6 Conclusion

This interim International Scientific Report on the Safety of Advanced AI finds that the future
trajectory of general-purpose Al is remarkably uncertain. A wide range of possible outcomes appears
possible even in the near future, including both very positive and very negative outcomes, as well as
anything in between. Among the most promising prospects for general-purpose Al are its potential for
education, medical applications, research advances in a wide range of fields, and increased
productivity leading to more prosperity. If managed properly, general-purpose Al systems could
substantially improve the lives of people worldwide.

But to reap the benefits of this transformative technology safely, researchers and policymakers need to identify and take informed action to mitigate the risks that come with it. Malicious use of general-purpose Al as well as malfunctioning general-purpose Al are already causing harm today, for instance through deepfakes, scams and biased outputs. Depending on the rate of progress of future general-purpose Al capabilities, the technical methods that developers and regulators employ to mitigate risks, the decisions of governments and societies in relation to general-purpose Al, and the degree of successful global coordination, it is also possible that further risks could emerge. The worst outcomes could see the emergence of risks like large-scale unemployment, general-purpose Al-enabled terrorism, or even humanity losing control over general-purpose Al systems. There is no consensus among experts about how likely these risks are and when they might occur.

This report also examines the factors that make addressing these risks difficult. Despite rapid
advances in capabilities, researchers currently cannot generate human-understandable accounts of
how general-purpose Al models and systems arrive at outputs and decisions. This makes it difficult to
evaluate or predict what they are capable of, how reliable they are, and obtain assurances on the risks
they might pose.

There are technical approaches to addressing the risks from general-purpose Al: methods for
reducing model bias, improving our understanding of the inner workings of general-purpose Al models,
assessing their capabilities and potential risks, and making them less likely to respond to user requests
that could cause harm. There are also complementary techniques for monitoring and mitigating
harmful actions from general-purpose Al systems. However, no existing techniques currently provide
quantitative guarantees about the safety of advanced general-purpose Al models or systems.

Nothing about the future of Al is inevitable. How general-purpose Al gets developed and by whom,
which problems it gets designed to solve, whether we will be able to reap general-purpose Al's full
economic potential, who benefits from it, and the types of risks we expose ourselves to — these and
many other questions depend on the choices that societies and governments make today and in the
future to shape the development of general-purpose Al. Since the impact of general-purpose Al on
many aspects of our lives is likely to be profound, and since progress might continue to be rapid, the
precautionary principle implies an urgent need to broker consensus and to put resources into
understanding and addressing these risks. Constructive scientific and public discussion will be
essential for societies and policymakers to make the right choices.

For the first time in history, this interim report brought together expert representatives nominated by
30 countries, and the EU and the UN, and several other world-leading experts to provide a shared
scientific, evidence-based foundation for these vital discussions. We continue to disagree on several
questions, minor and major, around the capabilities, risks, and risk mitigations for general-purpose Al.
But we consider this project essential for improving our collective understanding of general-purpose
Al and its potential risks, and for moving closer towards consensus and effective risk mitigation to
ensure people can enjoy general-purpose Al's benefits safely. The stakes are high. We look forward to
continuing this effort.
6 Conclusion

This interim International Scientific Report on the Safety of Advanced AI finds that the future
trajectory of general-purpose Al is remarkably uncertain. A wide range of possible outcomes appears
possible even in the near future, including both very positive and very negative outcomes, as well as
anything in between. Among the most promising prospects for general-purpose Al are its potential for
education, medical applications, research advances in a wide range of fields, and increased
productivity leading to more prosperity. If managed properly, general-purpose Al systems could
substantially improve the lives of people worldwide.

But to reap the benefits of this transformative technology safely, researchers and policymakers need to identify and take informed action to mitigate the risks that come with it. Malicious use of general-purpose Al as well as malfunctioning general-purpose Al are already causing harm today, for instance through deepfakes, scams and biased outputs. Depending on the rate of progress of future general-purpose Al capabilities, the technical methods that developers and regulators employ to mitigate risks, the decisions of governments and societies in relation to general-purpose Al, and the degree of successful global coordination, it is also possible that further risks could emerge. The worst outcomes could see the emergence of risks like large-scale unemployment, general-purpose Al-enabled terrorism, or even humanity losing control over general-purpose Al systems. There is no consensus among experts about how likely these risks are and when they might occur.

This report also examines the factors that make addressing these risks difficult. Despite rapid
advances in capabilities, researchers currently cannot generate human-understandable accounts of
how general-purpose Al models and systems arrive at outputs and decisions. This makes it difficult to
evaluate or predict what they are capable of, how reliable they are, and obtain assurances on the risks
they might pose.

There are technical approaches to addressing the risks from general-purpose Al: methods for
reducing model bias, improving our understanding of the inner workings of general-purpose Al models,
assessing their capabilities and potential risks, and making them less likely to respond to user requests
that could cause harm. There are also complementary techniques for monitoring and mitigating
harmful actions from general-purpose Al systems. However, no existing techniques currently provide
quantitative guarantees about the safety of advanced general-purpose Al models or systems.

Nothing about the future of Al is inevitable. How general-purpose Al gets developed and by whom,
which problems it gets designed to solve, whether we will be able to reap general-purpose Al's full
economic potential, who benefits from it, and the types of risks we expose ourselves to — these and
many other questions depend on the choices that societies and governments make today and in the
future to shape the development of general-purpose Al. Since the impact of general-purpose Al on
many aspects of our lives is likely to be profound, and since progress might continue to be rapid, the
precautionary principle implies an urgent need to broker consensus and to put resources into
understanding and addressing these risks. Constructive scientific and public discussion will be
essential for societies and policymakers to make the right choices.

For the first time in history, this interim report brought together expert representatives nominated by
30 countries, and the EU and the UN, and several other world-leading experts to provide a shared
scientific, evidence-based foundation for these vital discussions. We continue to disagree on several
questions, minor and major, around the capabilities, risks, and risk mitigations for general-purpose Al.
But we consider this project essential for improving our collective understanding of general-purpose
Al and its potential risks, and for moving closer towards consensus and effective risk mitigation to
ensure people can enjoy general-purpose Al's benefits safely. The stakes are high. We look forward to
continuing this effort.
Chair's note on the interim report

This interim report is the product of extremely rapid collaboration between a diverse and large group of AI experts, including an Expert Advisory Panel nominated by 30 countries as well as the EU and the UN. The field of AI research is moving at pace, and on many important questions the field is far from having a consensus view. Against this backdrop, I am particularly impressed by what the 75 international experts contributing their diverse perspectives to this report have achieved in such a short time. Writing a report that discusses the capabilities, risks and potential risks of general purpose Al in a balanced way has been especially important to me. I am very grateful to the experts contributing to the report for the collaborative spirit with which they approached this important project.

The short amount of time available for writing this interim report also means that several difficult procedural decisions and decisions about the scope of the report had to be made, leaving many important issues unaddressed or only covered briefly. My goal for the next publication, which will build on this interim report, is for the contributing experts to jointly identify the most important areas of improvement to work on for the next report.

This might, for example, include some of the following aspects:

* We aim to further improve how the report evaluates and synthesises the scientific evidence on capabilities and risks. I am satisfied with how we have done this for the interim report given the short time available, but for the next report improvements can and should be expected. In particular, I expect the next report to:

* Take into account an even larger body of scientific work to provide an even more comprehensive discussion of the literature;

* In some cases, be more explicit about how compelling particular pieces of research are based on their methodology;

* Do an even better job of synthesising the evidence in order to provide more nuanced and concise assessments of the state of the science on particular questions.

