge-generating as\nillustrated in Figure 1. Formally, given a set of source documents\nand a collection of writing objectives , expository writing aims to\ncompose a piece that synthesizes the information from the source\nand produces new information in accordance with the objectives.\nThere are different ways for authors to synthesize the source, in-\ncluding but not limited to the selection , grouping , contrasting , or\nreconciliation of multiple pieces of similar or different (even conflict-\ning) statements, and the authors\u2019 synthesis brings information not\npresent in the source documents. For example, a doctor might select\nand group a set of relevant observations from clinical notes, and\nreconcile with a possible condition to achieve the goal of devising a\ntreatment plan. The writing objectives guide both the reading and\nsynthesis process, and they can also be updated given new insights\ngenerated during the course of writing. Unlike a summarization\ntask, here, it is not necessary to involve all source documents in the\nfinal writing: in fact, the choice to not include a document also con-\nstitutes new information in the writing product (i.e., by indicating\nthe document\u2019s relative importance to the goal).\n3\nDESIGNING AI SUPPORTS FOR\nEXPOSITORY WRITING\nTo optimally involve LLMs to help expository writing, we argue\nthat there are three components: 1) supporting the reading and\nevidence-seeking for correct and efficient understanding of the\nsource, 2) assisting information synthesis and the production of\nnew knowledge and ideas, and 3) facilitating text composition to\ncommunicate relevant evidence and insights. Expository writing\npieces aim at bringing about new knowledge or perspectives, but it\ntakes significant effort for authors to comprehend the source and\nconvert the thinking into writing. By reducing the cognitive load\nand the interaction costs for information extraction and sensemak-\ning during the reading, it can help authors focus on the reasoning\nand idea generation [16, 23]. On the other hand, while LLMs have\ndemonstrated strong performance in document understanding and\ntext generation, they currently suffer from hallucination [15] and\nare limited in reasoning [4, 32] and long-form generation capabili-\nties. As such, it is sub-optimal to use LLMs to generate the whole\nwriting piece altogether and post-edit [6]. A modular design that\nprovides varying AI support at different stages of writing can be\nmost helpful, and we detail the components as follows.\nAugmenting Reading and Collecting Evidence. Reading relevant\ndocuments to gather evidence is a crucial early step for exposi-\ntory writing. While there have been significant research efforts on\ndocument discovery and comprehension (e.g., for scientific docu-\nments [8, 9, 14, 17, 25]), AI tools for supporting reading for writing\nis relatively sparse [12, 16, 26]. Experts typically expend significant\nmanual effort reading through many source documents to identify\nkey relevant information to help them synthesize new knowledge.\nBeyond Summarization: Designing AI Support for Real-World Expository Writing Tasks\nThis is referred as \u201cestablishing the working memory\u201d [2, 13], a\nkey step during the cognitive process of writing. One existing ap-\nproach to facilitate reading is using language models to automati-\ncally generate summaries for long documents [4, 34], but they can\nalso be prone to hallucination [15]. More importantly, Ziegler et al.\n[35] shows that recent instruction-tuned models are merely \u201csmart\ncopiers\u201d when performing summarization, and overly trusting the\nmodel outputs risks biasing authors\u2019 views and may lead to authors\ncollecting inaccurate evidence.\nAs such, authors\u2019 reading and understanding the source still\nplays a central role, and designing for reading for expository writing\nshould focus on the augmentation of reading and extraction of\nkey information from source documents [16], but at the same time\nproviding support for verifying the extracted evidence. For example,\nthe system can automatically generate a list of the key facts relevant\nto an author\u2019s goal from a set of source documents, and guide the\nauthors to examine and check the facts\u2019 correctness by linking each\none back to relevant parts of the original source documents.\nSupporting Information Synthesis. As an important middle step\nbetween reading and writing, the writer typically takes a long time\nto inspect and reason over the evidence collected and produce new\nideas to be written, e.g., finding the similarities and differences\nbetween papers and reconciling them under a shared framework\nas illustrated in Figure 1. Synthesizing a large of collection of in-\nformation gathered across source documents can be challenging,\nand different interfaces and techniques have been developed to\nsupport this step [5, 12, 26]. Language models can be involved to\nimprove the efficiency and accuracy of these approaches by, e.g.,\nautomatic grouping based on the textual representation [28] or\nenabling semantic search. Beyond better organizing evidence to\nreflect an author\u2019s mental model, a system could also leverage LLMs\nfor inspiration during synthesis [11]. One example is proposing\nconnections between evidences and suggesting relevant/different\ntopics to discover, though it is important to also provide mech-\nanisms so that authors can verify and ground model-generated\nideas with relevant evidence. The involvement of AI models is not\nintended to be a replacement for authors\u2019 thinking and contempla-\ntion but enhancement for producing new ideas, and discovering\notherwise unseen connections or findings.\nFacilitating Text Composition. The translation of ideas into lan-\nguage has been considered to be equally (if not more) cognitively\ndemanding and challenging as the other components: as Flower\nand Hayes [10] puts, it needs \u201cjuggle all the special demands of\nwritten English\u201d and \u201cthe process can interfere with ... planning\u201d.\nThe latest language models have demonstrated strong capabilities\nin generating fluent and high-quality text, and some studies suggest\nthe generations are even on par with freelance writers for certain\nsummarization tasks [34]. It is promising to incorporate language\nmodels to support text composition, but several design choices\nneed to be considered to reduce the risk of generation errors and\nbiasing authors\u2019 opinions [22]. For example, it may be important\nfor language models in this case to be evidence-aware and explicit\u2014\nthe generation should only be invoked when the authors call it,\nand relevant evidence and a short prompt about the writing intent\nneeds to be provided. An instance of model generation could be rel-\natively short, e.g., one or two sentences, with the authors calling it\nin multiple turns. Compared to generating long completions at one\ntime, e.g., a whole paragraph, we speculate that this could decrease\nthe possibility of erroneous and biased generation, and decrease\nthe cost of verification by the authors.\n4\nCONCLUSION\nExpository writing is genre of evidence-driven and knowledge-\ngenerating writing that takes place in many real-world settings. In\nthis paper, we argue that the unique characteristics in expository\nwriting open up new opportunities for designing AI support. We\nsketch the components of the design and highlight considerations\nand challenges for future implementation.\nACKNOWLEDGMENTS\nWe thank the anonymous reviewers for their insightful comments\nand suggestions. We appreciate the helpful discussion and advice\nfrom Oren Etzioni, Arvind Satyanarayan, Dennis Wei, Prasanna\nSattigeri, Subhro Das, Barbara Lam, Lauren Yu, and Ruochen Zhang.\nREFERENCES\n[1] Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, and No\u00e9mie El- hadad. 2021. What\u2019s in a Summary? Laying the Groundwork for Advances in\nHospital-Course Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Hu-\nman Language Technologies . Association for Computational Linguistics, Online, 4794\u20134811. https://doi.org/10.18653/v1/2021.naacl-main.382\n[2] Alan Baddeley. 1992. Working memory. Science 255, 5044 (1992), 556\u2013559. 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