CSET：2023年控制大型語言模型輸出入門（英文版）（19頁）.pdf

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1、Issue BriefDecember 2023Controlling Large Language Model Outputs:A PrimerAuthorsJessica JiJosh A.GoldsteinAndrew J.Lohn Center for Security and Emerging Technology|1 Executive Summary Concerns over risks from generative artificial intelligence(AI)systems have increased significantly over the past ye

2、ar,driven in large part by the advent of increasingly capable large language models(LLMs).Many of these potential risks stem from these models producing undesirable outputs,from hate speech to information that could be put to malicious use.However,the inherent complexity of LLMs makes controlling or

3、 steering their outputs a considerable technical challenge.This issue brief presents three broad categories of potentially harmful outputsinaccurate information,biased or toxic outputs,and outputs resulting from malicious usethat may motivate developers to control LLMs.It also explains four popular

4、techniques that developers currently use to control LLM outputs,categorized along various stages of the LLM development life cycle:1)editing pre-training data,2)supervised fine-tuning,3)reinforcement learning with human feedback and Constitutional AI,and 4)prompt and output controls.None of these te

5、chniques are perfect,and they are frequently used in concert with one another and with nontechnical controls such as content policies.Furthermore,the availability of open modelswhich anyone can download and modify for their own purposesmeans that these controls or safeguards are unevenly distributed

6、 across various LLMs and AI-enabled products.Ultimately,this is a complex and novel problem that presents challenges for both policymakers and AI developers.Todays techniques are more like sledgehammers than scalpels,and even the most cutting-edge controls cannot guarantee that an LLM will never pro

7、duce an undesirable output.Center for Security and Emerging Technology|2 Introduction Large language models(LLMs)are powerful AI models that can generate all kinds of text outputs,from poetry and professional emails to recipes and computer code.They have diffused broadly in recent months and are pro

8、jected to have important societal ramifications.For example,venture capitalists and large tech companies alike have poured funding into LLM development and application-layer products built on top of these tools,and researchers expect LLMs to be broadly integrated into society and the economy in the

9、years ahead.1 Despite their popularity and promise,LLMs are also capable of generating outputs that are hurtful,untrue,and,at times,even dangerous.In response to their increasing popularity,many people have asked:How do AI developers control the text that a language model generates?In this primer,we

10、 tackle this question directly and present a high-level overview of how AI developers attempt to prevent LLMs from outputting harmful or otherwise undesirable text.*In the first section,we begin with a brief motivation of why developers seek to control or influence model outputs.In the second sectio

11、n,we describe some relevant features of the language model development pipeline before diving deeper into four classes of techniques that developers often use in practice in the third section.The fourth section provides context on why the open vs.private model paradigm further complicates developers

12、 efforts to control and safeguard the outputs of their models.A common theme across the various methods to limit harmful content is that none are perfect.Whether a particular intervention is successful frequently depends on how dedicated the user is to generating malicious or subversive text,and may

13、 be a matter of degree(how frequently the model produces undesirable text)rather than absolutes(whether it will produce the undesirable text at all).*These practices can also be thought of as“aligning”LLMs with their developers policies or guardrails.AI alignment is a broader research area that gene

14、rally focuses on ensuring AI systems behave in ways that correspond to human values or human intentions.Center for Security and Emerging Technology|3 Why Control Large Language Model Outputs?Although interacting with a language model may occasionally feel like interacting with a real person,these sy

15、stems are not human.Instead,language models are essentially complex probability-calculating machines.They establish relations between language tokenswords,phrases,parts of words,or even punctuation marks and grammatical symbolsand calculate the probability for each of them to come next in response t

16、o a given prompt.The models repeatedly choose one of the most likely tokens until their outputs are complete.Importantly,this means that language models have no underlying understanding of factualness or truthfulness,nor are they retrieving information from any single source.They are more akin to“im

17、prov machines”2:they excel at replicating patterns but have no built-in way to verify whether or not their outputs are useful,correct,or harmful.3 While a full taxonomy of risks falls outside the scope of this piece,we highlight some of the risks that motivate interest in controlling LLM outputs.4 F

18、irstly,some risks result from ordinary users simply receiving incorrect information.Users have already shown a propensity to misunderstand the limitations of these systems and inappropriately cite LLMs,thinking they provide factual information5(an example of what AI researchers call“overreliance”6).