* For the interim publication, we restricted input to the report to the writing group, senior advisors, and expert input we needed on particular sections, including from a very limited set of civil society organisations. We intentionally excluded input from private sector representatives to avoid conflicts of interest. However, companies have been the main driver of recent advances in AI capabilities and are also active contributors to research, which we cite, on risk assessment and mitigation. Similarly, there is an active and diverse ecosystem of civil society organisations whose work has been crucial for the field's understanding of AI risks and risk mitigation. Therefore, now that the interim report has been published, we would like to broaden the input to the report. We are calling for submissions of evidence from companies and civil society to inform the development of the next, full report. We will set out more details on how to do this in due course. In the meantime, do email secretariat.AlStateofScience@dsit.gov.uk if you have evidence to submit.

* Ensuring that the report is comprehensive in its discussion of the risks of advanced AI has been a top priority for the writing team and senior advisors, but it has also been clear to us from the beginning that the short time available for producing the interim report would not be enough to address all issues with the depth they deserve. An example of a topic that I would like the next report to discuss in more detail is the global 'AI Divide' that prevents people around the world enjoying any benefits of AI equally. Another topic that the next report will discuss in greater detail than in the present report is the impact of AI development and deployment on the environment. In aiming to make the report even more comprehensive, we will remain conscious of the potential trade-offs between breadth, depth, and the coverage of evidence, and will aim for a balance that we consider most useful to policymakers.
# Chair's note on the interim report

This interim report is the product of extremely rapid collaboration between a diverse and large group of AI experts, including an Expert Advisory Panel nominated by 30 countries as well as the EU and the UN. The field of AI research is moving at pace, and on many important questions the field is far from having a consensus view. Against this backdrop, I am particularly impressed by what the 75 international experts contributing their diverse perspectives to this report have achieved in such a short time. Writing a report that discusses the capabilities, risks and potential risks of general purpose AI in a balanced way has been especially important to me. I am very grateful to the experts contributing to the report for the collaborative spirit with which they approached this important project.

The short amount of time available for writing this interim report also means that several difficult procedural decisions and decisions about the scope of the report had to be made, leaving many important issues unaddressed or only covered briefly. My goal for the next publication, which will build on this interim report, is for the contributing experts to jointly identify the most important areas of improvement to work on for the next report.

This might, for example, include some of the following aspects:

* We aim to further improve how the report evaluates and synthesises the scientific evidence on capabilities and risks. I am satisfied with how we have done this for the interim report given the short time available, but for the next report improvements can and should be expected. In particular, I expect the next report to:

* Take into account an even larger body of scientific work to provide an even more comprehensive discussion of the literature;

* In some cases, be more explicit about how compelling particular pieces of research are based on their methodology;

* Do an even better job of synthesising the evidence in order to provide more nuanced and concise assessments of the state of the science on particular questions.

* For the interim publication, we restricted input to the report to the writing group, senior advisors, and expert input we needed on particular sections, including from a very limited set of civil society organisations. We intentionally excluded input from private sector representatives to avoid conflicts of interest. However, companies have been the main driver of recent advances in AI capabilities and are also active contributors to research, which we cite, on risk assessment and mitigation. Similarly, there is an active and diverse ecosystem of civil society organisations whose work has been crucial for the field's understanding of AI risks and risk mitigation. Therefore, now that the interim report has been published, we would like to broaden the input to the report. We are calling for submissions of evidence from companies and civil society to inform the development of the next, full report. We will set out more details on how to do this in due course. In the meantime, do email secretariat.AIStateofScience@dsit.gov.uk if you have evidence to submit.

* Ensuring that the report is comprehensive in its discussion of the risks of advanced AI has been a top priority for the writing team and senior advisors, but it has also been clear to us from the beginning that the short time available for producing the interim report would not be enough to address all issues with the depth they deserve. An example of a topic that I would like the next report to discuss in more detail is the global 'AI Divide' that prevents people around the world enjoying any benefits of AI equally. Another topic that the next report will discuss in greater detail than in the present report is the impact of AI development and deployment on the environment. In aiming to make the report even more comprehensive, we will remain conscious of the potential trade-offs between breadth, depth, and the coverage of evidence, and will aim for a balance that we consider most useful to policymakers.
* We have restricted the scope of this interim report to general-purpose Al. I am confident that this was the right decision, but it is also clear that the variety of types of Al models and systems pose a challenge for defining any subgroup of Al cleanly. For the next report, whatever its scope, we will aim to delineate in even more detail what types of Al are in and out of scope of the report.

I am extremely grateful to all the experts who contributed to this interim report and look forward to working on the next publication.
* We have restricted the scope of this interim report to general-purpose Al. I am confident that this was the right decision, but it is also clear that the variety of types of Al models and systems pose a challenge for defining any subgroup of Al cleanly. For the next report, whatever its scope, we will aim to delineate in even more detail what types of Al are in and out of scope of the report.

I am extremely grateful to all the experts who contributed to this interim report and look forward to working on the next publication.
# Differing views

The Chair and Secretariat worked to reach consensus between members of the Expert Advisory Panel on the content of reports. As per the report's principles & procedures, a near-final version of the interim report was shared with the Expert Advisory Panel. Where differing views remained with the Panel, Panel members were offered the option for these to be noted. Below are the views noted.

## 1. Ciarán Seoighe (Ireland)

Noted concern that the general tone of the report is excessively negative. Noted that while the report does note that the future of AI is not predetermined, the language used could create the impression that the outlook for humanity is bleak no matter what steps are taken, and that consequently, the report's impact on policymakers could be undermined.

## 2. Ciarán Seoighe (Ireland)

Requested that future iterations of the report should include:

* Assessment of the societal impacts of generative AI in domains such as the information ecosystem, education, and democratic processes from both a technical and socio-technical perspective. It is important also to incorporate the range of regulatory approaches that could mitigate societal risk along with technical solutions.

* The importance of human/fundamental rights assessments and their role in mitigating risks before AI systems are developed and deployed. Noted this to be a significant omission in the current version of the report.
# Differing views

The Chair and Secretariat worked to reach consensus between members of the Expert Advisory Panel on the content of reports. As per the report's [principles & procedures](#), a near-final version of the interim report was shared with the Expert Advisory Panel. Where differing views remained with the Panel, Panel members were offered the option for these to be noted. Below are the views noted.

## 1. Ciarán Seoighe (Ireland)

Noted concern that the general tone of the report is excessively negative. Noted that while the report does note that the future of AI is not predetermined, the language used could create the impression that the outlook for humanity is bleak no matter what steps are taken, and that consequently, the report's impact on policymakers could be undermined.

## 2. Ciarán Seoighe (Ireland)

Requested that future iterations of the report should include:

* Assessment of the societal impacts of generative AI in domains such as the information ecosystem, education, and democratic processes from both a technical and socio-technical perspective. It is important also to incorporate the range of regulatory approaches that could mitigate societal risk along with technical solutions.

* The importance of human/fundamental rights assessments and their role in mitigating risks before AI systems are developed and deployed. Noted this to be a significant omission in the current version of the report.
# Glossary

The explanations below should all be taken for the use of the term in the context of AI or general-purpose AI.

**Adaptivity:** The ability to identify patterns, reason, and make decisions in contexts and ways not directly envisioned by human programmers or outside the context of a system's training data.

**AI agents/agent/autonomous agent:** AI systems that are capable of accomplishing multi-step tasks in pursuit of a high-level goal with little or no human oversight. AI agents may do things like browsing the internet, sending emails, or sending instructions to physical equipment.

**AI deployers:** Any individual or organisation that supplies or uses an AI system to provide a product or service. Deployment can be 'internal', where a system is only used by the developers, or 'external', allowing the public or other non-developer entities to use it.

**AI developers:** Organisations or individuals who design, build, train, adapt, or combine AI models and applications.

**AI end user:** Any intended or actual individual or organisation that uses or consumes an AI-based product or service as it is deployed.

