How Smart Are Smart Readers? LLMs and the Future of the
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Abstract
Professor Yonathan Arbel of the University of Alabama School of Law argues that Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns.
Citation
APA: Yonathan Arbel. (2024). How Smart Are Smart Readers? LLMs and the Future of the. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4491043
Bluebook: Yonathan Arbel, How Smart Are Smart Readers? LLMs and the Future of the, 2024, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4491043.
Summary (English)
1. ## TL;DR ≤100 words (start 'Professor Yonathan Arbel of the University of Alabama School of Law argues that')
Professor Yonathan Arbel of the University of Alabama School of Law argues that Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns.
2. ## Section Summaries ≤120 words each (author phrase repeated)
* Professor Yonathan Arbel of the University of Alabama School of Law writes that large language models (LLMs) as 'smart readers' can markedly reduce contract length and reading time, improving readability to a fifth-grade level without significant loss of essential information. However, he cautions that these tools are not flawless, sometimes miscommunicating legal terms or presenting errors. Thus, while they cannot replace lawyers, smart readers are effective for many daily transactions and signal a crucial need for a paradigm change in how contracts are approached.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that his paper investigates the capability of Large Language Models (LLMs) to address the pervasive "no-reading problem" by simplifying complex contractual texts. The study assesses the effectiveness of this simplification through metrics such as text length, complexity, and readability, and also critically evaluates the quality of these simplifications by analyzing specific clauses from major companies like the Wall Street Journal, Airbnb, and Amazon.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that a central challenge in consumer contracts is the "no-reading problem," where consumers' failure to engage with standard forms undermines informed decision-making and reduces sellers' incentives for fair terms. He explains that this chapter evaluates whether "smart readers," technological tools employing large language models, can effectively address this issue by simplifying contractual texts, thereby testing if current models have already achieved a utility threshold sufficient to empower consumers.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that his study examines how "smart readers" utilizing LLMs perform in simplifying legal documents by comparing complexity, length, readability, and quality before and after simplification, analyzing both entire agreements and specific clauses to tackle the "no-reading problem." He notes that while these smart readers significantly improve text difficulty and length and generally capture important aspects, they can struggle with certain clauses—sometimes understating, omitting, or providing incorrect information. Thus, they do not replace lawyers but offer a scalable solution for consumers.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that consumers often avoid reading form contracts because they are cognitively taxing and visually difficult, a situation that allows firms to implement a "HIDE" strategy using terms that are "Hardly Interpretable but Dependably Enforceable." He notes that in response, courts have sometimes imposed a "duty to read," while lawmakers have instituted numerous plain language laws aiming to improve contract readability and accessibility, though these traditional measures face challenges.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that current efforts to simplify legal contracts for consumers, such as plain language requirements, often prove inadequate. He argues these methods overlook consumer diversity, the challenges of low literacy, and the persistent issue of excessive contract length. However, the emergence of transformer AI technology, like GPT models, presents a promising new path with 'smart readers' capable of processing and interacting with complex legal texts in previously inconceivable ways, offering a potential solution.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that advanced large language models, or "smart readers," offer significant potential to empower consumers by personalizing and clarifying complex legal texts like contracts, thereby challenging sellers' obscure "HIDE" strategies. He acknowledges that while the technology in 2021 was nascent, unreliable, and met with skepticism, these issues were considered temporary, stemming from data and compute limitations rather than fundamental flaws, with subsequent models demonstrating clear advancements in capability and reliability.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Large Language Models like GPT-4 have demonstrated remarkable capabilities by excelling in complex exams, which has shifted the public's focus from what the technology can do to exploring its limitations, thereby highlighting its significant potential. Given the rapid mass adoption and accessibility of this impressive technology, he asserts it is now timely to assess whether "smart readers" can effectively empower consumers and comprehensively address the widespread "no-reading problem" concerning contracts and policies.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that their examination of consumer contracts, including those from major companies like Netflix and Google, focuses on readability using tests such as Flesch Ease of Reading to explore if language models can make these documents more comprehensible. He also points out that these traditional readability tests, which primarily assess syntactic features like sentence length and word rarity, are critiqued for their limited reliability, validity, and high manipulability, suggesting a need for more nuanced evaluation methods.