19、The range of potential failures is vast.Users depending on the system for health information who are fed false advice could put themselves at risk.Users turning to models for information about politics who receive false information may lose faith in candidates without justification,undermining the d

20、emocratic process.As people use language models more frequently,the risks associated with overreliance will likely grow.Secondly,content does not need to be demonstrably false to cause harm.This leads to another set of concerns that can occur when language models produce text that is biased(e.g.,reg

21、arding race,gender,religion,or other categories)or toxic.Research has tested and found evidence of biases related to political ideology,religion,gender,and more in specific models.7 Another line of research has traced biases in language models to the training data and noted that content excluded fro

22、m training data based on certain keywords can disproportionately remove text from and about members of various minority groups.8 Toxic content from LLMs may be particularly problematic if shown to children or other vulnerable groups.Finally,there are also worries about bad actors using language mode

23、ls intentionally for“malicious use.”9 One worst-case scenario that has received public attention is the risk of a bad actor using a language model to learn how to create a homegrown bomb Center for Security and Emerging Technology|4 or a bioweapon,although future research is needed to understand the

24、 relative risk(e.g.,assessing the risk compared to information already available on Google).10 Other troubling scenarios center on different types of malicious behavior such as the use of language models to facilitate hacking,scamming,or generating disinformation articles.11 In all these cases,a str

25、ategy to prevent a model from outputting harmful materials would likely center on having it refuse to return such content outright.How Large Language Models are Developed As described above,language models are essentially complex probability-calculating machines.In order to understand how AI develop

26、ers attempt to control their outputs,it is useful to first understand the process by which they are created,and how every stage of this process influences the system that ultimately ends up interacting with human end-users.“Language model”is a general category describing a class of AI models that ge

27、nerate natural language text outputs.Large language models,or LLMs,are particularly powerful language models that are trained on especially large quantities of text,much of it scraped from the open internet.12 While there is no strictly defined boundary between a regular language model and an LLM,a

28、models“largeness”is generally a question of scale,both in terms of data and computational power.Training an LLM is a long and computationally expensive process,with some of todays most cutting-edge models requiring thousands of powerful computer chips and hundreds of millions of dollars to create.13

29、 The most capable models are currently privately developed and protected as corporate intellectual property,but increasingly capable alternatives have been released openly for public use or adaptation.First,models are pre-trained on large general-purpose text datasets to learn correlations among tok

30、ens found in this natural language text.While some training datasetsconsisting mostly of data scraped from publicly accessible web archivesare available for open inspection and use,14 the exact composition of data sources used to train todays LLMs is largely unknown.Even AI developers generally do n

31、ot have total visibility into the contents of their training datasets because the quantity of data required to pre-train an LLM is often in the scale of hundreds of terabytes.15 After this initial training,they are commonly fine-tuned,at least once,on smaller,more specialized datasets to improve the

32、ir performance in a specific area.There are different types of fine-tuning for different purposes:reinforcement learning with human feedback attempts(described further in the following section)to guide models behavior using input from humans,while other types of fine-tuning might train a model Cente

33、r for Security and Emerging Technology|5 more on data for a certain application or style in order to improve the models capability to generate that kind of text specifically.These training steps are often repeated,with multiple rounds of iterative testing and evaluation to monitor model performance.

34、Finally,some fully-trained models are deployed for use,whether through a user-facing interface like a chatbot or via an API(application programming interface).The same model can be deployed in different forms;for example,OpenAIs GPT-4 has been deployed as both the LLM that powers ChatGPT and can als

35、o be accessed directly via its API,which allows third-party developers to integrate it into their software products without having direct access to the model.Another popular option for developers is to open-source their model,which allows anyone to access its underlying code,fine-tune it to their ow

36、n specifications,and use it to build their own applications.Figure 1:Stages in the AI development pipeline and some associated language model control techniques.Source:CSET.Each of these steps in the development process offers opportunities to shape the models behavior.In the following section,we di