**AI lifecycle:** All events and processes that relate to an AI system's lifespan, from inception to decommissioning, including its design, research, training, development, deployment, integration, operation, maintenance, sale, use, and governance.

**AI risks:** The combination of the probability of an occurrence of harm arising from the development or deployment of AI models or systems, and the severity of that harm.

**Algorithmic Transparency:** The degree to which the factors informing general-purpose AI output, e.g. recommendations or decisions, are knowable by various stakeholders. Such factors might include the inner workings of the AI model, how it has been trained, what data it is trained on, what features of the input affected its output, and what decisions it would have made under different circumstances.

**Alignment:** The process of ensuring an AI system's goals and behaviours are in line with its developer's values and intentions.

**Application Programming Interface (API):** A set of rules and protocols that enables integration and communication between AI systems and other software applications.

**Artificial General Intelligence (AGI):** A potential future AI system that equals or surpasses human performance on all or almost all cognitive tasks. A number of AI companies have publicly stated their aim to build AGI. However, the term AGI has no universally precisely agreed definition.

**Autonomy/autonomous:** Capable of operating, taking actions, or making decisions without the express intent or oversight of a human.

**Biological design tools (BDTs):** In this report, biological design tools (BDTs) refers to AI systems trained on biological data that can help design new proteins or other biological agents such as enzymes.
# Glossary

The explanations below should all be taken for the use of the term in the context of AI or general-purpose AI.

**Adaptivity:** The ability to identify patterns, reason, and make decisions in contexts and ways not directly envisioned by human programmers or outside the context of a system's training data.

**AI agents/agent/autonomous agent:** AI systems that are capable of accomplishing multi-step tasks in pursuit of a high-level goal with little or no human oversight. AI agents may do things like browsing the internet, sending emails, or sending instructions to physical equipment.

**AI deployers:** Any individual or organisation that supplies or uses an AI system to provide a product or service. Deployment can be 'internal', where a system is only used by the developers, or 'external', allowing the public or other non-developer entities to use it.

**AI developers:** Organisations or individuals who design, build, train, adapt, or combine AI models and applications.

**AI end user:** Any intended or actual individual or organisation that uses or consumes an AI-based product or service as it is deployed.

**AI lifecycle:** All events and processes that relate to an AI system's lifespan, from inception to decommissioning, including its design, research, training, development, deployment, integration, operation, maintenance, sale, use, and governance.

**AI risks:** The combination of the probability of an occurrence of harm arising from the development or deployment of AI models or systems, and the severity of that harm.

**Algorithmic Transparency:** The degree to which the factors informing general-purpose AI output, e.g. recommendations or decisions, are knowable by various stakeholders. Such factors might include the inner workings of the AI model, how it has been trained, what data it is trained on, what features of the input affected its output, and what decisions it would have made under different circumstances.

**Alignment:** The process of ensuring an AI system's goals and behaviours are in line with its developer's values and intentions.

**Application Programming Interface (API):** A set of rules and protocols that enables integration and communication between AI systems and other software applications.

**Artificial General Intelligence (AGI):** A potential future AI system that equals or surpasses human performance on all or almost all cognitive tasks. A number of AI companies have publicly stated their aim to build AGI. However, the term AGI has no universally precisely agreed definition.

**Autonomy/autonomous:** Capable of operating, taking actions, or making decisions without the express intent or oversight of a human.

**Biological design tools (BDTs):** In this report, biological design tools (BDTs) refers to AI systems trained on biological data that can help design new proteins or other biological agents such as enzymes.
**Black-box:** A system deployed with restrictions such that a user cannot access or analyse its inner workings. See also "White-box" below.

**Capabilities:** The range of tasks or functions that an AI system can perform and the proficiency with which it can perform them.

**Cloud labs:** Remotely controlled automatised biochemical laboratories.

**Cognitive tasks:** Tasks involving a combination of information processing, memory, information recall, planning, reasoning, organisation, problem solving, learning, and goal-oriented decision-making.

**Compute:** Computational resources, required in large amounts to train and run general-purpose AI models. Mostly provided through clusters of Graphics Processing Units (GPUs).

**Cross-lingual differences:** Discrepancies in how a general-purpose AI model or system might respond to the same input in different languages.

**Deep Learning:** A set of methods for AI development that leverages very large amounts of data and compute.

**Deployment:** The process of releasing an AI system into a real-world environment, such as a consumer-facing AI system.

**Disinformation:** Deliberately false information generated or spread with the intent to deceive or mislead.

**Ecosystem Audit:** A broad evaluation of an AI system and its surrounding ecosystem. Ecosystem audits might consider AI models, their training data, the circumstances of their deployment, and surrounding operational practice.

**Evaluations:** Systematic assessments of an AI system's performance, capabilities, or potential impacts. Evaluations can include benchmarking, red-teaming, and audits.

**FLOPS:** 'Floating point operations per second' – a measure of the computing power of a computer.

**Foundation models:** Machine learning models trained on very large amounts of data that can be adapted to a wide range of tasks.

**Frontier AI:** For the AI Safety Summit at Bletchley Park, frontier AIs were defined as models that can perform a wide variety of tasks and match or exceed the capabilities present in today's most advanced models.

**GPU (Graphics Processing Unit):** A piece of computer hardware assembled from semiconductors widely used as the central source of computational power for general-purpose AI. GPUs were originally designed for graphics rendering applications.

**Guardrails:** Pre-defined safety constraints or boundaries set up in an attempt to ensure an AI system operates within desired parameters and avoids unintended or harmful outcomes.

**Heuristic:** A rule-of-thumb, strategy, or a simplified principle that, in the context of computer science, has been developed to solve problems more efficiently when classic methods are too slow or fail to find an exact solution.
**Black-box:** A system deployed with restrictions such that a user cannot access or analyse its inner workings. See also "White-box" below.

**Capabilities:** The range of tasks or functions that an AI system can perform and the proficiency with which it can perform them.

**Cloud labs:** Remotely controlled automatised biochemical laboratories.

**Cognitive tasks:** Tasks involving a combination of information processing, memory, information recall, planning, reasoning, organisation, problem solving, learning, and goal-oriented decision-making.

**Compute:** Computational resources, required in large amounts to train and run general-purpose AI models. Mostly provided through clusters of Graphics Processing Units (GPUs).

**Cross-lingual differences:** Discrepancies in how a general-purpose AI model or system might respond to the same input in different languages.

**Deep Learning:** A set of methods for AI development that leverages very large amounts of data and compute.

**Deployment:** The process of releasing an AI system into a real-world environment, such as a consumer-facing AI system.

**Disinformation:** Deliberately false information generated or spread with the intent to deceive or mislead.

**Ecosystem Audit:** A broad evaluation of an AI system and its surrounding ecosystem. Ecosystem audits might consider AI models, their training data, the circumstances of their deployment, and surrounding operational practice.

**Evaluations:** Systematic assessments of an AI system's performance, capabilities, or potential impacts. Evaluations can include benchmarking, red-teaming, and audits.

**FLOPS:** 'Floating point operations per second' – a measure of the computing power of a computer.

**Foundation models:** Machine learning models trained on very large amounts of data that can be adapted to a wide range of tasks.

**Frontier AI:** For the AI Safety Summit at Bletchley Park, frontier AIs were defined as models that can perform a wide variety of tasks and match or exceed the capabilities present in today's most advanced models.

**GPU (Graphics Processing Unit):** A piece of computer hardware assembled from semiconductors widely used as the central source of computational power for general-purpose AI. GPUs were originally designed for graphics rendering applications.

**Guardrails:** Pre-defined safety constraints or boundaries set up in an attempt to ensure an AI system operates within desired parameters and avoids unintended or harmful outcomes.

