Regulation and NLP (RegNLP): Taming Large Language Models
Authors: Catalina Goanta
Nikolaos Aletras
Ilias Chalkidis
Sofia Ranchordás
Gerasimos Spanakis
Venue: EMNLP 2023 Main (2023)
Keywords: LLM
regulation
ethics
safety
public policy
science influencers

Abstract: The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) is at its fastest pace to date. As large language models (LLMs) unleash a new era of automation, important debates emerge regarding the benefits and risks of their development, deployment and use. Currently, these debates have been dominated by often polarized narratives mainly led by the AI Safety and AI Ethics movements. This polarization, often amplified by social media, is swaying political agendas on AI regulation and governance and posing issues of regulatory capture. Capture occurs when the regulator advances the interests of the industry it is supposed to regulate, or of special interest groups rather than pursuing the general public interest. Meanwhile in NLP research, attention has been increasingly paid to the discussion of regulating risks and harms. This often happens without systematic methodologies or sufficient rooting in the disciplines that inspire an extended scope of NLP research, jeopardizing the scientific integrity of these endeavors. Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options. This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity to regulatory studies and adjacent fields. We do so by discussing basic tenets of regulation, and risk and uncertainty, and by highlighting the shortcomings of current NLP discussions dealing with risk assessment. Finally, we advocate for the development of a new multidisciplinary research space on regulation and NLP (RegNLP), focused on connecting scientific knowledge to regulatory processes based on systematic methodologies.

Regulation and NLP (RegNLP): Taming Large Language Models
Catalina Goanta
Nikolaos Aletras
Ilias Chalkidis University of Copenhagen
Utrecht University
University of Sheffield
Sofia Ranchordas
Gerasimos Spanakis
Tilburg University
Maastricht University
Abstract
Lobbying
The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) at its fastest pace date. large language models (LLMs) unleash new era of automation, important debates emerge regarding the benefits and risks of their devel- opment, deployment and use. Currently, these debates have been dominated by often polar- ized narratives mainly led by the A/ Safety and Al Ethics movements. This polarization, often amplified by social media, swaying political agendas Al regulation and govemancee and posing issues of regulatory capture. Capture occurs when the regulator advances the inter- ests of the industry is supposed to regulate, or of special interest groups rather than pursuing the general public interest. Meanwhile NLP research, attention has been increasingly paid the discussion regulating risks and harms. This often happens without systematic method- ologies or sufficient rooting in the disciplines inspire an extended scope NL.P research, jeopardizing the scientific integrity of these deavors. Regulation studies are rich source of knowledge how to systematically deal with risk and uncertainty, well as with scientific evidence, to evaluate and compare regulatory options. This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity regulatory studies and adjacent fields. We do so by discussing basic tenets of regulation, and risk and uncertainty, and by highlighting the shortcomings of current NL.P discussions dealing with risk assessment. Fi- nally, we advocate for the development of new multidisciplinary research space regulation and NLP (RegNLP), focused on connecting sci- entific knowledge to regulatory processes based on systematic methodologies.
Industry
Special Groups
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Al Safety
Al Ethics
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Al Regulation
Regulatory Capture in Al Governance
Figure 1: A depiction of the cross-over between Al Safety, Ethics, Doomism, and how they capture Al Reg- ulation.
years alone, LLMs have seen considerable advance- ment across a multitude of languages and types of data, with models such GPT-3.5 (Ouyang et al., 2022), GPT-4 (OpenAI, 2023), LLaMA (Tou- al., 2023), and PALM-2 (Anil al., 2023) demonstrating unprecedented capabilities across a broad collection of natural language processing (NLP) tasks.'
These innovations have led to rapid shifts in various applications such open-domain search, coding, e-commerce and education. For example, state-of-the-e LLMs already power conversational search engines (e.g. OpenAl ChatGPT, Bing Chat, and Google Bard), coding assistants (c.g. OpenAl Codex and Github Copilot), product recommender systems (e.g. Alibaba Tongyi and SalesForce Com- merceGPT) and educational assistants (e.g. Khan- migo) inter alia.
As with most technologies, the development and use LLMs do not come without concerns. Researchers are rightfully worried that while this technology may be transformative, its societal implications might be higher than its benefits (Gabriel, 2020). Concerns have been raised es-
1 Introduction
The development of Large Language Models (LLMss is at its fastest pace to date. In the past
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pecially around ethics (Floridi, 2023; Tsarapatsa- nis and Aletras, 2021), bias (Hovy and Prabhu- moye, 2021; Blodgett et al., 2020), safety (Dobbe et al., 2021) and environmental impact (Rillig et al., 2023; Schwartz et al., 2020; Strubell et al., 2019). The unsupervised use of LLMs has already led to widely-publicized examples of professional negli- gence. The all too public fiasco of the lawyer who used ChatGPT for court brief and unknowingly included made-up case law references was taken by many as an example of the dangers of immersing daily professional activitics in generative AI.² The public debate around the future of AI, and implic- itly NLP, is very complex and multi-layered. While the debate seems to converge on the point of calling for regulation to control the unwanted effects of these technologies, different regulatory directions are proposed by the various stakeholders involved in this debate.
from prior work on regulation studies, Instead, the often intense public conflicts in this space are nudg- ing regulators towards reactionary public relations activities rather than the collection of scientific ex- pertise representing broader parts of the NL. and AI communities. For instance, right after the most recent letter on existential risks, the US and EU agreed to develop an Al code of conduct 'within weeks' Similarly, the UK has announced it will be holding a global summit on Al safety, and the US Congress has been taking evidence from a wide array of industry executives, in what has been described as a global race to regulate AI. 1I With legacy and social media considerably increasing the visibility of these often polarizing debates, there is real danger of regulatory capture by visible voices in industry and academia alike scientific views that might not necessarily be representative of the 'silent majority' of NLP researchers. Regu- latory capture the process by which regulation is directed away from the public interest and to-
The current public discourse has been dominated by two groups. On the one hand, proponents of AT existential risks (the Al Safery movement)* that in- clude technology CEOs and AI researchers have been publishing open letters5.6.7 and regularly meet with regulators to warn about catastrophic scenar- ios around general AI, proposing industry-friende solutions (Roose, 2023). On the other hand, the Al ethics movement (Borenstein et al., 2021), mostly reflects the voice rescarchers from various disci- plines as well as civil society activists. They have raised compelling alarm bells with respect to the risks posed by LLMs (Bender et al., 2021; Wei- dinger et al., 2022; Floridi, 2023). The Al ethics movement has also offered guidance to regulators such as the European parliament on Al governance, arguing for c.g. broad definitions of general pur- pose A
wards the interests of specific groups (Levine and Forrence, 1990; Dal Bo, 2006).