37、scuss four classes of techniques that LLM developers use to steer outputs.Center for Security and Emerging Technology|6 Four Techniques to Control LLM Outputs 1.Editing Pre-training Data Since language models predictive power derives from correlations in the text they are trained on,a common misconc

38、eption about LLMs is that their outputs can be easily steered by manipulating or editing their training data.A model whose training data contains no reference to a particular word,for instance,is extremely unlikely to output anything containing that word.However,real-world pre-training is much more

39、complicated.Considering the sheer volume of data that these models are pre-trained on,it is extremely difficult to predict how changing their training data will affect their performance or their propensity to output certain types of content.For example,one study found that filtering a dataset reduce

40、d one language models likelihood of generating harmful text,but the filtered versions also consistently performed worse on standard performance benchmarks than their unfiltered counterparts.16 Many LLM developers are wary of techniques that decrease model performance,especially if other techniques c

41、an also be used to control outputs.Data filtering methods can also backfire and lead to other unwanted outcomes,like erasing dialect patterns or marginalizing certain groups within the models training data.17 And while data augmentationsupplementing training data with examples of desired outcomeshas

42、 shown some promise,it is very difficult to effectively scale in order to reduce bias in large models.18 Ultimately,while training data manipulation is theoretically a powerful mechanism to control model behavior,it is not a panacea for preventing many types of harmful output,especially when meaning

43、 and harm are context dependent.19 Even though factors like content filters and data sources can ultimately have significant effects on the fully trained models behavior,20 researchers have yet to fully understand exactly how to manipulate data in a way that will have meaningful impacts on the resul

44、ting model while minimizing performance loss.Smaller,specialized language models that are pre-trained on curated datasets are likely to have more success with data filtration or augmentation,but LLM developers are likely to rely on other methods in order to steer their models.Center for Security and

45、 Emerging Technology|7 2.Supervised Fine-Tuning Once a model has been pre-trained,developers can continue to adjust its behavior by training it further on a specialized dataset.This process,known as supervised fine-tuning,is one of the most common ways to modify a language model,usually in an effort

46、 to improve its performance in a particular area.To make a general-purpose model like OpenAIs GPT-4 better at math,for instance,an intuitive solution is to train the model on math problems to improve its ability to recognize patterns in that particular domain.The more high-quality data a model has b

47、een exposed to that is relevant to a specific topic,the better the model will be at predicting the next token in its output in a way that is likely to be useful to human users.Supervised fine-tuning can be quite powerful in the right context when the right kind of data is available,and is one of the

48、 best ways to specialize a model to a specific domain or use case.(“Supervised,”in this context,refers to the fact that the model is provided with labeled data and thus does not have to perform the prerequisite step of learning patterns and associations within the data.)For example,fine-tuning an LL

49、M on a collection of datasets described via instructionsconsisting of specific tasks or requests,such as“can we infer the following?”or“translate this text into Spanish”significantly improves the models ability to respond to prompts in natural language.21 This technique,now known as instruction tuni

50、ng,has been instrumental in creating LLM chatbots that can interpret all different kinds of inputs,from simple questions and declarative statements to lists of multi-step instructions.Fine-tuning is a broadly applicable specialization technique that can be applied in certain contexts to steer model

51、behavior.Some research has shown that this technique can not only improve a models performance in a particular area22 but can also compensate for bias inherited from the pretrained model.23 These biases,which can include those related to protected categories like race or gender,stem from statistical

52、 patterns that the model has learned from its training data.A training dataset that only consists of images of male doctors,for example,will result in a model that will consistently produce images of men when prompted with“doctor.”Fine-tuning the model on a more balanced dataset can be one way to co

53、rrect this issue.However,effective supervised fine-tuning depends on access to specialized and high-quality datasets,which may not be available in all domains or accurately capture the behavior that researchers are attempting to control.Researchers have therefore looked to develop alternative techni

54、ques that are either not as reliant on specialized data or that allow for a more flexible way to steer an LLMs behavior.Center for Security and Emerging Technology|8 3.Reinforcement Learning with Human Feedback(RLHF)and Constitutional AI Two techniques often used to complement supervised fine-tuning

55、 employ reinforcement learning,which is the process of training a machine learning model to make decisions via many iterations of trial and error.Over the course of the training process,the model receives either negative or positive feedback which gradually“teaches”it to take the series of actions t