**Heuristic:** A rule-of-thumb, strategy, or a simplified principle that, in the context of computer science, has been developed to solve problems more efficiently when classic methods are too slow or fail to find an exact solution.
**Input (to an AI system):** The data or prompt fed into an AI system, often text or an image, which the AI system processes before producing an output.

**Large Language Model (LLMs):** Machine learning models trained on large datasets that can recognise, understand, and generate text and other content.

**Massive Multitask Language Understanding (MMLU):** A widely used benchmark AI research that assesses a general-purpose AI model's performance across a broad range of tasks and subject areas.

**Misgeneralisation:** When an AI system trained to perform well in one context fails to perform well in a new context. For instance, if an AI trained mostly on pictures of white cats labels a black cat as a 'dog', it is misgeneralising from its training data.

**Misinformation:** Incorrect or misleading information, potentially generated and spread without harmful intent.

**Modalities:** The types and nature of data that an AI model can process, such as text, image, sound, or videos. Models might be unimodal, i.e. only able to process one type of data, or multimodal, i.e. able to process multiple types of data.

**Model Card:** A document providing important information on a general-purpose AI model, such as its purpose, its performance on evaluations and benchmarks, and safety features.

**Narrow AI:** An AI system that only performs well on a single task or narrow set of tasks, like sentiment analysis or playing Chess.

**Open-ended domains:** Scenarios or environments that have a very large set of possible states and inputs to an AI system – so that developers cannot anticipate all types of contexts of use and thus cannot test the AI's behaviour in all possible situations.

**Pre-training:** The first stage of developing a modern general-purpose AI model, in which models learn from large amounts of data. Pre-training is the part of general-purpose AI training that requires the most data and computational resources.

**Prompt:** An input to an AI system, often a text-based question or query, that the system processes before it produces a response.

**Red-teaming:** A method to evaluate the safety and robustness of systems by attempting to design inputs that make them fail. This is often done by developing 'adversarial attacks' or challenging conditions. Red-teaming tries to reveal worst-case behaviours or malicious use opportunities.

**Risk factors:** Elements or conditions that can increase downstream risks. For example, weak guardrails constitute a risk factor that could enable an actor to malicious use an AI system to perform a cyber attack (downstream risk).

**Safety and security:** The protection, wellbeing, and autonomy of civil society and the population. In this publication, safety is often used to describe prevention of or protection against AI-related harms. AI security refers to protecting AI systems from technical interference such as cyber-attacks or leaks of the code and weights of the AI model.

**Scaffold:** Additional software that aids in processing inputs and outputs of an AI model while leaving the model itself unchanged. For example, a scaffold allows GPT-4 to power the autonomous AI agent AutoGPT. The scaffold prompts GPT-4 to break down a high-level task into sub-tasks, assign sub-tasks to other copies of itself, save important information to memory, and browse the internet.
**Input (to an AI system):** The data or prompt fed into an AI system, often text or an image, which the AI system processes before producing an output.

**Large Language Model (LLMs):** Machine learning models trained on large datasets that can recognise, understand, and generate text and other content.

**Massive Multitask Language Understanding (MMLU):** A widely used benchmark AI research that assesses a general-purpose AI model's performance across a broad range of tasks and subject areas.

**Misgeneralisation:** When an AI system trained to perform well in one context fails to perform well in a new context. For instance, if an AI trained mostly on pictures of white cats labels a black cat as a 'dog', it is misgeneralising from its training data.

**Misinformation:** Incorrect or misleading information, potentially generated and spread without harmful intent.

**Modalities:** The types and nature of data that an AI model can process, such as text, image, sound, or videos. Models might be unimodal, i.e. only able to process one type of data, or multimodal, i.e. able to process multiple types of data.

**Model Card:** A document providing important information on a general-purpose AI model, such as its purpose, its performance on evaluations and benchmarks, and safety features.

**Narrow AI:** An AI system that only performs well on a single task or narrow set of tasks, like sentiment analysis or playing Chess.

**Open-ended domains:** Scenarios or environments that have a very large set of possible states and inputs to an AI system – so that developers cannot anticipate all types of contexts of use and thus cannot test the AI's behaviour in all possible situations.

**Pre-training:** The first stage of developing a modern general-purpose AI model, in which models learn from large amounts of data. Pre-training is the part of general-purpose AI training that requires the most data and computational resources.

**Prompt:** An input to an AI system, often a text-based question or query, that the system processes before it produces a response.

**Red-teaming:** A method to evaluate the safety and robustness of systems by attempting to design inputs that make them fail. This is often done by developing 'adversarial attacks' or challenging conditions. Red-teaming tries to reveal worst-case behaviours or malicious use opportunities.

**Risk factors:** Elements or conditions that can increase downstream risks. For example, weak guardrails constitute a risk factor that could enable an actor to malicious use an AI system to perform a cyber attack (downstream risk).

**Safety and security:** The protection, wellbeing, and autonomy of civil society and the population. In this publication, safety is often used to describe prevention of or protection against AI-related harms. AI security refers to protecting AI systems from technical interference such as cyber-attacks or leaks of the code and weights of the AI model.

**Scaffold:** Additional software that aids in processing inputs and outputs of an AI model while leaving the model itself unchanged. For example, a scaffold allows GPT-4 to power the autonomous AI agent AutoGPT. The scaffold prompts GPT-4 to break down a high-level task into sub-tasks, assign sub-tasks to other copies of itself, save important information to memory, and browse the internet.
**Semiconductors:** Fundamental material components of modern computer hardware, such as GPUs.

**Synthetic data:** Data, e.g., text, images, etc., that has been generated artificially, for instance by general-purpose AI models. Synthetic data might be used for training general-purpose AI models, such as in cases of scarcity of high-quality natural data.

**System integration:** The process of combining different software elements into one cohesive system assembled to perform some function. For instance, system integration might combine a general-purpose AI model, a content filter, a user interface, and various other components into a chatbot application.

**Transfer learning:** A machine learning technique in which a model's completed training on one task or subject area is used as a starting point for training or using the model on another subject area.

**Transformer architecture:** A deep-learning architecture at the heart of most modern general-purpose AI models. The transformer architecture has proven particularly efficient at converting increasingly large amounts of training data and computational power into better model performance.

**Weights:** Parameters in a model that are akin to adjustable dials in the algorithm. Training a model means adjusting its parameters to help it make accurate predictions or decisions based on input data, ensuring it learns from patterns it has seen.

**White-box:** A system deployed without restrictions such that a user can access or analyse its inner workings. See also "Black-box" above.
**Semiconductors:** Fundamental material components of modern computer hardware, such as GPUs.

**Synthetic data:** Data, e.g., text, images, etc., that has been generated artificially, for instance by general-purpose AI models. Synthetic data might be used for training general-purpose AI models, such as in cases of scarcity of high-quality natural data.

**System integration:** The process of combining different software elements into one cohesive system assembled to perform some function. For instance, system integration might combine a general-purpose AI model, a content filter, a user interface, and various other components into a chatbot application.

**Transfer learning:** A machine learning technique in which a model's completed training on one task or subject area is used as a starting point for training or using the model on another subject area.

**Transformer architecture:** A deep-learning architecture at the heart of most modern general-purpose AI models. The transformer architecture has proven particularly efficient at converting increasingly large amounts of training data and computational power into better model performance.

**Weights:** Parameters in a model that are akin to adjustable dials in the algorithm. Training a model means adjusting its parameters to help it make accurate predictions or decisions based on input data, ensuring it learns from patterns it has seen.

**White-box:** A system deployed without restrictions such that a user can access or analyse its inner workings. See also "Black-box" above.
References

* denotes that the reference was either published by an AI company or at least 50% of the authors of a preprint article have an AI company as their affiliation.