Against this background. we call on the NLP community to familiarize itself with regulation studies. We argue that this can lead to a clearer vision about how NLP as a field can properly partic- ipate in Al governance not only as an object of reg- ulation, but also as a source of scientific knowledge that can benefit individuals, societies and markets alike. In this paper, we contribute to the existing debate relating to the future of NLP by discussing the benefits of interfacing NLP rescarch with reg- ulation studies in systematic way. This view is based on two main ideas:
1. NLP research on regulation needs a multidis- ciplinary framework engaging with regulation studies, as well as adjacent disciplines such law, economics, environmental science, etc. We advocate for on regulation and NLP (RegNLP), with har- monized and systematic methodologies.
Yet while NLP research increasingly focuses on LLM regulation, it remains generally detached
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and regulation (such as RegNLP) can interact with policy-makers with more transparency, representation, and trustworthiness.
are two well-known examples (Morgan and Yeung. 2007). According to public interest theories, regu- lation is used by law-makers to serve a broad public interest, seeking to regulate in the most efficient way possible. Regulators assume that markets need 'a helping hand' because when unhindered, they will fail. Information asymmetries are one of the market failures that regulation seeks to address. At the same time, public interest theories of regulation are normative and prescriptive: on the one hand, they assume that benevolent state regulators ought always to use regulation to advance the public inter- est; on the other, they also advise on how to achieve this goal.
2 Regulation: A Short Introduction
Why Do We Regulate? Calls to regulate LLMs and Al are everywhere, to the extent that overusing the term 'regulation' is trivializing its meaning. So what exactly is regulation and why do we rely on it?
Historically, regulation was defined by reference to state intervention in the economy. Selznick (1985) defined regulation as 'a sustained and fo- cused control exercised by a public agency over activities that are valued by the community'. Over the last decades, regulation has evolved and it has increasingly acquired hybrid character as both public and private actors may issue rules that shape social behavior. In this paper, we draw on Black's definition of regulation as an organized and inten- tional attempt to manage another person's behavior so as to solve a collective problem (Black, 2008). This is done through a combination of rules or norms which come together with means for their implementation and enforcement (Ogus, 2009). Regulation includes different types of regula- tory instruments (e.g. laws) such as traditional top- regulations. Recent
In contrast, private interest theories not pri- marily concerned with normative justifications for regulation. Rather, they are prescriptive accounts of the complex dynamics between different market actors, stakeholders and public officials situated in a given socio-economic, political and cultural time and place. They explain, for example, why regula- tion may fail to pursue the public interest but they do not offer prescriptions on how address to such problems. Private interest theories assume that reg- ulation emerges from the actions of individuals or groups motivated to maximize their self-interest.
Technology Regulation and the Role of Science Technological change often disrupts the wider reg- ulatory order, triggering concerns about its ad- equacy and regulatory legitimacy (Brownsword et al., 2017). Differences in the timing of tech- nology and regulation explain this difficulty. The literature has claimed there is a 'pacing gap' be- tween the slow-going nature of regulation and the speed of tcchnological change (Marchant et al., 2013). Technological innovations have specific de- velopment trajectories, investment and life cycles, and path dependencies (van den Hoven, 2014) that do not go well with the speed of technology. This also applies LLMs. This is well-known prob- lem in regulatory studies that has been captured by the Collingridge dilemma (Genus and Stirling, 2018). This dilemma explains that when an inno- vation emerges, regulators hesitate to regulate due to the limited availability of information. However, by the time more is known, regulations may have become obsolete as technology may have already changed.
down or command-and-comee examples in digital public policy include the laws issued by the European Union, such as the Digital Services Act, or India's ' law banning Tok. How- ever, beyond these public regulatory instruments, there is an array of private or hybrid instruments (Veale et al., 2023). Some of these are qualified as 'soft' regulation because they cannot be enforced in court but they remain relevant and they effectively shape the behavior of the industry. Examples in- clude the EU's Ethics Guidelines for Trustworthy 22 but also codes of conduct initiated by industry itself." 13
Over the last decades, different theories of regu- lation have helped us understand either on norma- tive or empirical accounts why we should regulate. They generally reflect various hypotheses about whhy regulation emerges, which actors contribute to that emergence and typical patterns of interaction between regulatory actors' (Morgan and Yeung. 2007). Public interest and private interest theories
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The increased pace of regulatory activities in the past years in the field of technology shows that regulators are trying to close the pacing gap and

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be more proactive in tackling the potential risks of technology. In doing so, they increasingly depend on retrieving scientific information in a quick and agile way.
3 LLMs: Risk and Uncertainty
The need to bridge NLP research with regulation studies is especially important the discussion of risks. New and emerging technologics are typically accompanied by risk and uncertainty. The regu- lation of technological change and innovation is highly complex, as innovation remains an elusive concept hard to define, measure, and thus regulate. the past years, the question of risks arising out of NLP developments such as LLMs has been increasingly embraced in computer science litera- ture. One strand of this literature is reflected by the theme of algorithmic unfairness. This theme emerged at the intersection of discrimination law and automated decision-making and includes ques- tions relating fairness and machine learning in general (Barocas al., 2019), as well as specific examples of algorithmic bias risks in NLP (Field et al., 2023; Talat et al., 2022; Kidd and Birhane, 2023), computer vision (Wolfe et al., 2023), mul- timodal models (Birhane et al., 2021), as well as privacy risks (Mireshghallah et al., 2022). Another strand of this literature looks at LLMs from more holistic perspective, raising concerns about their size vis-a-vis a broader number of risks for c.g. the environment, bias, representation or hate speech (Bender et al., 2021; Weidinger et al., 2022: Bom- masani et al., 2022). This theme does not only include risks in commercial applications, but also risks arising out of the mere scientific development of technology.