56、hat will maximize the amount of positive feedback.Given the right conditions,reinforcement learning can be extremely effective and powerfulGoogle DeepMinds AlphaGo Zero system,an earlier version of which famously beat the human Go world champion in 2016,24 was primarily trained using reinforcement l

57、earning.While playing millions of games against itself over the course of several days,AlphaGo Zero repeatedly updated its own parameters to select better and better moves.25 Since reinforcement learning already incorporates a built-in feedback process,AI researchers leveraged it to create new techn

58、iques for fine-tuning LLMs.Reinforcement learning with human feedback(RLHF)is a technique in which an LLM is fine-tuned with the help of a different machine-learning model,known as a“reward model.”This reward model is trained on some of the original LLMs text outputs,which human annotators have rank

59、ed based on some set of guidelines or preferences.The goal of the reward model is to encode human preferences:once given some text input,it outputs a numerical score which reflects how likely humans are to prefer that text.The reward model then serves as the basis for the feedback mechanism used to

60、fine-tune the original LLM:as the original LLM outputs text,the reward model“scores”that output and with each iteration,the original LLM adjusts its output to improve its“score.”26 Put more simply,RLHF attempts to train an LLM to generate outputs that humans are more likely to deem acceptable.Its pe

61、rhaps most well-known for its role in turning OpenAIs GPT-3.5 into ChatGPT,27 and has been remarkably successful at producing LLMs that interact with users in human-like ways.Unlike supervised fine-tuning,which is typically used for creating a specialized model and does not necessarily involve steer

62、ing a model based on any sense of“right”or“wrong,”RLHF centers on the principle that human preferences should play a role in how an LLM behaves.The“human feedback”aspect of RLHF is its central component and also its greatest limitation.For example,in 2022 a team of OpenAI researchers hired 40 contra

63、ctors to create and label a dataset of human preferences.28 Today,data annotators around the world spend hours rating interactions with pre-deployed versions of AI systems like ChatGPT and Googles Sparrow.29 As long as human labor is necessary for RLHF,LLM creators will naturally face limitations on

64、 how much human Center for Security and Emerging Technology|9 feedback their models will receive because of the sheer time and cost of such measures.30 Furthermore,RLHF is tricky to implement even with enough feedback.A poorly designed feedback process may result in the model learning how to act in

65、ways that maximize the amount of positive feedback it receives but that may not actually translate into the kinds of outputs that human users prefer.31 Constitutional AI is a related fine-tuning process,developed by the AI company Anthropic,that attempts to steer an LLMs behavior with minimal human

66、guidance.32 Unlike RLHF,Constitutional AI does not rely on human labels or annotations as a way to encode human preferences.Instead,researchers provide the system with a list of guiding rules or principleshence the term“constitutional”and essentially ask another model to evaluate and revise its outp

67、uts.33 While Constitutional AI is promising as an RLHF alternative that relies on far fewer human-generated labels,RLHF still seems to be the industry standard for guiding and steering LLMs at the fine-tuning stage.34 4.Prompt and Output Controls Even after pre-training and multiple rounds of fine-t

68、uning,an LLM may still output undesirable text.Before developers incorporate models into consumer-facing products,they can choose to control models using additional techniques at either the pre-output or the post-output stage.These techniques are also commonly referred to as“input filters”(applied a

69、t the pre-output stage)and“output filters”(applied at the post-output stage)and generally fall into three camps:detection,flagging,and redaction.Many existing tools and solutions originate from the need to moderate content on social media and are not necessarily specific to AI,but developers have in

70、creasingly adapted them for use on large language models.35 Before the LLM even receives a users input,developers can screen prompts to assess whether they are likely to evoke harmful text and show users a warning or refusal message in lieu of completion from the AI system.This can create a similar

71、effect to the model itself refusing to answer certain types of prompts.While both of these methods can be bypassed by jailbreaking,in which users deliberately circumvent models content restrictions,36 these methods can serve as a basic defense for non-malicious users.AI developers are also increasin