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2.* OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, R. Avila, I. Babuschkin, S. Balaji, V. Balcom, P. Baltescu, H. Bao, M. Bavarian, J. Belgum, ... B. Zoph, "GPT-4 Technical Report" (OpenAI, 2024); http://arxiv.org/abs/2303.08774.

3.* Gemini Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, D. Silver, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. Lillicrap, A. Lazaridou, ... O. Vinyals, "Gemini: A Family of Highly Capable Multimodal Models" (Google DeepMind, 2023); http://arxiv.org/abs/2312.11805.

4.* Anthropic, "The Claude 3 Model Family: Opus, Sonnet, Haiku" (Anthropic, 2024); https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf.

5.* Wen Team, J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang, B. Hui, L. Ji, M. Li, J. Lin, R. Lin, D. Liu, G. Liu, C. Lu, ... T. Zhu, Wen Technical Report, arXiv:2309.16609 [cs.CL] (2023). http://arxiv.org/abs/2309.16609.

6.* Meta, Build the future of AI with Meta Llama 3 (2024). https://llama.meta.com/llama3/.

7.* A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. Le Scao, T. Lavril, T. Wang, T. Lacroix, W. El Sayed, "Mistral 7B" (Mistral AI, 2023); https://doi.org/10.48550/arXiv.2310.06825.

8. L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, M.-H. Yang, Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Comput. Surv. 56, 1-39 (2023). https://doi.org/10.1145/3626235.

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12.* T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, A. Ramesh, "Video generation models as world simulators" (OpenAI, 2024); https://openai.com/research/video-generation-models-as-world-simulators.

13. D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, ... P. Florence, "PaLM-E: an embodied multimodal language model" in *Proceedings of the 40th International Conference on Machine Learning* (ICML'23) (PMLR, 2023) vol. 202, pp. 8469-8488. https://dl.acm.org/doi/10.5555/3618408.3618748.

14. J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick, S. W. Bodenstein, D. A. Evans, C.-C. Hung, M. O'Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, ... J. M. Jumper, Accurate structure prediction of biomolecular interactions with AlphaFold 3. *Nature* (2024). https://doi.org/10.1038/s41586-024-07487-w.

15. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. *Nature* 521, 436-444 (2015). https://doi.org/10.1038/nature14539.

16. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. U. Kaiser, I. Polosukhin, "Attention is All you Need" in *Advances in Neural Information Processing Systems (NIPS 2017)* (Curran Associates, Inc., 2017) vol. 30. https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.

17. J. Sevilla, L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, P. Villalobos, "Compute Trends Across Three Eras of Machine Learning" in *2022 International Joint Conference on Neural Networks (IJCNN 2022)* (2022) pp. 1-8. https://doi.org/10.1109/ijcnn55064.2022.9891914.

18. C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, S. Zhang, G. Ghosh, M. Lewis, L. Zettlemoyer, O. Levy, "LIMA: Less Is More for Alignment" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=KBMOKmX2he.

19. R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, C. Finn, "Direct Preference Optimization: Your Language Model is Secretly a Reward Model" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=HPuSIXJaa9&utm.
References

* denotes that the reference was either published by an AI company or at least 50% of the authors of a preprint article have an AI company as their affiliation.

1. Simmons-Edler, R., Badman, R., Longpre, S., & Rajan, K. (2024). AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research. arXiv preprint arXiv:2405.01859. Online at: https://arxiv.org/abs/2405.01859.

2.* OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, R. Avila, I. Babuschkin, S. Balaji, V. Balcom, P. Baltescu, H. Bao, M. Bavarian, J. Belgum, ... B. Zoph, "GPT-4 Technical Report" (OpenAI, 2024); http://arxiv.org/abs/2303.08774.

3.* Gemini Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, D. Silver, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. Lillicrap, A. Lazaridou, ... O. Vinyals, "Gemini: A Family of Highly Capable Multimodal Models" (Google DeepMind, 2023); http://arxiv.org/abs/2312.11805.

4.* Anthropic, "The Claude 3 Model Family: Opus, Sonnet, Haiku" (Anthropic, 2024); https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf.

5.* Wen Team, J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang, B. Hui, L. Ji, M. Li, J. Lin, R. Lin, D. Liu, G. Liu, C. Lu, ... T. Zhu, Wen Technical Report, arXiv:2309.16609 [cs.CL] (2023). http://arxiv.org/abs/2309.16609.

6.* Meta, Build the future of AI with Meta Llama 3 (2024). https://llama.meta.com/llama3/.

7.* A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. Le Scao, T. Lavril, T. Wang, T. Lacroix, W. El Sayed, "Mistral 7B" (Mistral AI, 2023); https://doi.org/10.48550/arXiv.2310.06825.

8. L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, M.-H. Yang, Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Comput. Surv. 56, 1-39 (2023). https://doi.org/10.1145/3626235.

9.* OpenAI, "DALL-E 3 system card" (OpenAI, 2023); https://cdn.openai.com/papers/DALL_E_3_System_Card.pdf.

10.* Midjourney, Midjourney Documentation (2024). https://docs.midjourney.com/v1/en.

11.* Stability AI, Stable Diffusion 3 (2024). https://stability.ai/news/stable-diffusion-3.

12.* T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, A. Ramesh, "Video generation models as world simulators" (OpenAI, 2024); https://openai.com/research/video-generation-models-as-world-simulators.

13. D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, ... P. Florence, "PaLM-E: an embodied multimodal language model" in *Proceedings of the 40th International Conference on Machine Learning* (ICML'23) (PMLR, 2023) vol. 202, pp. 8469-8488. https://dl.acm.org/doi/10.5555/3618408.3618748.

14. J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick, S. W. Bodenstein, D. A. Evans, C.-C. Hung, M. O'Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, ... J. M. Jumper, Accurate structure prediction of biomolecular interactions with AlphaFold 3. *Nature* (2024). https://doi.org/10.1038/s41586-024-07487-w.

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17. J. Sevilla, L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, P. Villalobos, "Compute Trends Across Three Eras of Machine Learning" in *2022 International Joint Conference on Neural Networks (IJCNN 2022)* (2022) pp. 1-8. https://doi.org/10.1109/ijcnn55064.2022.9891914.

18. C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, S. Zhang, G. Ghosh, M. Lewis, L. Zettlemoyer, O. Levy, "LIMA: Less Is More for Alignment" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=KBMOKmX2he.

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24. J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. V. Le, D. Zhou, "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" in *Advances in Neural Information Processing Systems (NeurIPS 2022)* (2022) vol. 35, pp. 24824-24837. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html.

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27. R. Bommasani, D. Soylu, T. I. Liao, K. A. Creel, P. Liang, Ecosystem Graphs: The Social Footprint of Foundation Models, arXiv:2303.15772 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2303.15772.

28.* A. Das, W. Kong, R. Sen, Y. Zhou, A decoder-only foundation model for time-series forecasting, arXiv:2310.10688 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.10688.

29.* P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, I. Sutskever, "Jukebox: A Generative Model for Music" (OpenAI, 2020); http://arxiv.org/abs/2005.00341.

30. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, ... D. Amodei, "Language Models are Few-Shot Learners" in *Advances in Neural Information Processing Systems (Curran Associates, Inc., 2020) vol. 33*, pp. 1877-1901. https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.

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34. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" in *The 9th International Conference on Learning Representations (ICLR 2021)* (2021). https://openreview.net/forum?id=YicbFdNTTy.

35. A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, "Learning Transferable Visual Models From Natural Language Supervision" in *Proceedings of the 38th International Conference on Machine Learning (ICML 2021)* (PMLR, 2021) pp. 8748-8763. https://proceedings.mlr.press/v139/radford21a.html.

36.* A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, R. Girshick, "Segment Anything" (Meta AI, 2023); http://arxiv.org/abs/2304.02643.

37.* Meta, V-JEPA: The next step toward advanced machine intelligence (2024). https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/.