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that their research assesses language models' ability to simplify legal texts based on key criteria: improvements in readability, significant length reduction, and the crucial preservation of essential meaning and context. He explains that to address technical challenges, such as model selection and input length limits, they opted for cost-effective yet competent models like Claude and ChatGPT and also developed their own smart reader to manage these operational constraints effectively.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that a critical challenge in their research was devising a specific, iteratively developed prompt for AI models to simplify contracts effectively. This prompt aimed for no information loss, simpler language, and brevity while meticulously preserving necessary legal concepts. Their analysis then proceeded to test the AI's simplification ability using objective metrics like length and readability, and to assess the quality of summaries by their capacity to capture key contractual information accurately.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that their high-level results indicate that various AI models, on average, successfully reduced the word count of contracts to approximately 30% of their original length. He highlights that this substantial reduction in text significantly decreased the estimated reading time for these documents, for example, from an average of 20 minutes and 45 seconds for an original contract down to just 6 minutes and 6 seconds for its AI-simplified version.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that AI models demonstrate significant variability in summarization, producing outputs with widely different lengths and reduction percentages even when given similar prompts, indicating a large degree of inconsistency between them. He notes that beyond aggregated reductions in word count, sentence count, and reading time, another important way to assess the simplification of text is by examining its complexity, for instance, through a systematic count of difficult words within the processed text.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that by using the Dalle-Chall word list, his research found an average 61% reduction in difficult words when comparing original contractual texts to versions processed by various AI models. He further states that beyond merely counting difficult words, the study also comprehensively assessed overall text readability using established measures such as the Flesch-Kincaid score and an average derived from multiple readability assessment tools to provide a broader view of simplification.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that original contracts, typically requiring 10 to 14 years of schooling according to the Flesch-Kincaid measure, saw an average reduction in reading difficulty of 1.47 grade levels when processed by LLMs. He highlights that the best performing model, Claude-001, significantly improved readability, reducing the Flesch-Kincaid level by 5.6 grades to a 5.4 grade level. This makes contracts accessible to 11-year-olds and aligns with expert recommendations for consumer documents.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that despite prompts to simplify contracts, some large language models surprisingly made them more complex, although models like Claude-001 and Text-Davinci-003 showed more consistent success in this task. He adds that his team also subjectively assessed output quality by comparing Spotify's original terms with simplified versions from ChatGPT-Turbo and Claude, specifically looking for whether 11 identified "traps" for unwary consumers were adequately addressed in the simplified outputs.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while both smart reader platforms effectively simplified contracts and captured most important information and identified "traps," they exhibited varied success and omissions. This suggests models could be complementary and that LLMs might systematically miss certain types of information. He notes that current flaws, such as those arising from "chunking" text, are considered transient, and the research subsequently shifted to analyzing specific challenging clauses to develop a more robust understanding of simplification quality.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that the research actively selected consumer-relevant contract clauses, such as those concerning unilateral modifications by companies, and utilized GPT-4 to create simplified versions. These simplified versions were then meticulously evaluated for improvements in length, complexity, and the overall quality of simplification. An analysis of a Wall Street Journal clause allowing unilateral changes to its subscriber agreement found GPT-4's simplification to be highly effective in clarifying this term of considerable importance to consumers.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while a simplified contract version is generally true to the original and clearly presents its main point, it may subtly mislead consumers. For instance, it might imply changes are entirely unilateral, contrasting with the original's requirement that changes are only effective if communicated. Despite this, he notes this simplified version demonstrates significant quantitative improvements, reducing text complexity by nearly 7.5 to 8 grade levels to an eighth-grade reading standard, cutting word count by 26%, and decreasing average word length.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that the Wall Street Journal's original 'Agreement to Arbitrate' clause stipulates that most controversies or claims, with specific exceptions for intellectual property and small claims court disputes, will be resolved by arbitration administered by the American Arbitration Association. He further clarifies that by entering into this agreement, individuals effectively waive their right to a jury trial and are explicitly barred from participating in class arbitrations or class actions.