72、gly evaluating their LLMs using“red-teaming,”a systematic testing process that includes jailbreaking models in a controlled environment in order to see how their guardrails might fail.37 While red-teaming is used for a number of purposes,including acquiring information that can be used to improve co

73、ntrols,one major incentive for developers is to detect and fix potential Center for Security and Emerging Technology|10 jailbreaks before the model is released to the public.Another variation on prompt screening is prompt rewriting,in which a different language model rewrites user-submitted prompts

74、before they are passed to the target model.38 This may provide some protection against jailbreaking,as it effectively creates another set of guardrails between the user and the target model.At the post-output stage,once the LLM has composed a response to a prompt but before that output has been show

75、n to the user,developers can employ additional checks and filters.One option is to train a separate machine learning modeloften referred to as a“toxicity filter”39to detect harmful content,then use that model to catch outputs before they can be shown to users.Like supervised fine-tuning,these techni

76、ques rely on human-labeled data.While they have demonstrably positive effects on how toxic LLM outputs are,labeling the datasets of harmful content that are used to train the detection models is often actively harmful to workers mental health.40 Post-fine-tuning model controls are also often combine

77、d with monitoring or user reporting.Usually,this involves a combination of automated content detection or filtering,human content moderation,and user reporting.For example,OpenAI provides a moderation endpoint that developers can use to automatically flag or filter potentially harmful outputs,41 and

78、 was also initially relying on human content moderators to help evaluate outputs from ChatGPT.42 Finally,if a harmful or undesirable output makes it through all of the existing controls,many LLM interfaces contain a user feedback mechanism so that users can flag individual outputs directly.Developer

79、s are extremely unlikely to catch every prompt or use case that might lead to a harmful output,and thus rely on users to give feedback on model performance.Open vs.Private Models The safeguards listed in the previous section are mostly voluntary techniques that are applied by the companies that deve

80、lop and host top-tier LLMs.These large companies generally have both the incentives and the resources to attempt to secure their models and improve the safety of their outputs.For example,minimizing the chances that a language model will return toxic text may improve the user experience and boost pu

81、blic confidence in the model,making it easier to incorporate into a software product.However,private companies are not the only ones building and supplying these models.Smaller,but still powerful,open models are increasingly available for anyone to download and adapt.Some publicly accessible models

82、were originally trained by AI labs on carefully selected pre-training data and may have been red-teamed prior to release,but these released models can be fine-tuned by third-party users who may be Center for Security and Emerging Technology|11 less diligent or even outright malicious.Other open mode

83、ls may be trained by developers who may have fewer resources dedicated to safety,less interest in curating their fine-tuning data,or fewer incentives to monitor the prompts that are provided to their models or the outputs that are ultimately created.Recently,some powerful open models have also been

84、produced in foreign countries,such as the United Arab Emirates and China,where developers may have different views about what guardrails should exist.43 While many of these open models are created with safety and responsible use in mind,there is no way to guarantee that all of their downstream users

85、 adhere to the same standards and that the resulting applications will be error-free or sufficiently safeguarded.In fact,some research suggests that fine-tuning a model can undermine its developers safeguards even when users may not intend to do so.44 The AI development community is currently debati

86、ng whether private or open models are better for safety.For one,private models are not guaranteed to be easier to control in all circumstances.Even if they are secured and safeguarded,cutting-edge models are more likely to possess capabilities that require novel or more rigorous control techniques.O

87、ther variables,such as whether or not the user is interfacing directly with the model,may also affect how easy it is to control.Finally,while open models are difficult to control and monitor once they are adopted by downstream users,they also broaden access to researchers outside private companies w

88、ho may have fewer resources or need the flexibility to experiment freely with an LLM.Conclusion Controlling LLM outputs remains challenging.In practice,the methods above are almost always used in combination with each other,and undesirable outputs will continue to slip through despite developers bes

89、t efforts.Todays methods are more like sledgehammers than scalpels;any attempt to control a model in a specific way,such as preventing it from outputting violent content,is likely to have unintended consequences,like making it unable to describe the plot of an R-rated movie.45 There are also legitim

90、ate disagreements about whether or not a given output is harmful,and the definition of what constitutes harmful or toxic content might vary depending on the context in which any given model is deployed.Several other factors complicate the situation further.Firstly,this is a pacing problem.AI researc