38.* OpenAI, Sora: Creating video from text (2023). https://openai.com/sora.

39. B. Ichter, A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, D. Kalashnikov, S. Levine, Y. Lu, C. Parada, K. Rao, P. Sermanet, A. T. Toshev, V. Vanhoucke, ... C. K. Fu, "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances" in *Proceedings of The 6th Annual Conference on Robot Learning (CoRL)* (PMLR, 2022) vol. 205. https://openreview.net/forum?id=bdHkMjBJG_w.
20. L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Gray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, ... R. Lowe, "Training language models to follow instructions with human feedback" in *36th Conference on Neural Information Processing Systems (NeurIPS 2022)* (2022). https://openreview.net/forum?id=TG8KACxEON.

21.* Y. Bai, A. Jones, K. Ndousse, A. Askell, A. Chen, N. DasSarma, D. Drain, S. Fort, D. Ganguli, T. Henighan, N. Joseph, S. Kadavath, J. Kernion, T. Conerly, S. El-Showk, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, T. Hume, ... J. Kaplan, Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, arXiv:2204.05862 [cs.CL] (2022). https://doi.org/10.48550/arXiv.2204.05862.

22.* H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, ... T. Scialom, "Llama 2: Open Foundation and Fine-Tuned Chat Models" (Meta AI, 2023); http://arxiv.org/abs/2307.09288.

23. L. Sharkey, C. Ní Ghuidhir, D. Braun, J. Scheurer, M. Balesni, L. Bushnaq, C. Stix, M. Hobbhahn, A Causal Framework for AI Regulation and Auditing. (2024). https://doi.org/10.20944/preprints202401.1424.v1.

24. J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. V. Le, D. Zhou, "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" in *Advances in Neural Information Processing Systems (NeurIPS 2022)* (2022) vol. 35, pp. 24824-24837. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html.

25. T. Davidson, J.-S. Denain, P. Villalobos, G. Bas, "AI capabilities can be significantly improved without expensive retraining" (Epoch, 2023); http://arxiv.org/abs/2312.07413.

26. L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, J. Wen, A survey on large language model based autonomous agents. *Front. Comput. Sci.* **18**, 186345 (2024). https://doi.org/10.1007/s11704-024-40231-1.

27. R. Bommasani, D. Soylu, T. I. Liao, K. A. Creel, P. Liang, Ecosystem Graphs: The Social Footprint of Foundation Models, arXiv:2303.15772 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2303.15772.

28.* A. Das, W. Kong, R. Sen, Y. Zhou, A decoder-only foundation model for time-series forecasting, arXiv:2310.10688 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.10688.

29.* P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, I. Sutskever, "Jukebox: A Generative Model for Music" (OpenAI, 2020); http://arxiv.org/abs/2005.00341.

30. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, ... D. Amodei, "Language Models are Few-Shot Learners" in *Advances in Neural Information Processing Systems (Curran Associates, Inc., 2020) vol. 33*, pp. 1877-1901. https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.

31.* S. Pichai, D. Hassabis, Our next-generation model: Gemini 1.5. (2024). https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/.

32. Fan, Gokkaya, Harman, Lyubarskiy, Sengupta, Yoo, Zhang, "Large Language Models for Software Engineering: Survey and Open Problems" in *2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)* (2023) vol. 0, pp. 31-53. https://doi.org/10.1109/ICSE-FoSE59343.2023.00008.

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79.* E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, É. Goffinet, D. Hesslow, J. Launay, Q. Malartic, D. Mazzotta, B. Noune, B. Pannier, G. Penedo, The Falcon Series of Open Language Models, arXiv:2311.16867 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2311.16867.

80.* T. Wei, L. Zhao, L. Zhang, B. Zhu, L. Wang, H. Yang, B. Li, C. Cheng, W. Lü, R. Hu, C. Li, L. Yang, X. Luo, X. Wu, L. Liu, W. Cheng, P. Cheng, J. Zhang, X. Zhang, . . . Y. Zhou, Skywork: A More Open Bilingual Foundation Model, arXiv:2310.19341 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.19341.

81. N. Muennighoff, A. Rush, B. Barak, T. Le Scao, N. Tazi, A. Piktus, S. Pyysalo, T. Wolf, C. A. Raffel, "Scaling Data-Constrained Language Models" in Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track (2023) vol. 36, pp. 50358-50376.
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82. P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, A. Ho, Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning, arXiv:2211.04325 [cs.LG] (2022). http://arxiv.org/abs/2211.04325.

83. M. Marion, A. Üstün, L. Pozzobon, A. Wang, M. Fadaee, S. Hooker, "When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale" in 1st Workshop on Attributing Model Behavior at Scale (2023). https://openreview.net/forum?id=XUIYn3jo5T.

84. G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, H. Alobeidli, A. Cappelli, B. Pannier, E. Almazrouei, J. Launay, "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data Only" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Datasets and Benchmarks Track (2023). https://openreview.net/pdf?id=kM5eGcdCzq.

85. S. M. Xie, H. Pham, X. Dong, N. Du, H. Liu, Y. Lu, P. Liang, Q. V. Le, T. Ma, A. W. Yu, "DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=lXuByUeHhd.

86.* M. Mitchell, A. S. Luccioni, N. Lambert, M. Gerchick, A. McMillan-Major, E. Ozoani, N. Rajani, T. Thrush, Y. Jernite, D. Kiela, Measuring Data, arXiv:2212.05129 [cs.AI] (2022). https://doi.org/10.48550/arXiv.2212.05129.

87.* D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, G. Irving, "Fine-Tuning Language Models from Human Preferences" (OpenAI, 2020); http://arxiv.org/abs/1909.08593.

88.* D. Hernandez, T. B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, arXiv:2005.04305 [cs.LG] (2020). https://doi.org/10.48550/arXiv.2005.04305.

89. A. Ho, T. Besiroglu, E. Erdil, D. Owen, R. Rahman, Z. C. Guo, D. Atkinson, N. Thompson, J. Sevilla, "Algorithmic progress in language models" (Epoch, 2024); http://arxiv.org/abs/2403.05812.

90. F. E. Dorner, Measuring Progress in Deep Reinforcement Learning Sample Efficiency, arXiv:2102.04881 [cs.LG] (2021). https://doi.org/10.48550/arXiv.2102.04881.

91.* S. Chen, S. Wong, L. Chen, Y. Tian, Extending Context Window of Large Language Models via Positional Interpolation, arXiv:2306.15595 [cs.CL] (2023). http://arxiv.org/abs/2306.15595.

92.* Anthropic, Long context prompting for Claude 2.1 (2023). https://www.anthropic.com/news/claude-2-1-prompting.

93. A. Gu, T. Dao, Mamba: Linear-Time Sequence Modeling with Selective State Spaces, arXiv:2312.00752 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2312.00752.

94. D. Kiela, M. Bartolo, Y. Nie, D. Kaushik, A. Geiger, Z. Wu, B. Vidgen, G. Prasad, A. Singh, P. Ringshia, Z. Ma, T. Thrush, S. Riedel, Z. Waseem, P. Stenetorp, R. Jia, M. Bansal, C. Potts, A. Williams, "Dynabench: Rethinking benchmarking in NLP" in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Association for Computational Linguistics, 2021) pp. 4110-4124. https://doi.org/10.18653/v1/2021.naacl-main.324.

95. D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, J. Steinhardt, "Measuring Massive Multitask Language Understanding" in The 9th International Conference on Learning Representations (ICLR 2021) (2021). https://openreview.net/forum?id=d7KBjml3GmQ.

96. A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, A. Kluska, A. Lewkowycz, A. Agarwal, A. Power, A. Ray, A. Warstadt, A. W. Kocurek, A. Safaya, A. Tazarv, ... Z. Wu, Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research (2023). https://openreview.net/forum?id=uyTL5Bvosj.