* Professor Yonathan Arbel of the University of Alabama School of Law writes to present example arbitration clauses, both in standard legal language and a simplified version. These clauses stipulate that most disputes will be resolved through arbitration governed by New York law and the Federal Arbitration Act, thereby waiving jury trials and prohibiting class actions. He further details these clauses, which specify arbitration locations, procedural options for small claims under $14,000, and limitations on the arbitrator's award to individual relief, typically without a statement of reasons unless jointly requested.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while simplifying a mandatory arbitration clause is generally beneficial, the evaluated attempt introduced problems. These included reducing the salience of arbitration, altering the legal meaning of terms like 'intellectual property' and 'equitable relief,' and critically omitting the customer's right to bring complaints to state or federal agencies. Despite these shortcomings, he notes the simplification dramatically reduced reading complexity from a PhD to an eighth-grade level, though effectively translating complex legal terms like 'equitable relief' into simple language remains a pressing dilemma.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that analysis of publications like the Wall Street Journal indicates a trend towards clause simplification and shorter average word lengths in their contractual terms. He also notes that companies like Airbnb outline in their policies how they collect personal information from third-party services, such as linked social media accounts, and also obtain background information or criminal records as permitted by applicable laws, illustrating common data collection practices.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Airbnb details its collection of personal information from various third-party sources, including co-travelers, insurance claims, connected services like Google, and background check providers, and presents a simplified version of this policy section. He observes that this simplification successfully distills the original provision’s complex legal language into more accessible terms for consumers without distorting the overall meaning regarding third-party data collection practices by the company.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while Airbnb's simplified privacy policy offers some clearer phrasing, it problematically omits important details such as the sharing of friend lists. He also notes it can be misleading regarding user consent for background checks and the extent of information sharing with insurers. However, quantitatively, the simplified policy shows substantial improvement, reducing readability from a college level to a sixth or seventh grade level and decreasing the word count by a significant 50%.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while the simplified Netflix cancellation clause generally maintains the original's integrity and improves accessibility, cancellation provisions are inherently tricky in consumer contracts. He points out that this specific simplification, however, contains a critical misinterpretation of the refund policy by omitting the original's qualification that payments are non-refundable only "to the extent permitted by the applicable law," which could consequently mislead consumers about their rights in certain jurisdictions.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Netflix's simplified text erroneously claims an inability to refund payments, a statement that could potentially mislead consumers in jurisdictions where laws may mandate such refunds under certain circumstances. He notes that despite this significant miscommunication regarding refund policies, the simplification still managed to improve readability to a 6th-7th grade level, although with only a modest 16% reduction in word count compared to the original clause.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Amazon's terms permit users to post content like reviews and comments, provided it is not illegal, obscene, or infringing. Amazon reserves the right, but not the obligation, to remove or edit such content without regular review. He further explains that by posting content, users grant Amazon a nonexclusive, royalty-free, perpetual, irrevocable, and fully sublicensable right to use, reproduce, and distribute that content worldwide, and users also warrant ownership and agree to indemnify Amazon for claims arising from their content.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while the evaluated simplification of a contract provision governing user content contribution, like online reviews on Amazon, is largely effective and impressively improves readability, it does omit some important restrictions present in the original text. He highlights a more significant concern: that the simplification, despite communicating most user obligations, might not sufficiently convey the gravity of these potentially burdensome warranties (such as ensuring review accuracy and non-harm) due to its less formal tone.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that dense legal clauses, such as Amazon's "Risk of Loss" terms, can be significantly simplified by using shorter sentences and more straightforward language, as demonstrated by a tangible reduction in average word length in the simplified versions. He illustrates that this simplification effectively transforms complex statements, like Amazon's original risk of loss clause, into much clearer terms such as "When you buy physical items from Amazon, they are yours once we give them to the carrier for delivery."