91、hers are racing to develop and test these techniques while simultaneously keeping up with the breakneck pace of AI capabilities progress.The popularity of jailbreaks and other methods for bypassing content controls also means that Center for Security and Emerging Technology|12 developers are constan

92、tly discovering new ways their models can be manipulated.Finally,it is very difficult for those outside the leading AI labs to evaluate how effective these individual methods are because there is little information about their effectiveness for some of the most popular and powerful LLMs.While open m

93、odels can provide useful data in this vein,they may be smaller and less capable than state-of-the-art models.Public data on user behavior,such as API calls or what kinds of feedback users are giving models,is also scarce.Language models can carry inherent risks,including their propensity to output u

94、ndesirable text,including falsehoods,potentially dangerous information such as instructions for biological or nuclear weapons,or malware code.Nevertheless,the idea that developers can gain perfect control over an LLM by simply tweaking its inputs is misleading.LLMs can be complex,messy,and behave in

95、 unpredictable ways.As AI governance and regulation become increasingly important,however,understanding how they work and how they might be controlled will be more critical than ever.Center for Security and Emerging Technology|13 Authors Jessica Ji is a Research Analyst with the CyberAI Project at G

96、eorgetown Universitys Center for Security and Emerging Technology(CSET),where Josh Goldstein is a Research Fellow.Andrew Lohn is a Senior Fellow at CSET and the Director for Emerging Technology on the National Security Council Staff,Executive Office of the President under an Interdepartmental Person

97、nel Act agreement with CSET.Dr.Lohn completed this work before starting at the National Security Council.The views expressed are the authors own personal views and do not necessarily reflect the views of the White House or the Administration.During the preparation of this brief,Andrew Lohn also part

98、icipated in the red-teaming of Metas and OpenAIs large language models for which he was compensated.Acknowledgments For feedback and assistance,we would like to thank John Bansemer,Helen Toner,Karson Elmgren,Krystal Jackson,Rishi Bommasani,and Katherine Lee.We are also grateful to Lauren Lassiter,Sh

99、elton Fitch,Tessa Baker,and Jason Ly for editorial assistance.2023 by the Center for Security and Emerging Technology.This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.To view a copy of this license,visit https:/creativecommons.org/licenses/by-nc/4.0

100、/.Document Identifier:doi:10.51593/2023CA009 Center for Security and Emerging Technology|14 Endnotes 1 Tyna Eloundou,Sam Manning,Pamela Mishkin,and Daniel Rock,“GPTs are GPTs:An Early Look at the Labor Market Impact Potential of Large Language Models,”arXiv preprint arXiv:2303.10130v5(2023),https:/a

101、rxiv.org/abs/2303.10130.2 Helen Toner,“AI Chatbots Are Doing Something a Lot Like Improv,”TIME,May 18,2023,https:/ Some researchers are exploring different ways to ground language model outputs in factual sources.For more,see Jacob Menick,Maja Trebacz,Vladimir Mikulik,John Aslanides,Francis Song,Mar

102、tin Chadwick et al.,“Teaching language models to support answers with verified quotes,”arXiv preprint arXiv:2203.11147(2022),https:/arxiv.org/abs/2203.11147.4 For a broader taxonomy of risks from LLMs,see:Laura Weidinger,Jonathan Uesato,Maribeth Rauh,Conor Griffin,Po-Sen Huang,John Mellor,Amelia Gla

103、ese,Myra Cheng,et al.,“Taxonomy of Risks posed by Language Models,”FAccT 22:Proceedings of the 2022 ACM Conference on Fairness,Accountability,and Transparency,June 2022,https:/dl.acm.org/doi/10.1145/3531146.3533088.5 Benjamin Weiser,“Heres What Happens When Your Lawyer Uses ChatGPT,”The New York Tim

104、es,May 27,2023,https:/ OpenAI,“GPT-4 Technical Report,”arXiv preprint arXiv:2303.08774(2023),https:/arxiv.org/abs/2303.08774.7 Jochen Hartmann,Jasper Schwenzow,and Maximilian Witte,“The political ideology of conversational AI:Converging evidence on ChatGPTs pro-environmental,left-libertarian orienta