The role of science in regulation and public pol- icy has been the subject of important debates. On the one hand, regulation should reflect the latest scientific evidence and be evidence-based based. An il- lustration of this approach is the European Union's "Better Regulation' agenda, a public policy strategy aiming to ensure that European regulation is based on scientific evidence, as well as the involvement of a wide range of stakeholders in the decision- making process (Commission, 2023b; Simonelli and lacob, 2021). Citizens, businesses and any other stakcholders can submit their contributions the calls for evidence, feedback and public con- sultations. For instance, 303 contributions were re- ceived on the AI Act proposal during 26 April 2021 and 6 August 2021, out of which 28% from busi- nesses, 24% from business associations, 17% from NGOs, and 6% from academic/research institutions (Commission, 2023a). On the other hand, science is complex and difficult to translate into regulatory measures. Science has thus been used to oversim- plify regulatory problems and justify poor regula- tory decisions based on the existence of scientific evidence pointing in a specific direction (Porter, 2020). This is a particular danger in the LLM pub- lic debate. With science becoming increasingly complex, so do the scientific perspectives on how to proceed with this technology.
This literature has raised important concerns re- lating to the immediate and longer- implica- tions around the advancement of machine learning and NLP. However, when positioned in the regu- latory context, we can observe conceptual clashes with frameworks which have been traditionally re- lied upon in public policy and risk regulation. One such framework is the field of risk and uncertainty. Put simply, 'risk is the situation under which the decision outcomes and their probabilities of oc- currences are known to the decision-maker, and uncertainty is the situation under which such in- formation is not available to the Decision-makee (Park and Shapira, 2017). In more technical terms, 'risk is the probability of an event multiplied by its impact, and uncertainty reflects the accuracy with which risk can be assessed' (Krebs, 2011), 1). As a field, risk and uncertainty has made considerable contributions to the development of risk regulation, most notably in relation to environmental regula-
Lessons to be Learned The theoretical underpin- nings of regulation have helped shape a cohesive understanding of the rationale behind regulatory activity. The interest in technology regulation, par- ticularly for disruptive innovations such as LLMs, has exploded in past years across a wide array of scientific disciplines. While understandable, such popularity often leads to inquiries which are not connected to prior knowledge on regulation stud- ies. This reflects more general problem faced by contemporary science, namely that of tackling mul- tidisciplinary issues without multidisciplinary ex- pertise. Considering these circumstances, research on L.MSs and regulation could benefit from en- gaging with the regulation studies and governance context.

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tion (Heyvaert, 2011: Yarnold al., 2022). It is important that policy-makers have a concrete quan- tification of risk (Aumann and Serrano, 2008), in order to determine the adequate level of risk as- sociated with various public policies. In addition, risk determination and management have important economic consequences, specifically for determin- ing 'what level of expenditure in reducing risk is proportionate to the risk itself" (Krebs, 2011).
supporting scientific advancement, their use today has become mundane as part of solving consider- able society problems such as infertility or food availability. These comparisons by no means imply that earlier biotechnological innovations pose the same levels of risk as LLMs or should entail the same level of regulation. However, it is important to learn from our past experiences with technology how distinguish between moral panics and real problems that need scientific solutions.
Especially in the case of technologics that easily transcend physical borders, societies and economies, determining risk and uncertainty is complex undertaking, even for scientists. Some of the factors that make it difficult to assess risk and uncertainty include the complexity of the technol- ogy itself, as well as the information asymmetry underlying commercial practices. LLMs are hu- mongous
Researchers are starting to develop concrete methodologics for the auditing of LLMs and re- lated NLP technologies (Derczynski et al., 2023)," 14 as well as dealing with particular risks such as en- vironmental impact (Rolnick et al., 2022). These contributions are much needed, as they can be trans- lated into concrete measurements of risk and un- certainty, and further lead to the development of policy options in risk management. However, these initiatives are so far too few, as no cohesive scien- tific approach exists on the assessment of the risk and uncertainty posed by LLMs. To date, even the most comprchensive overviews of LLM risks (Weidinger et al., 2022) lack basic methodological practices such as the systematic retrieval of infor- mation from the disciplines of inquiry (Page al., 2021). In some cases, strong projections about risk impact are made without any scientific rigor whatsoever (Hendrycks et al., 2023). Similarly, Dobbe et al. (2021) note that while many technical approaches, including approaches related to 'math- ematical criteria for "safety" or "fairness" have started to emerge. 'their systematic organization and prioritization remains unclear and contested. As a result, the lack of systematization and methodological integrity in scientific work around LLM risks contributes to a credibility crisis which may impact regulation and governance directly.
Transformer- based (Vaswani et al., 2017) models, which have been initially pre-trained as standard language mod- els (Radford et al., 2018) in a vast quantity of text data (mostly web scrapes) and have been also further optimized to follow instructions (Chung al., 2022) and user aligument (Leike al., 2018) with reinforcement learning from human feedback (RLHF) (Christiano al., 2017; Stiennon et al., 2020). Particularly, AI alignment has been a con- troversial topic, since it implies a broad consensus on what sort of values (standards) Al should align with (Gabriel, 2020). As such, LLMs are com- plex technologies where in addition to risk, also
deal with considerable uncertainty, which can be, among others, descriptive (e.g. relating to the vari- ables defining a system), or related to measurement (e.g. uncertainty about the value of the variables in a system) (Gough, 1988).