97. D. Rein, B. L. Hou, A. C. Stickland, J. Petty, R. Y. Pang, J. Dirani, J. Michael, S. R. Bowman, GPQA: A Graduate-Level Google-Proof Q&A Benchmark, arXiv:2311.12022 [cs.AI] (2023). http://arxiv.org/abs/2311.12022.

98. D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, J. Steinhardt, "Measuring Mathematical Problem Solving With the MATH Dataset" in 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Datasets and Benchmarks Track (Round 2) (2021). https://openreview.net/forum?id=7Bywt2mQsCe.

99.* S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, Y. Zhang, Sparks of Artificial General Intelligence: Early experiments with GPT-4, arXiv:2303.12712 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2303.12712.

100. A. Zhou, K. Wang, Z. Lu, W. Shi, S. Luo, Z. Qin, S. Lu, A. Jia, L. Song, M. Zhan, H. Li, "Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification" in The 12th International Conference on Learning Representations (ICLR 2024) (2023). https://openreview.net/forum?id=c8McWs4Av0.

101. T. R. McIntosh, T. Susnjak, T. Liu, P. Watters, M. N. Halgamuge, Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence, arXiv:2402.09880 [cs.AI] (2024). https://doi.org/10.48550/arXiv.2402.09880.

102. M. Mitchell, A. B. Palmarini, A. K. Moskvichev, "Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks" in AAAI 2024 Workshop "Are Large Language Models Simply Causal Parrots?" (2023). https://openreview.net/forum?id=3rGT5OkzpC.
82. P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, A. Ho, Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning, arXiv:2211.04325 [cs.LG] (2022). http://arxiv.org/abs/2211.04325.

83. M. Marion, A. Üstün, L. Pozzobon, A. Wang, M. Fadaee, S. Hooker, "When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale" in *1st Workshop on Attributing Model Behavior at Scale* (2023). https://openreview.net/forum?id=XUIYn3jo5T.

84. G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, H. Alobeidli, A. Cappelli, B. Pannier, E. Almazrouei, J. Launay, "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data Only" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023) Datasets and Benchmarks Track* (2023). https://openreview.net/pdf?id=kM5eGcdCzq.

85. S. M. Xie, H. Pham, X. Dong, N. Du, H. Liu, Y. Lu, P. Liang, Q. V. Le, T. Ma, A. W. Yu, "DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=lXuByUeHhd.

86.* M. Mitchell, A. S. Luccioni, N. Lambert, M. Gerchick, A. McMillan-Major, E. Ozoani, N. Rajani, T. Thrush, Y. Jernite, D. Kiela, Measuring Data, arXiv:2212.05129 [cs.AI] (2022). https://doi.org/10.48550/arXiv.2212.05129.

87.* D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, G. Irving, "Fine-Tuning Language Models from Human Preferences" (OpenAI, 2020); http://arxiv.org/abs/1909.08593.

88.* D. Hernandez, T. B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, arXiv:2005.04305 [cs.LG] (2020). https://doi.org/10.48550/arXiv.2005.04305.

89. A. Ho, T. Besiroglu, E. Erdil, D. Owen, R. Rahman, Z. C. Guo, D. Atkinson, N. Thompson, J. Sevilla, "Algorithmic progress in language models" (Epoch, 2024); http://arxiv.org/abs/2403.05812.

90. F. E. Dorner, Measuring Progress in Deep Reinforcement Learning Sample Efficiency, arXiv:2102.04881 [cs.LG] (2021). https://doi.org/10.48550/arXiv.2102.04881.

91.* S. Chen, S. Wong, L. Chen, Y. Tian, Extending Context Window of Large Language Models via Positional Interpolation, arXiv:2306.15595 [cs.CL] (2023). http://arxiv.org/abs/2306.15595.

92.* Anthropic, Long context prompting for Claude 2.1 (2023). https://www.anthropic.com/news/claude-2-1-prompting.

93. A. Gu, T. Dao, Mamba: Linear-Time Sequence Modeling with Selective State Spaces, arXiv:2312.00752 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2312.00752.

94. D. Kiela, M. Bartolo, Y. Nie, D. Kaushik, A. Geiger, Z. Wu, B. Vidgen, G. Prasad, A. Singh, P. Ringshia, Z. Ma, T. Thrush, S. Riedel, Z. Waseem, P. Stenetorp, R. Jia, M. Bansal, C. Potts, A. Williams, "Dynabench: Rethinking benchmarking in NLP" in *Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies* (Association for Computational Linguistics, 2021) pp. 4110-4124. https://doi.org/10.18653/v1/2021.naacl-main.324.

95. D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, J. Steinhardt, "Measuring Massive Multitask Language Understanding" in *The 9th International Conference on Learning Representations (ICLR 2021)* (2021). https://openreview.net/forum?id=d7KBjml3GmQ.

96. A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, A. Kluska, A. Lewkowycz, A. Agarwal, A. Power, A. Ray, A. Warstadt, A. W. Kocurek, A. Safaya, A. Tazarv, ... Z. Wu, Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. *Transactions on Machine Learning Research* (2023). https://openreview.net/forum?id=uyTL5Bvosj.

97. D. Rein, B. L. Hou, A. C. Stickland, J. Petty, R. Y. Pang, J. Dirani, J. Michael, S. R. Bowman, GPQA: A Graduate-Level Google-Proof Q&A Benchmark, arXiv:2311.12022 [cs.AI] (2023). http://arxiv.org/abs/2311.12022.

98. D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, J. Steinhardt, "Measuring Mathematical Problem Solving With the MATH Dataset" in *35th Conference on Neural Information Processing Systems (NeurIPS 2021) Datasets and Benchmarks Track (Round 2)* (2021). https://openreview.net/forum?id=7Bywt2mQsCe.

99.* S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, Y. Zhang, Sparks of Artificial General Intelligence: Early experiments with GPT-4, arXiv:2303.12712 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2303.12712.

100. A. Zhou, K. Wang, Z. Lu, W. Shi, S. Luo, Z. Qin, S. Lu, A. Jia, L. Song, M. Zhan, H. Li, "Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification" in *The 12th International Conference on Learning Representations (ICLR 2024)* (2023). https://openreview.net/forum?id=c8McWs4Av0.

101. T. R. McIntosh, T. Susnjak, T. Liu, P. Watters, M. N. Halgamuge, Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence, arXiv:2402.09880 [cs.AI] (2024). https://doi.org/10.48550/arXiv.2402.09880.

102. M. Mitchell, A. B. Palmarini, A. K. Moskvichev, "Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks" in AAAI 2024 Workshop "Are Large Language Models Simply Causal Parrots?" (2023). https://openreview.net/forum?id=3rGT5OkzpC.
103. S. Srivastava, M. B. Annarose, P. V. Anto, S. Menon, A. Sukumar, S. T. Adwaith, A. Philipose, S. Prince, S. Thomas, Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap, arXiv:2402.19450 [cs.AI] (2024). https://doi.org/10.48550/arXiv.2402.19450.

104. C. Deng, Y. Zhao, X. Tang, M. Gerstein, A. Cohan, Investigating Data Contamination in Modern Benchmarks for Large Language Models, arXiv:2311.09783 [cs.CL] (2023). http://arxiv.org/abs/2311.09783.

105. O. Sainz, J. Campos, I. García-Ferrero, J. Etxaniz, O. L. de Lacalle, E. Agirre, "NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark" in Findings of the Association for Computational Linguistics: EMNLP 2023 (Association for Computational Linguistics, 2023) pp. 10776-10787. https://doi.org/10.18653/v1/2023.findings-emnlp.722.