105、tion,”arXiv preprint arXiv:2301.01768(2023),https:/arxiv.org/abs/2301.01768;Abubakar Abid,Maheen Farooqi,and James Zou,“Persistent Anti-Muslim Bias in Large Language Models,”AIES 21:Proceedings of the 2021 AAAI/ACM Conference on AI,Ethics,and Society,July 30,2021,https:/dl.acm.org/doi/pdf/10.1145/34

106、61702.3462624;Umang Gupta,Jwala Dhamala,Varun Kumar,Apurv Verma,Yada Pruksachatkun,Satyapriya Krishna,Rahul Gupta,Kai-Wei Chang,Greg Ver Steeg,and Aram Galstyan,“Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal,”arXiv preprint arXiv:2203.12574(2022),https:/arxiv.o

107、rg/abs/2203.12574;for a more thorough discussion of language models and bias,see Emilio Ferrara,“Should ChatGPT be Biased?Challenges and Risks of Bias in Large Language Models,”arXiv preprint arXiv:2304.03738(2023),https:/arxiv.org/abs/2304.03738.8 Jesse Dodge,Maarten Sap,Ana Marasovi,William Agnew,

108、Gabriel Ilharco,Dirk Groeneveld,Margaret Mitchell,and Matt Gardner,“Documenting Large Webtext Corpora:A Case Study on the Colossal Clean Crawled Corpus,”Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processes,November 7-11,2021,https:/aclanthology.org/2021.emnlp-main.98

109、.pdf.Center for Security and Emerging Technology|15 9 Laura Weidinger,John Mellor,Maribeth Rauh,Conor Griffin,Jonathan Uesato,Po-Sen Huang et al.,“Ethical and social risks of harm from Language Models,”arXiv preprint arXiv:2112.04359(2021),https:/arxiv.org/abs/2112.04359;Miles Brundage,Shahar Avin,J

110、ack Clark,Helen Toner,Peter Eckersley,Ben Garfinkel et al.,“The Malicious Use of Artificial Intelligence:Forecasting,Prevention,and Mitigation,”arXiv preprint arXiv:1802.07228(2018),https:/arxiv.org/abs/1802.07228.10 OpenAI,“GPT-4 Technical Report.”11 Julian Hazell,“Large Language Models Can Be Used

111、 To Effectively Scale Spear Phishing Campaigns,”arXiv preprint arXiv:2305.06972(2023),https:/arxiv.org/abs/2305.06972;Andrew J.Lohn and Krystal A.Jackson,“Will AI Make Cyber Swords or Shields?,”Center for Security and Emerging Technology,August 2022,https:/cset.georgetown.edu/publication/will-ai-mak

112、e-cyber-swords-or-shields/;Josh A.Goldstein,Jason Chao,Shelby Grossman,Alex Stamos,and Michael Tomz,“Can AI Write Persuasive Propaganda?,”SocArXiv preprint,April 8,2023,https:/doi.org/10.31235/osf.io/fp87b.12 Jesse Dodge,Maarten Sap,Ana Marasovi,William Agnew,Gabriel Ilharco,Dirk Groeneveld,Margaret

113、 Mitchell,and Matt Gardner,“Documenting Large Webtext Corpora:A Case Study on the Colossal Clean Crawled Corpus,”Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing,Association for Computational Linguistics,November 2021,https:/aclanthology.org/2021.emnlp-main.98/.

114、13 Will Knight,“OpenAIs CEO Says the Age of Giant AI Models Is Already Over,”Wired,April 17,2023,https:/ Leo Gao,Stella Biderman,Sid Black,Laurence Golding,Travis Hoppe,Charles Foster,Jason Phang,Horace He et al.,“The Pile:An 800GB Dataset of Diverse Text for Language Modeling,”arXiv preprint arXiv:

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116、 Frost,“Mitigating harm in language models with conditional-likelihood filtration,”arXiv preprint arXiv:2108.07790(2021),https:/arxiv.org/abs/2108.07790.17 Sachin Kumar,Vidhisha Balachandran,Lucille Njoo,Antonios Anastasopoulos,and Yulia Tsvetkov,“Language Generation Models Can Cause Harm:So What Ca

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