LLMs as the new GMOs? Yet LLMs are neither the first nor the last technology development posing concerns about wide-spread risks. As an illustra- tion, in the 1970s and 1980s, In- Vntro Fertilization (IVF) was a demonized scientific development con- sidered inhumane, which led to nothing short of a large-scale moral panic (Garber, 2012). In the 2000s, concerns around Genetically Modified Or- ganisms (GMOs) dominated media coverage in European and North American countries, in what deemed 'superstorm' of moral panic and new risk discourses (Howarth, 2013). These are only two examples of risk narratives that were amplified by media coverage in ways that overshadowed im- portant scientific expertise. Yet through regulation
Innovation Governance and Risk Relativization Learning from earlier experiences with risk and un- certainty can also help NLP researchers understand how risk has been dealt with in other policy areas. One of the reasons why it is important to contex- tualize NLP research on risks into a broader reg- ulatory landscape is because this area has already generated meaningful frameworks for the under- standing of risk in the context of innovation gover- nance. Such frameworks include for instance the principle of responsible innovation, which calls for 'taking care of the future through collective steward-
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ship of science and innovation in the present' (von Schomberg, 2013). This is only one of the many other approaches that can guide speiisin-mmaknn on technology regulation (Hemphill, 2020).
between stakeholders from industry, academia and policy relating LLM risks is had on social media platforms such as Twitter. This can pose a regula- tory capture problem. Regulatory capture occurs when the regulator advances the interests of the in- dustry it is supposed to regulate, or of special inter- est groups rather than pursuing the public interest (Carpenter and Moss, 2013). Regulatory capture fits within the private interest theories we explored in Section 2, and often refers to the influence exer- cised by industry over regulation processes (Saltelli et al., 2022). The most recent example is OpenAl's white paper suggesting narrow regulatory interpre- tations for general purpose high-risk Al systems to European regulators 16, Similarly, multiple tech- nology executives of large companies using LLMs, such as HuggingFace or OpenAI have been testi- fying before the US Congress to propose industry- friendly interpretations of AI risks. This is hap- pening in context of existing concerns around the industry orchestration of research agendas in NLP (Abdalla et al., 2023), and science (Abdalla and Abdalla, 2021) in general. However, regula- tion can also be captured by special interest groups from civil society, and increasingly, academia. In this meaning, regulatory capture has cultural value-driven dimension that encompasses 'intellec- tual, ideological, or political forms of dominance' (Saltelli et al., 2022). In a landscape where spe- cial interest groups are increasingly represented by popular science communicators, a lot of questions arise in relation to the power exercised by the ris- ing impact of science influencers. whether from academic, journalism and industry enviromments (Zhang and Lu, 2023).
A regulatory angle can also help with the rel- atívization of risk that is, putting risks into per- spective by considering other policy areas as well. For instance, the UK Risk Register 2020 discusses potential risks and challenges that could cause sig- nificant disruption to the UK 15, The report themat- ically groups six risks (malicious attacks, serious and organized crime, environmental hazards, hu- man and animal health, major accidents and soci- etal risks). This insight is useful in understanding the scale and diversity of risks that public policy needs to account for. Such awareness could also contribute to the generation of policy options that can put LLM risks into perspective in relation to other categories of risks like the ones mentioned above. Here, a more holistic perspective on risk could also take a sectoral approach to LLM risks. For instance, going back to the example of the lawyer who invented case law using ChatGPT, ex- isting legal and self-regulatory frameworks already address the risk of negligence in conducting pro- fessional legal activitics. Considering this context can contribute with insights into whether it is re- ally necessary to treat LLd-mediated information as a novel danger. While technology has gener- ated a broad digital transformation (Verhoef et al., 2021), it adds layers to existing problems (e.g. so- cial inequality) which need policy interventions independent from their digital amplification.
4 Scientific Expertise, Social Media and Regulatory Capture
The Rise of Science Influencers Traditionally, science communication has followed conven- tional model dominated by professional actors gate- keeping information (e.g. scientists, journalists and government). Social media has led to the creation of a networked model of science communication. with underlying socio-technical and political power shifts (van Dijck and Alinejad, 2020). Science in- fluencers are rooted in this development, as well as the broader rise of social media influencers (Goanta and Ranchordás, 2020). They also bring with them additional complexities. They may emerge from scientific background, but may use their plat- forms both for professional as well as personal
Regulatory Capture As a source of evidence for policy-makers, scientific expertise has increasingly played central role in regulation (Paschke et al., 2019). In some supranational governance contexts, scientific expertise is called upon in procedures that often require a certain level of transparency. This is the case of the call for public comments which we have discussed above as part of the EU's 'Better Regulation' agenda. However, in the past decade, the rise of science communication on so- cial media has somewhat changed the interaction between volicy-makees and scientists (van Dijck and Alinejad, 2020). A lot of the public debate
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self-disclosure (Kim and Song, 2016). In doing so, they also become political influencers who 'harness their digital clout to promote political causes and social issues' (Riedl et al., 2023). To signal the social media influence of such public opinion lead- ers, some media outlets even rank them for awards purposes, 17 or profile them vis-a-vis state of the art scientific expertise." 18
Under the premise of basing policy on scientific ev- idence, politicians may rely on research that gains visibility due to amplification by scientific influ- encers, particularly when social media popularity is doubled by the brand power of prestigious aca- demic institutions. Second, by amplifying polar- ized debates that may trigger policy options which are not sufficiently informed through transparent and collective processes of evidence gathering. If multiple AI research groups are vocal on social media about the future of AT research, this fuels a race towards AI regulation. This can take away from the thoroughness that is necessary in collect- ing evidence for such complex field.
This development is especially important since some social media platforms are more relevant for regulators than others. A recent report published by Oxford University's Reuters Institute shows that Twitter is the platform politicians pay most atten- tion to across all studied markets." 19 With this in mind, the polarized narratives around LLM risks unfolding on social media pose the danger that scientific expertise is only partially represented in public debates, in spite of the promises of speech democratization expected from these platforms in the context of science communication.