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while a simplified contract clause regarding delivery risk effectively communicates ownership transfer to the buyer upon shipping, it inadequately conveys that the customer bears the risk if delivery subsequently fails. He notes that this criticism of unclear risk allocation extends to simplified return clauses as well, and observes that while the simplification achieved only modest readability gains for an already relatively simple original clause, more significant improvements could potentially be made.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Yahoo's privacy policy details how user information is shared within its affiliated brands and companies, and also for purposes described in its policy, including the provision of requested services to users. He further notes that Yahoo asserts it does not sell, license, or share information that individually identifies customers with external companies unless specific circumstances apply, such as obtaining user consent, sharing within Yahoo affiliates, or with trusted partners under confidentiality measures.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Yahoo shares aggregated or pseudonymous, non-personally identifiable user information with partners like advertisers and analytics companies, while also sharing information within its own related brands and companies. He clarifies that personally identifiable information is not shared with outside companies unless users give explicit permission, and notes that third-party apps, websites, or advertisers integrated with Yahoo services collect data under their own distinct privacy policies, separate from Yahoo's.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while AI simplification can capture the intended logic of complex legal provisions, such as Yahoo's policies on information sharing, its value is inherently limited if the original legal documents are poorly drafted or contain logical inconsistencies. Despite these limitations, he finds that AI simplification proved helpful by dramatically reducing text complexity and improving readability, though he cautions that firms might adopt specific drafting strategies to circumvent the effectiveness of smart readers.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Spotify's terms state a user's sole remedy for dissatisfaction with its service or any linked third-party applications is to uninstall the service and cease using them. He further explains that Spotify also disclaims liability for various types of damages, capping its aggregate liability for all claims related to the service at the greater of the amounts paid by the user in the prior twelve months or a nominal sum of $30.00.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that Spotify's terms attempt to significantly limit its liability, for instance by stating the user's sole remedy for issues is to stop using the service and by capping potential monetary damages at recent payments or $30. He observes that these liability limitation clauses, while common in consumer contracts, are worded inconsistently and confusingly within Spotify's terms, initially offering uninstallation as the only remedy before then moving to limit monetary damages.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while a simplification of Spotify's disclaimer had some communicative benefits for consumers, it problematically altered the legal meaning of the clause. This was achieved by replacing specific legal terms with simpler but inexact phrases. Quantitatively, he notes this simplification improved readability scores but also slightly increased document length and, importantly, highlighted the limitations of standard readability tests when dealing with complex sentence structures common in legal texts.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that simplified legal clauses demonstrated enhanced accessibility through notably shorter length, reduced complexity, and significantly improved readability metrics, on average halving the required education level for comprehension. However, he cautions that despite these substantial gains, simplified texts might still be inaccessible by standard metrics and offer no guarantee that consumers will actually read them, though an overall significant improvement in understandability was observed across the board.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that while smart readers made contracts more accessible with simpler language, their accuracy was mixed; some simplifications were beneficial, but others introduced substantial issues such as suggesting non-existent active consent requirements or omitting crucial consumer rights. A significant observed problem was the incorrect usage of legal terminology, like confusing "consequential" with "follow-on" damages, which could be harmful if courts adopt the smart reader's potentially flawed interpretation over the canonical contract.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that smart readers can substantially shorten legal texts, reduce their complexity, and improve overall readability, generally including important information in their summaries. He concludes that while not perfect, these simplification tools offer a marked improvement over the common scenario of consumers not reading legal texts at all, potentially facilitating more informed decisions and thereby enhancing market efficiency by better aligning consumer understanding with contractual obligations.
* Professor Yonathan Arbel of the University of Alabama School of Law writes that current smart reader models, though not yet replacements for lawyers, have effectively arrived and can serve as a cheap, effective, and scalable alternative for the many contracts and privacy policies that presently go unread by consumers. He states that despite their revolutionary potential to transform consumer contracting, concerns regarding accuracy, corporate capture, and bias must be diligently addressed, as their materializing potential would make a law and policy paradigm shift appropriate, if not inevitable.