5 Regulation and NLP (RegNLP): A New Field
The danger of regulatory capture, taken together with the lack of systematization in the identification and measurement of risk and uncertainty around LLMs, calls for a cohesive scientific agenda and strategy. In 2023, the world counts 8 billion hu- mans 20 and further digitalization and automation are not only unstoppable, but also absolutely nec- essary. Technological innovation is currently vital in the governance of our society. That does not mean its pursuit ought to be free from regulatory frameworks mandating rules on how to deal with the risks poses. NLP research has already drawn attention to some of the potential risks of LLMs. To consolidate this effort, it is necessary to consider in what direction NLP research can further develop and what contributions it can make to regulation. A concrete proposal we advocate for is the creation of field of scientific inquiry which we call Regulation and NLP (RegNLP). RegNLP has three essential features which we discuss below.
While followers and engagement may be a mea- sure of popularity with some communities and stakeholders, it raises concerns relating to the role popularity metrics and algorithmic amplification on social media may have in representngg scien- tific or industry consensus before policy-makers. As Zhang et al. (2018) put it, there is popularity bias that means "attention tends beget attention'. other words, 'the more contacts you have and make, the more valuable you become, because peo- ple think you are popular and hence want to connect with you' (van Dijck, 2013).
How exactly scientific popularity influences reg- ulation still needs to be explored in greater detail, particularly as a novel example of potential regu- latory capture. What we know so far is that social media influencers can be highly effective in rely- ing on authenticity and para-social relations for persuasion purposes (Vannini and Franzese, 2008; Hudders et al., 2021). In this context, popularity determines power relationships within social me- dia networks that may capture regulatory processes in two ways. First, by exercising persuasion over policy-makers as audiences through visibility and popularity. Simply put, not all research that is avail- able in a given field is presented on social media.
5.1 Multidisciplinarity
First, RegNLP needs to be multidisciplinary field that spans across any scientific areas of study which are relevant for the intersection of regulation and AL In the past years, multidisciplinary communi- ties have been increasingly popular. An example is the ACM Fairness, Accountability and Trans- parency Conference (FAccT")), which often fea- tures NLP research. Such research communities
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form around the disciplines that are most reflected by their research questions. For RegNLP, the con- stitutive disciplines ought to include NLP and reg- ulation studies but also law, economics, political science, etc.. NLP research approaches cannot re- place expertise from other fields. At the same time, expertise entails more than an interest in an adja- cent field, but rather deep understanding of the contributions and limitations such field can entail when interacting with NLP. One strategy to encour- age this cross-po pross-pollination is for NLP researchers interested in regulation to co-author papers with regulation experts and other relevant scholars. In doing so, RegNLP can also contribute to the re- search gaps in other fields, such as public admin- istration, where literature on Al still needs further development. For instance, in 2019, only 12 sci- entific articles were published on Al and public policy and administration, mostly focused on the use of Al in public administration (Valle-Cruz et al., 2020). Similarly, a quick search in 'eguulttion & Governance', a leading journal in regulation stud- ies, yields total of 18 results, out of which only one discusses AI risks (Laux et al., 2023).
information and overviews from existing scientific rescarch, such meta-studies (Heijden, 2021).
5.3 Science Participation in Regulation
RegNLP can help research on regulation and NLP interface with regulatory processes. At time of increased complexity, it is important for scientists to clarify the state of art of fast paced technolog- ical change. Using harmonized methodologies in the context of multidisciplinary research agenda can bring much needed coordination to the inter- action between NLP development and regulation. In the absence of such coordination, as we have discussed in Section 4, there is a potential dan- ger that policy-makers are only exposed to popular scientific opinions instead of consolidated science communication. What is more, embedding Reg- NLP into a risk and regulation context can offer further inspiration for the role of academia, as a repository of public trust. A lot of regulatory agen- cies and standardization bodies govern the imple- mentation of regulation. In addition, new forms of interactions with civil society are being set up by EU regulation, such as the Digital Services Act's 'trusted flaggers', namely organizations that can flag illegal content on online platforms. Similarly, there can be new roles play for RegNLP agendas and communities.
5.2 Harmonized Methodologies
RegNLP needs harmonized methodologies. One of the biggest problems with the consolidation of multidisciplinary research agendas and communi- ties relates to the lack of alignment between the different methods and goals pursued by different researchers. This issue trickles down into all rel- evant activities which normally help consolidate multidisciplinary groups, and is most specifically visible in the process of peer review. If review- ers are not familiar with methodologies from other fields, they will be unable to adequately assess the quality of research (Laufer et al., 2022). This can lead to the publication of research which may be interesting across disciplines, but which may not meet the methodological rigor of the scientific dis- cipline to which a given method pertains.
Conclusion
LLMs reflect a momentous development in NLP re- search. As they unleash a new era of automation, it is important to understand their risks and how these risks can be controlled. While eager to engage with regulatory matters, NLP research on LLM risks has so far been disjointed from other fields which are of direct interest, such as regulation studies. In particular, the field of risk and uncertainty has been conceptualizing and discussing scientific risks for decades. In this paper, we introduced these two areas of study and explained why would be bene- ficial for NLP research to consider them in greater depth. In doing so, we also raised concerns relat- ing to the fact that a lot of scientific debates on NLP risks are taking place on social media. This may lead to regulatory capture, or in other words the exercise of inffuence over law-makers, who are notoriously active on social media platforms. To tackle these issues, we propose new multidisci- plinary area of scientific inquiry at the intersection of regulation and NLP (RegNLP). aimed at the
NLP can help establish shared standards for scientific quality around shared methodologies and science practices. This can mean embracing di- verse methodological scope to reflect the tools that are needed in the inquiry of different types of re- search questions. can also mean perfecting exist- ing methods and deploying them on novel sources of data, as NLP research methods are a natural starting point for the systemic retrieval of complex

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development of a systematic approach for the iden- tification and measurement of risks arising out of LLMs and NLP technology more broadly.
Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Chowe Aakanksha Chowdhery. Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du Ethan Dyer, Vlad Feinberg, Fangxiaoyu Feng. Vlad Fienber, Markus Freitag, Xavier Gar- cia, Sebastian Gehrmann, Lucas Gonzalez, Guy Gur- Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hur- witz, Michael Isard, Abe Ittycheriah, Matthew Jagiel- ski, Wenhao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Ben- jamin Lee, Eric Li, Music Li, Wei Li, YaGuang Li, Jian Li, Hyeontaek Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra. Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nys- trom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif. Bryan Richter, Parker Riley, Alex Castro Ros, Au- rko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R. So. Daniel Sohn, Simon Tokumine, Dasha Valter. Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang. John Wiet- ing, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting Xue, Pengcheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng, Weikang Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. 2023. Palm 2 technical report.
Limitations
Our paper reflects on the future of NLP in land- scape where interest in regulation is increasing ex- ponentially, within and outside the field. Given the nature of this paper, we will refer to limita- tions dealing with the feasibility of our proposed research agenda. The most important limitation reflects the discussion around inter- and multidis- ciplinarity. This is by no means a new theme in science, and its implementation has cultural, man- agerial and economic implications that we do not discuss in the paper, but which are important to acknowledge. Similarly, another limitation is re- flected by the modest amount of knowledge we have relating to the impact of social media influ- encers (such as science influencers) on regulation and public policy. In this paper, we raise certain issues around science communication as the start- ing point of broader discussion around power and influence in law-making as amplified by social media.
Acknowledgements
Ilias Chalkidis is funded by the Novo Nordisk Foun- dation (grant NNF 20S A0066065,, Sofia Ranchor- das is funded by the NWO- project 'Vulnera- bility in the Digital Administrative State'. Catalina Goanta is funded by the ERC Starting Grant HU- MANads 2ERC-2021-StG No 101041824).
Robert J Aumann and Roberto Serrano. 2008. An eco- nomic index of riskiness. J. Polit. Econ., 116(5):810- 836.
Solon Barocas, Moritz. Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org. http:/ /ttp://www.ffff fa irmlbook. org.
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Reviews:
[{"review_id": "qJ8ck74dPn", "reviewer": "Reviewer_kJVK", "summary": "", "questions": "- How does your proposed RegNLP fit in with the widespread efforts for addressing NLP risks and safety through regulation and other than get it published, how do you intend to promote your views to those entities?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer kJVK for their useful feedback. We respond to all of their points below:\n\n> A lot of ground has been covered. I hope in the final camera-ready version the authors will include a section where they also talk about how the introduction of experts may help NLP regulation. I think a lot of recent applied-NLP papers in areas of healthcare, mental health, social media, and cultural analytics, have taken guidance from experts to make a case for trustworthy NLP. I think in the final paper, given extra space having a section on this would be a good addition.\n\nThis is a great suggestion, thanks. In the camera-ready version, we plan to elaborate further on how regulation frameworks can advance NLP research, but also, as suggested by reviewer kJVK, how NLP expertise can help regulation. More specifically, we will highlight concrete benefits for evidence-based decision-making, such as the improvement of ‘agile regulation’ ([King, 2023](https://www.theregreview.org/2023/08/02/king-regulation-must-become-agile-to-remain-relevant/)) through NLP expertise. In a nutshell, this position paper engages with one of the most crucial debates about the future of LLM: its wider implementation and regulation. The importance of regulatory studies in NLP research has been visible not only in numerous regulatory debates in the European Union but also in the United States. Some regulators such as the Italian Data Privacy Authority already consider issuing prohibitive regulations to limit the use and development of LLMs. Scholars from different sciences and policymakers are increasingly turning to regulation for solutions regarding risk management. However, scholars outside Regulatory studies are often less informed about the intricacies of regulations and may easily regard it as a panacea. This paper introduces NLP scholars to key regulatory concepts and theories of regulation. Therefore, the paper's focus on addressing regulatory concerns and risks associated with LLMs is both timely and highly relevant to the NLP community and other stakeholders.\n\n> The paper has insufficient technical depth and primarily centers around regulatory studies and risk assessment, which might make it more appropriate to a different type of conference.\n\nWe would like to acknowledge that our paper does not make any empirical or technical contributions since it is a position paper. However, position papers are considered as legitimate contributions according to the EMNLP call for papers (see section [Contributions](https://2023.emnlp.org/calls/main_conference_papers/) at EMNLP CfP). In particular, we believe that our contribution is well-aligned with the theme track ‘Large Language Models and the Future of NLP’. Our position paper is the product of a  multidisciplinary collaboration between legal scholars specialized in AI regulation and NLP experts. Furthermore, a position paper such as ours is also crucial to inform the direction that research on Large Language Models is taking. In addition, an empirical approach does not fit well with a position paper that aims to open new research paths for others and offer a novel and interdisciplinary research framework. We hope that you will consider the value of a position paper and its ability to contribute to inform future empirical and non-empirical research in the field.\n\n> The \"RegNLP\" framework is quite loosely defined.\n\nThanks for the suggestion to make RegNLP more specific. Our paper defines RegNLP as a proposed field of scientific inquiry at the intersection of regulation studies and NLP (p. 7). RegNLP has three main characteristics: \n- *it is multidisciplinary*, therefore remedying the current knowledge gap when discussing regulatory implications in the absence of theoretical and practical frameworks from regulation studies as the scientific field that specializes on regulation.\n- *it relies on harmonized methodologies*, which is one of the biggest pitfalls of existing multi/interdisciplinary research fields (e.g. FAccT). Our paper does not propose the methods themselves, but rather establishes the need for an NLP community-wide discussion about harmonized methodologies. \n- *it contributes to science participation in regulation*, through the systematization of research on NLP and regulation aimed at informing public policy processes. \nWe will clarify these points further given the extra space upon acceptance.\n\n> How does your proposed RegNLP fit in with the widespread efforts for addressing NLP risks and safety through regulation and other than get it published, how do you intend to promote your views to those entities?