One-page summary
# How Smart Are Smart Readers? LLMs and the Future of the — one-page summary **Paper ID:** `ssrn-4491043` **Year:** 2024 **Author(s):** Yonathan Arbel **SSRN:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4491043 ## TL;DR Professor Yonathan Arbel of the University of Alabama School of Law argues that Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns. ## Keywords contracts; AI; law ## Files - Full text: `papers/ssrn-4491043/paper.txt` - PDF: `papers/ssrn-4491043/paper.pdf` - Summary (EN): `papers/ssrn-4491043/summary.md` - Summary (ZH): `papers/ssrn-4491043/summary.zh.md` _Auto-generated study aid. For canonical content, rely on `paper.txt`/`paper.pdf`._
Study pack
# Study pack: How Smart Are Smart Readers? LLMs and the Future of the (ssrn-4491043) - SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4491043 - Full text: `papers/ssrn-4491043/paper.txt` - Summary (EN): `papers/ssrn-4491043/summary.md` - Summary (ZH): `papers/ssrn-4491043/summary.zh.md` ## Elevator pitch Professor Yonathan Arbel of the University of Alabama School of Law argues that Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns. ## Keywords / concepts contracts; AI; law ## Suggested questions (for RAG / study) - What is the paper’s main claim and what problem does it solve? - What method/data does it use (if any), and what are the main results? - What assumptions are doing the most work? - What are the limitations or failure modes the author flags? - How does this connect to the author’s other papers in this corpus? _Auto-generated study aid. For canonical content, rely on `paper.txt`/`paper.pdf`._
摘要（中文）
好的，这是您提供的英文法律摘要的正式中文翻译：
1. ## 内容摘要 ≤100字 (以“阿拉巴马大学法学院的Yonathan Arbel教授认为”开头)
阿拉巴马大学法学院的Yonathan Arbel教授认为，作为“智能阅读器”的大型语言模型（LLMs）能显著简化复杂合同，缩短篇幅、提高可读性，以赋能消费者对抗“不阅读问题”。虽非完美——偶有误解法律术语或遗漏信息，故无法取代律师——但它们为日常交易提供了可扩展方案。Arbel教授总结，这些工具是重大进步，有望革新消费者签约，并促使法律政策范式转变，惟需解决准确性与偏见问题。
2. ## 各章节摘要 ≤120字/每条 (重复作者引语)
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，作为“智能阅读器”的大型语言模型（LLMs）能显著缩短合同篇幅和阅读时间，将可读性提升至五年级水平，且基本不损失重要信息。但他提醒，这些工具并非完美，有时会错误传达法律术语或出现错误。因此，智能阅读器虽不能取代律师，但对许多日常交易有效，并表明亟需对合同处理方式进行范式变革。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，其论文研究了大型语言模型（LLMs）通过简化复杂合同文本以解决普遍存在的“不阅读问题”的能力。该研究通过文本长度、复杂性和可读性等指标评估简化效果，并通过分析《华尔街日报》、爱彼迎和亚马逊等大公司的具体条款，严格评估了这些简化的质量。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，消费者合同的核心挑战在于“不阅读问题”，即消费者未能理解标准格式合同，这削弱了知情决策，并降低了卖方提供公平条款的动机。他解释道，本章评估了采用大型语言模型的“智能阅读器”这类技术工具能否通过简化合同文本有效解决此问题，从而检验当前模型是否已达到足以赋能消费者的效用阈值。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，其研究通过比较简化前后的复杂度、长度、可读性和质量，分析了整个协议和特定条款，以考察“智能阅读器”（利用LLMs）在简化法律文件方面的表现，旨在解决“不阅读问题”。他提到，尽管这些智能阅读器显著改善了文本难度和长度，并通常能抓住要点，但在处理某些条款时可能存在不足——有时会轻描淡写、遗漏或提供错误信息。因此，它们不能取代律师，但为消费者提供了可扩展的解决方案。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，消费者常因格式合同认知负荷大且视觉上难以阅读而避免阅读，这种情况使得公司得以实施“HIDE”策略，使用“难以理解但可强制执行”的条款。他提到，作为回应，法院有时会施加“阅读义务”，而立法者则制定了许多旨在提高合同可读性和易获取性的通俗语言法，尽管这些传统措施面临挑战。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，当前为消费者简化法律合同的努力，如通俗语言要求，往往不尽如人意。他认为这些方法忽视了消费者的多样性、低读写能力的挑战以及合同篇幅过长这一持续存在的问题。然而，以GPT模型为代表的Transformer人工智能技术的出现，带来了“智能阅读器”这一前景广阔的新路径，其能够以前所未有的方式处理复杂法律文本并与之互动，为解决问题提供了潜在方案。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，先进的大型语言模型，即“智能阅读器”，通过个性化和阐释如合同之类的复杂法律文本，在赋能消费者方面展现出巨大潜力，从而挑战了卖方晦涩的“HIDE”策略。他承认，尽管2021年时该技术尚不成熟、不可靠且受到质疑，但这些问题被认为是暂时的，源于数据和计算能力的限制而非根本缺陷，后续模型已在能力和可靠性方面展现出明显进步。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，诸如GPT-4之类的大型语言模型通过在复杂考试中表现出色，已展现出卓越能力，这使公众的关注点从技术能做什么转向探究其局限性，从而凸显了其巨大潜力。鉴于这项令人印象深刻的技术已迅速大规模普及且易于获取，他断言，现在是评估“智能阅读器”能否有效赋能消费者并全面解决合同与政策方面普遍存在的“不阅读问题”的适当时机。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，他们对包括Netflix和谷歌等大公司在内的消费者合同的审查，侧重于使用弗莱什易读性等测试方法评估可读性，以探究语言模型能否使这些文件更易理解。他还指出，这些主要评估句子长度和词语罕见度等句法特征的传统可读性测试，因其可靠性、有效性有限且易被操控而受到批评，这表明需要更细致的评估方法。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，他们的研究基于关键标准评估语言模型简化法律文本的能力：可读性的改善、篇幅的显著缩减以及核心含义和语境的关键保留。他解释说，为应对模型选择和输入长度限制等技术挑战，他们选择了如Claude和ChatGPT这样性价比高且功能胜任的模型，并开发了自己的智能阅读器以有效管理这些操作限制。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，其研究中的一个关键挑战是为人工智能模型设计一个具体的、迭代开发的提示指令，以有效简化合同。该提示指令旨在实现信息无损、语言简化和简洁，同时审慎保留必要的法律概念。他们的分析随后使用长度和可读性等客观指标测试了人工智能的简化能力，并通过其准确捕捉关键合同信息的能力来评估摘要的质量。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，他们的高层次结果表明，各种人工智能模型平均成功地将合同字数减少至原文长度的约30%。他强调，文本的大幅缩减显著减少了这些文件的预计阅读时间，例如，一份原始合同的平均阅读时间从20分45秒降至人工智能简化版的仅6分6秒。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，人工智能模型在摘要生成方面表现出显著的变异性，即使给予相似的提示指令，其输出的长度和缩减百分比也大相径庭，表明模型间存在很大程度的不一致性。他提到，除了词数、句数和阅读时间的总体减少外，评估文本简化的另一个重要方法是考察其复杂性，例如，通过系统性统计处理后文本中的疑难词汇数量。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，通过使用Dalle-Chall词汇表，其研究发现，在比较原始合同文本与经各种人工智能模型处理后的版本时，疑难词汇平均减少了61%。他进一步指出，除了简单计算疑难词汇外，该研究还运用弗莱什-金凯德评分以及综合多种可读性评估工具得出的平均值等既定标准，全面评估了整体文本可读性，以提供更广泛的简化视角。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，根据弗莱什-金凯德标准，原始合同通常需要10至14年的学校教育才能理解，经大型语言模型处理后，阅读难度平均降低了1.47个年级水平。他强调，表现最佳的模型Claude-001显著提高了可读性，将弗莱什-金凯德等级降低了5.6个年级，达到了5.4年级水平。这使得合同对11岁儿童也易于理解，并符合专家对消费者文件的建议。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管提示要求简化合同，但一些大型语言模型出人意料地使其更为复杂，不过Claude-001和Text-Davinci-003等模型在此任务中表现出更稳定和成功的简化效果。