\n\nThe third characteristic of the proposed field of RegNLP is its contribution to science participation (see p. 8: ‘At a time of increased complexity, it is important for scientists to clarify the state of art of fast paced technological change’). By systematizing multidisciplinary knowledge on regulation and NLP, this field can inform public policy processes (e.g. in the adoption of new laws), and also contribute to the development of emerging regulatory approaches to innovation, such as ‘agile regulation’ ([King, 2023](https://www.theregreview.org/2023/08/02/king-regulation-must-become-agile-to-remain-relevant/)). Agile regulation consists of new forms of regulation (e.g. sandboxes, where new technologies are tested in an experimental setup with the support of the state, see [Ranchordas, 2021](https://www.lawandmethod.nl/tijdschrift/lawandmethod/2021/12/lawandmethod-D-21-00012)). When this regulation is aimed at technology, it is essential that scientific expertise is part of the policy process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Concerns about the safety of LLMs is at an all time high, so the paper's focus on addressing regulatory concerns and risks associated with LLMs is both timely and highly relevant to the NLP community.\n- The paper explores a novel intersection between NLP research and regulatory studies, which is not an avenue that has been explored or emphasized before. This perspective is likely to result in \"for\" and \"against\" discussions, the ultimate outcome which is likely to be beneficial in addressing current concerns as a whole. Engaging the EMNLP community in these discussions is important \n- The paper nicely overviews current endeavors for regulating NLP and highlights some of their short comings (from the point of view of the authors). \n- The paper emphasizes the need for systematic methodologies and rigorous evaluation when assessing risks. \n", "reasons_to_reject": "- The paper has insufficient technical depth and  primarily centers around regulatory studies and risk assessment, which might make it more appropriate  to a different type of conference.\n- The \"RegNLP\" framework  is quite loosely defined. \n", "questions_to_authors": "- How does your proposed RegNLP fit in with the widespread efforts for addressing NLP risks and safety through regulation and other than get it published, how do you intend to promote your views to those entities?", "paper_topic_and_main_contributions": "This paper seeks to promote a  balanced and informed discussion surrounding regulation associated with advances in NLP with emphasis on LLMs.  Specifically, the paper addresses the challenges posed by the rapid progress of LLMs, by advocating for a multidisciplinary approach that integrates insights from regulatory studies into NLP research so as address the  risks, and ethical considerations brought on by such models,  while ensuring the scientific integrity of NLP research endeavors.\n\n\n\n\n\n", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Dt371u6eZ7", "reviewer": "Reviewer_dLf1", "summary": "", "questions": "A) What is the difference between governance and regulation?\nB) You advocate for a new field of RegNLP. Can you elaborate on what the first concrete steps would be to actually realize this vision? What are some \"quick wins\" that can demonstrate value and build momentum?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer dLf1 for their constructive feedback. See our responses to the points you raised below:\n\n> Hypothetical nature - As a thought experiment, the ideas may be seen as too abstract without empirical grounding or tangible steps for realizing the RegNLP proposal.\n\nWe first want to acknowledge that our paper does not have empirical grounding since it is a position paper. However, our work is grounded on theoretical and normative frameworks in regulatory studies. Moreover, position papers are considered as legitimate contributions according to the EMNLP call for papers (see section [Contributions](https://2023.emnlp.org/calls/main_conference_papers/)). In particular, our contribution is well-aligned with the theme track ‘Large Language Models and the Future of NLP’. Our paper is a multidisciplinary collaboration between two legal scholars specialized in AI regulation and three NLP experts. It is rooted in a palpable research gap identified in the paper (p. 2), surrounding the need to familiarize the NLP research community with sound regulation frameworks. In a nutshell, this position paper engages with one of the most crucial debates about the future of LLM: its wider implementation and regulation. The importance of regulatory studies in NLP research has been visible not only in numerous regulatory debates in the European Union but also in the United States. Some regulators such as the Italian Data Privacy Authority have considered issuing prohibitive regulations to limit the use and development of LLMs. Scholars from different sciences and policymakers are increasingly turning to regulation for solutions regarding risk management. However, scholars outside Regulatory studies are often less informed about the intricacies of regulations and may easily regard it as a panacea. This paper introduces NLP scholars to key regulatory concepts and theories of regulation. Therefore, the paper's focus on addressing regulatory concerns and risks associated with LLMs is both timely and highly relevant to the NLP community.\n\n> Overstatement and lengthy paper- The critiques, while valid, could be construed as exaggerating the current problems or polarization in the discourse.\n\nThe critique aims to do the opposite, namely argue that there is a lot of space outside of the polarized discourse, for constructive arguments relating to how a scientifically sound field of LLM regulation could look like. The paper aims to highlight regulation theories and science communication, and therefore bringing non-technical contributions focused on defending the need for a new field of study that focuses on both NLP and regulation studies. However, we will try our best to revise our critique to make it more brief in the camera ready upon acceptance as you suggested.\n\n> The paper is light on details about operationalizing its recommendations, so applicability could be questioned.\n\nThis is a position paper that highlights the need for a new field of study in NLP. It critically addresses some of the current pitfalls of NLP research discussing regulatory implications without actual regulation knowledge. In the camera-ready version, we plan to elaborate further on how regulation frameworks can advance NLP research, and how NLP expertise can help regulation. More specifically, we will highlight concrete benefits for evidence-based decision-making, such as the improvement of ‘agile regulation’ ([King, 2023](https://www.theregreview.org/2023/08/02/king-regulation-must-become-agile-to-remain-relevant/)) through NLP expertise. \n\n**Questions**\n\n> A) What is the difference between governance and regulation?\n\nRegulation and governance are two social science concepts that deal with the way in which public and private actors (e.g. public actors such as state bodies like a federal regulator of telecommunications, but also private actors such as companies like Facebook) coordinate and rule socio-economic and legal spaces. Governance focuses on primarily informal systems to organize groups and individuals, often without coercion (e.g., a financial sanction). Governance is a broader term that encompasses multiple organizational systems and instruments and regulation is one of them. Regulation includes both formal and informal instruments, public and private measures used to shape individual and group behavior, and it may be accompanied by formal sanctions to ensure compliance. In the meaning of one of the most prominent regulation scholars also cited in the paper, governance is a broader term than regulation (Black 2002), as regulation is one of the tools used in governance, dealing with controlling the behaviors of regulated actors. We will make this more clear in the camera ready.