他补充说，其团队还通过比较Spotify的原始条款与ChatGPT-Turbo和Claude生成的简化版本，主观评估了输出质量，特别关注简化版是否充分处理了11个已识别的针对粗心消费者的“陷阱”。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管两个智能阅读器平台均有效地简化了合同，并捕捉了大部分重要信息及识别出的“陷阱”，但它们在成功程度和遗漏方面表现各异。这表明不同模型可能具有互补性，且大型语言模型可能系统性地遗漏某些类型的信息。他指出，当前诸如文本分块处理引发的缺陷被认为是暂时性的，研究随后转向分析特定的疑难条款，以更深入地理解简化质量。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，该研究主动选取了与消费者相关的合同条款，例如涉及公司单方面修改权的条款，并利用GPT-4创建了简化版本。随后，对这些简化版本在长度、复杂性以及整体简化质量方面的改进进行了细致评估。对《华尔街日报》一项允许单方面更改其订户协议的条款分析发现，GPT-4的简化在阐明这一对消费者至关重要的条款方面非常有效。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管简化后的合同版本通常忠于原文并清晰呈现其要点，但它可能巧妙地误导消费者。例如，它可能暗示变更完全是单方面的，而原文则要求变更需经通知方可生效。尽管如此，他指出这个简化版本在量化指标上展现了显著改进，将文本复杂度降低了近7.5至8个年级，达到八年级阅读水平，词数减少了26%，平均词长也有所缩短。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，《华尔街日报》的原始“仲裁协议”条款规定，除知识产权和小额索赔法庭争议等特定例外情况外，大多数争议或索赔将通过美国仲裁协会管理的仲裁解决。他进一步阐释，签订此协议即意味着个人有效放弃陪审团审判权，并明确禁止参与集体仲裁或集体诉讼。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，为展示仲裁条款示例，特提供标准法律文本和简化版本。这些条款规定，多数争议将通过受纽约州法律及《联邦仲裁法》管辖的仲裁解决，从而放弃陪审团审判并禁止集体诉讼。他进一步详述了这些条款，包括仲裁地点、14000美元以下小额索赔的程序选项，以及仲裁员裁决仅限于个体救济且通常不附理由说明（除非双方共同要求）的限制。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管简化强制性仲裁条款通常有益，但所评估的尝试却引入了问题。这些问题包括降低了仲裁的显著性，改变了“知识产权”和“衡平法救济”等术语的法律含义，并严重遗漏了客户向州或联邦机构投诉的权利。尽管存在这些不足，他指出简化版将阅读复杂度从博士水平显著降低至八年级水平，但如何有效地将“衡平法救济”等复杂法律术语转化为简单语言仍是一个亟待解决的难题。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，对《华尔街日报》等出版物的分析表明，其合同条款有条款简化和平均词长缩短的趋势。他还提到，像爱彼迎这样的公司在其政策中概述了如何从第三方服务（如关联的社交媒体账户）收集个人信息，并在适用法律允许的情况下获取背景信息或犯罪记录，这说明了常见的数据收集做法。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，爱彼迎详述了其从各种第三方来源收集个人信息的情况，包括同行旅伴、保险索赔、关联服务（如谷歌）及背景调查提供商，并展示了该政策部分的简化版本。他观察到，此简化成功地将原始条款复杂的法律语言提炼为消费者更易理解的表述，且未扭曲公司关于第三方数据收集实践的整体含义。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管爱彼迎简化后的隐私政策在措辞上更为清晰，但它在遗漏重要细节（如好友列表的共享）方面存在问题。他还指出，该简化版在用户对背景调查的同意以及与保险公司共享信息的范围方面可能产生误导。然而，从量化角度看，简化后的政策有显著改进，可读性从大学水平降至六或七年级水平，字数也大幅减少了50%。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，虽然Netflix简化后的取消条款通常保持了原文的完整性并提高了易获取性，但取消条款本身在消费者合同中就具有复杂性。他指出，此特定简化版本在退款政策的解读上存在关键性错误，遗漏了原文中“付款不可退还”仅限于“在适用法律允许的范围内”这一限定条件，这可能导致消费者对其在某些司法管辖区的权利产生误解。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，Netflix的简化文本错误地声称无法退款，这一陈述在某些法律可能规定特定情况下必须退款的司法管辖区内，可能会误导消费者。他指出，尽管在退款政策方面存在此等重大信息传递失误，该简化版仍成功地将可读性提高至六至七年级水平，尽管与原始条款相比，字数仅略微减少了16%。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，亚马逊的条款允许用户发布评论等内容，前提是内容不违法、不淫秽或不侵权。亚马逊保留移除或编辑此类内容的权利（而非义务），且不进行定期审查。他进一步解释，用户发布内容即授予亚马逊一项非排他性、免版税、永久、不可撤销且完全可再许可的权利，在全球范围内使用、复制和分发该内容；用户还需保证拥有所有权，并同意就其内容引发的索赔对亚马逊进行赔偿。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管对一项规管用户内容贡献（如亚马逊在线评论）的合同条款的评估简化版大体有效，并显著提高了可读性，但它确实遗漏了原文中的一些重要限制。他强调了一个更值得关注的问题：该简化版尽管传达了大部分用户义务，但由于其语气不够正式，可能未能充分传达这些潜在繁琐保证义务（如确保评论准确性和无害性）的严肃性。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，诸如亚马逊“灭失风险”条款等密集的法律条文，可以通过使用更短的句子和更直白的语言得到显著简化，简化版本中平均词长的切实减少即证明了这一点。他举例说明，这种简化有效地将复杂陈述（如亚马逊原始的灭失风险条款）转化为更清晰的表述，例如“当您从亚马逊购买实物商品时，一旦我们将其交给承运人进行配送，商品即归您所有。”