\n\n> B) You advocate for a new field of RegNLP. Can you elaborate on what the first concrete steps would be to actually realize this vision? What are some \"quick wins\" that can demonstrate value and build momentum?\n\nIn the camera-ready version, by further building on the example of agile regulation, we will propose steps to further regulatory theory and multidisciplinary practice such as: \n- Contributing to the development of regulation theory that reflects the fast pace of technology. Agile regulation consists of new forms of regulation (e.g. sandboxes, where new technologies are tested in an experimental setup with not only the support of the state but also its close collaboration,, see Ranchordas, 2021). When this regulation is aimed at technology, it is essential that scientific expertise is part of the policy process.\n- Proposing NLP research activities (e.g. shared tasks; workshops) on public interest cases as a form of experimental regulation: in the example that we address on p. 8, this can entail operationalizing the trusted flaggers concept used by the EU’s recent Digital Services Act, which allows various organizations to detect, monitor and report illegal content on online platforms. Illegal content is any content that goes against EU law or national law that is in accordance with EU law. Trusted flagger organizations that use academic NLP expertise could be offered a special regulatory framework (e.g. special rules that apply only to them in specific cases, or for a specific period of time), in order to test the reliability, usefulness and appropriateness of state of the art NLP technologies (e.g. LLMs) in detecting illegal content.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": " It engages with an important ongoing debate about regulating LLMs that needs more nuanced perspectives beyond hype and fear-mongering. The paper can catalyze more transparent and constructive dialog.", "reasons_to_reject": "- Hypothetical nature - As a thought experiment, the ideas may be seen as too abstract without empirical grounding or tangible steps for realizing the RegNLP proposal.\n- Overstatement and lengthy paper- The critiques, while valid, could be construed as exaggerating the current problems or polarization in the discourse.\n- The paper is light on details about operationalizing its recommendations, so applicability could be questioned.", "questions_to_authors": "A) What is the difference between governance and regulation?\nB) You advocate for a new field of RegNLP. Can you elaborate on what the first concrete steps would be to actually realize this vision? What are some \"quick wins\" that can demonstrate value and build momentum?", "paper_topic_and_main_contributions": "This paper appears to be a thought experiment that critically examines the current discourse on regulating large language models (LLMs) in NLP research. It makes the following key points:\n\n- The paper argues that the current debate on regulating LLMs has become polarized between voices from industry and academia, amplified by social media. This poses risks of regulatory capture, where regulators advance interests of influential groups rather than the public interest.\n-It advocates bringing in perspectives from regulation studies to inform and systematically frame the discussion around risks, uncertainty, and governance of LLMs. \n-It proposes creating a new multidisciplinary research area on \"Regulation and NLP\" (RegNLP) with harmonized methodologies to properly assess risks and transparently engage with policymakers.", "excitement": 3, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "dtaEEcZaUk", "reviewer": "Reviewer_6v9w", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer 6v9w for their useful feedback. See our responses to the points you raised below:\n\n> Nothing major, I think this is overall a good paper. I do think that some additional details about current practices in NLP that have arisen since LLM introduction and how we can think of those in terms of regulation would be helpful.\n\nThis is a very good point. We will expand on this in the camera ready given the extra space. We already acknowledge on p. 4 the wide scope of NLP literature aimed at improving NLP research practices. In the camera-ready version, we will further clarify additional regulatory issues that have arisen in the light of current NLP practices (e.g., copyright litigation, open access classifications, but also annotation biases), but also how more critical research has been trying to guide NLP research (e.g. [Kapoor et al, 2023](https://reforms.cs.princeton.edu/#about)). We also plan to elaborate further on how regulation frameworks can advance NLP research, and how NLP expertise can help regulation, especially by exploring more nuanced views than the public on AI development ([O’Shaughnessy et al. 2023](https://academic.oup.com/spp/article-abstract/50/2/161/6760880?redirectedFrom=fulltext)). More specifically, we will highlight concrete benefits for evidence-based decision-making, such as the improvement of ‘agile regulation’ (King, 2023) through NLP expertise. Agile regulation consists of new forms of regulation (e.g. sandboxes, where new technologies are tested in an experimental setup with the support of the state, see [Ranchordas, 2021](https://www.lawandmethod.nl/tijdschrift/lawandmethod/2021/12/lawandmethod-D-21-00012)). When this regulation is aimed at technology, it is essential that scientific expertise is part of the policy process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The examples are well cited, URLs are properly mentioned, and every sentence or fact is helped up by a paper or proper discussion and citation. This is a good practice and shows proper thorough research. \n\n2. There are a lot of examples and they are quite varied, ranging from talking about proper definitions, prior work, risk of LLMs, risk of innovations, and even touches upon modern-day influencers. \n\n3. A lot of ground has been covered. I hope in the final camera-ready version the authors will include a section where they also talk about how the introduction of experts may help NLP regulation. I think a lot of recent applied-NLP papers in areas of healthcare, mental health, social media, and cultural analytics, have taken guidance from experts to make a case for trustworthy NLP. I think in the final paper, given extra space having a section on this would be a good addition. ", "reasons_to_reject": "Nothing major, I think this is overall a good paper. I do think that some additional details about current practices in NLP that have arisen since LLM introduction and how we can think of those in terms of regulation would be helpful. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper talks about the current landscape of AI and NLP in the era of large generative language modeling that seeks to permeate every space around us. The paper talks about the shortcomings in current regulatory policies that surround AI and LLMs. They also talk about the rapid spur of innovation and the exponential increase in discussion about LLMs even from non-scientific groups which may also lead to misinformation. Throughout the paper, the authors also provide a good amount of examples and a thorough discussion of the space that they explore. \n\nI think given this year's theme track, it will be a good fit for the conference.", "excitement": 3, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This position paper discusses issues surrounding regulation of LLMs, including risks, ethical considerations, and scientific integrity. This topic is important and timely and deserves further discussion in our community. Concerns about the paper mainly revolve around the applicability of the paper, due to the hypothetical nature of this paper and the lack of concrete recommendations.
Decision: Accept-Main