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管简化后的关于交付风险的合同条款有效地传达了所有权在发货时即转移给买方，但它未能充分说明若后续交付失败，客户仍需承担风险。他指出，这种对风险分配不明确的批评同样适用于简化后的退货条款，并观察到，虽然对于一个本已相对简单的原始条款，该简化仅实现了适度的可读性提升，但仍有潜力做出更显著的改进。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，雅虎的隐私政策详细说明了用户信息如何在其附属品牌和公司内部共享，以及用于其政策中所述的目的，包括向用户提供所请求的服务。他进一步指出，雅虎声明其不会向外部公司出售、许可或共享可单独识别客户身份的信息，除非特定情况适用，例如获得用户同意、在雅虎附属公司内部共享，或与保密措施下的可信合作伙伴共享。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，雅虎与其合作伙伴（如广告商和分析公司）共享汇总的或假名化的、非个人可识别的用户信息，同时也在其自有相关品牌和公司内部共享信息。他澄清，除非用户明确许可，否则个人可识别信息不会与外部公司共享，并指出与雅虎服务集成的第三方应用程序、网站或广告商根据其自身独立的隐私政策收集数据，这些政策与雅虎的政策不同。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，虽然人工智能简化能够捕捉复杂法律条款（如雅虎关于信息共享的政策）的预期逻辑，但如果原始法律文件起草不佳或包含逻辑矛盾，其价值则 inherently limited（本质上有限）。尽管存在这些局限性，他发现人工智能简化通过显著降低文本复杂性和提高可读性证明是有帮助的，但他同时警告，公司可能会采取特定的起草策略来规避智能阅读器的有效性。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，Spotify的条款声明，用户对其服务或任何关联第三方应用程序不满的唯一补救措施是卸载该服务并停止使用。他进一步解释，Spotify还不承担各种类型损害的责任，并将其与服务相关的所有索赔的总赔偿责任上限设定为用户在前十二个月支付的金额与象征性的30.00美元两者中的较大者。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，Spotify的条款试图显著限制其责任，例如声明用户对问题的唯一补救措施是停止使用服务，并将潜在的金钱赔偿上限设定为最近支付的金额或30美元。他观察到，这些责任限制条款虽然在消费者合同中常见，但在Spotify的条款中措辞不一致且令人困惑，最初将卸载作为唯一补救措施，随后又转而限制金钱赔偿。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管Spotify免责声明的简化版本对消费者在信息沟通方面带来一些益处，但它通过将特定的法律术语替换为更简单但不精确的短语，从而在问题性地改变了条款的法律含义。从量化角度看，他指出这种简化提高了可读性分数，但也略微增加了文件长度，并且重要的是，凸显了标准可读性测试在处理法律文本中常见的复杂句子结构时的局限性。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，简化后的法律条款通过显著缩短的长度、降低的复杂性和大幅改善的可读性指标，展现了增强的可访问性，平均将理解所需的教育水平降低了一半。然而，他警告说，尽管取得了这些实质性进展，简化后的文本按标准指标衡量可能仍然难以访问，并且不能保证消费者会实际阅读它们，尽管在整体可理解性方面观察到了普遍的显著改善。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，尽管智能阅读器通过更简单的语言使合同更易获取，但其准确性参差不齐；一些简化是有益的，但另一些则引入了重大问题，例如暗示了不存在的主动同意要求或遗漏了关键的消费者权利。一个观察到的显著问题是法律术语使用不当，例如将“间接”损害（consequential damages）与“后续”损害（follow-on damages）混淆，如果法院采纳智能阅读器可能存在缺陷的解释而非规范合同文本，这可能是有害的。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，智能阅读器能够大幅缩短法律文本，降低其复杂性，并提高整体可读性，通常能在其摘要中包含重要信息。他总结道，虽然并非完美，但与消费者根本不阅读法律文本的普遍情况相比，这些简化工具提供了显著的改进，有可能促进更明智的决策，从而通过更好地使消费者理解与合同义务相一致来提高市场效率。
* 阿拉巴马大学法学院的Yonathan Arbel教授指出，当前的智能阅读器模型虽然尚不能取代律师，但已有效问世，并可作为一种廉价、有效且可扩展的替代方案，用于处理目前消费者普遍不阅读的众多合同和隐私政策。他表示，尽管它们具有革新消费者签约的革命性潜力，但必须努力解决有关准确性、企业俘获和偏见的问题，因为其潜力的实现将使得法律和政策范式的转变变得适当，甚至不可避免。