106. Y. Cao, L. Zhou, S. Lee, L. Cabello, M. Chen, D. Hershcovich, "Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study" in Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) (Association for Computational Linguistics, 2023) pp. 53-67. https://doi.org/10.18653/v1/2023.c3nlp-1.7.

107. L. Berglund, M. Tong, M. Kaufmann, M. Balesni, A. C. Stickland, T. Korbak, O. Evans, "The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"" in The 12th International Conference on Learning Representations (ICLR 2024) (2023). https://openreview.net/forum?id=GPKTIktAOk.

108. J. Geiping, A. Stein, M. Shu, K. Saifullah, Y. Wen, T. Goldstein, "Coercing LLMs to do and reveal (almost) anything" in ICLR 2024 Workshop on Secure and Trustworthy Large Language Models (SET LLM) (2024). https://openreview.net/forum?id=Y5inHAjMu0.

109.* J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, D. Amodei, Scaling Laws for Neural Language Models, arXiv:2001.08361 [cs.LG] (2020). https://doi.org/10.48550/arXiv.2001.08361.

110.* J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, ... L. Sifre, Training Compute-Optimal Large Language Models, arXiv:2203.15556 [cs.CL] (2022). http://arxiv.org/abs/2203.15556.

111.* T. Henighan, J. Kaplan, M. Katz, M. Chen, C. Hesse, J. Jackson, H. Jun, T. B. Brown, P. Dhariwal, S. Gray, C. Hallacy, B. Mann, A. Radford, A. Ramesh, N. Ryder, D. M. Ziegler, J. Schulman, D. Amodei, S. McCandlish, Scaling Laws for Autoregressive Generative Modeling, arXiv:2010.14701 [cs.LG] (2020). http://arxiv.org/abs/2010.14701.

112. X. Zhai, A. Kolesnikov, N. Houlsby, L. Beyer, "Scaling Vision Transformers" in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022) pp. 1204-1213. https://doi.org/10.1109/cvpr52688.2022.01179.

113.* A. L. Jones, Scaling Scaling Laws with Board Games, arXiv:2104.03113 [cs.LG] (2021). https://doi.org/10.48550/arXiv.2104.03113.

114. S. Goyal, P. Maini, Z. C. Lipton, A. Raghunathan, J. Z. Kolter, "The Science of Data Filtering: Data Curation Cannot Be Compute Agnostic" in ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models (DPFM) (2024). https://openreview.net/forum?id=9wzo4EjEGM.

115.* Y. Bahri, E. Dyer, J. Kaplan, J. Lee, U. Sharma, Explaining Neural Scaling Laws, arXiv:2102.06701 [cs.LG] (2021). https://doi.org/10.48550/arXiv.2102.06701.

116.* A. Maloney, D. A. Roberts, J. Sully, A Solvable Model of Neural Scaling Laws, arXiv:2210.16859 [cs.LG] (2022). http://arxiv.org/abs/2210.16859.

117. U. Sharma, J. Kaplan, Scaling laws from the data manifold dimension. J. Mach. Learn. Res. 23, 343-376 (2022). https://dl.acm.org/doi/abs/10.5555/3586589.3586598.

118. Ł. Dębowski, A Simplistic Model of Neural Scaling Laws: Multiperiodic Santa Fe Processes, arXiv:2302.09049 [cs.IT] (2023). http://arxiv.org/abs/2302.09049.

119. E. J. Michaud, Z. Liu, U. Girit, M. Tegmark, "The Quantization Model of Neural Scaling" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=3tbTw2ga8K.

120. S. Biderman, U. S. Prashanth, L. Sutawika, H. Schoelkopf, Q. G. Anthony, S. Purohit, E. Raff, "Emergent and Predictable Memorization in Large Language Models" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=lqODvhB4Kf.

121. D. Ganguli, D. Hernandez, L. Lovitt, A. Askell, Y. Bai, A. Chen, T. Conerly, N. Dassarma, D. Drain, N. Elhage, S. El Showk, S. Fort, Z. Hatfield-Dodds, T. Henighan, S. Johnston, A. Jones, N. Joseph, J. Kernian, S. Kravec, ... J. Clark, "Predictability and Surprise in Large Generative Models" in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery, 2022) pp. 1747-1764. https://doi.org/10.1145/3531146.3533229.

122.* Z. Du, A. Zeng, Y. Dong, J. Tang, Understanding Emergent Abilities of Language Models from the Loss Perspective, arXiv:2403.15796 [cs.CL] (2024). http://arxiv.org/abs/2403.15796.
103. S. Srivastava, M. B. Annarose, P. V. Anto, S. Menon, A. Sukumar, S. T. Adwaith, A. Philipose, S. Prince, S. Thomas, Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap, arXiv:2402.19450 [cs.AI] (2024). https://doi.org/10.48550/arXiv.2402.19450.

104. C. Deng, Y. Zhao, X. Tang, M. Gerstein, A. Cohan, Investigating Data Contamination in Modern Benchmarks for Large Language Models, arXiv:2311.09783 [cs.CL] (2023). http://arxiv.org/abs/2311.09783.

105. O. Sainz, J. Campos, I. García-Ferrero, J. Etxaniz, O. L. de Lacalle, E. Agirre, "NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark" in Findings of the Association for Computational Linguistics: EMNLP 2023 (Association for Computational Linguistics, 2023) pp. 10776-10787.
https://doi.org/10.18653/v1/2023.findings-emnlp.722.

106. Y. Cao, L. Zhou, S. Lee, L. Cabello, M. Chen, D. Hershcovich, "Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study" in Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) (Association for Computational Linguistics, 2023) pp. 53-67.
https://doi.org/10.18653/v1/2023.c3nlp-1.7.

107. L. Berglund, M. Tong, M. Kaufmann, M. Balesni, A. C. Stickland, T. Korbak, O. Evans, "The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"" in The 12th International Conference on Learning Representations (ICLR 2024) (2023). https://openreview.net/forum?id=GPKTIktAOk.

108. J. Geiping, A. Stein, M. Shu, K. Saifullah, Y. Wen, T. Goldstein, "Coercing LLMs to do and reveal (almost) anything" in ICLR 2024 Workshop on Secure and Trustworthy Large Language Models (SET LLM) (2024).
https://openreview.net/forum?id=Y5inHAjMu0.

109.* J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, D. Amodei, Scaling Laws for Neural Language Models, arXiv:2001.08361 [cs.LG] (2020). https://doi.org/10.48550/arXiv.2001.08361.

110.* J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, ... L. Sifre, Training Compute-Optimal Large Language Models, arXiv:2203.15556 [cs.CL] (2022).
http://arxiv.org/abs/2203.15556.

111.* T. Henighan, J. Kaplan, M. Katz, M. Chen, C. Hesse, J. Jackson, H. Jun, T. B. Brown, P. Dhariwal, S. Gray, C. Hallacy, B. Mann, A. Radford, A. Ramesh, N. Ryder, D. M. Ziegler, J. Schulman, D. Amodei, S. McCandlish, Scaling Laws for Autoregressive Generative Modeling, arXiv:2010.14701 [cs.LG] (2020). http://arxiv.org/abs/2010.14701.

112. X. Zhai, A. Kolesnikov, N. Houlsby, L. Beyer, "Scaling Vision Transformers" in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022) pp. 1204-1213. https://doi.org/10.1109/cvpr52688.2022.01179.

113.* A. L. Jones, Scaling Scaling Laws with Board Games, arXiv:2104.03113 [cs.LG] (2021).
https://doi.org/10.48550/arXiv.2104.03113.

114. S. Goyal, P. Maini, Z. C. Lipton, A. Raghunathan, J. Z. Kolter, "The Science of Data Filtering: Data Curation Cannot Be Compute Agnostic" in ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models (DPFM) (2024). https://openreview.net/forum?id=9wzo4EjEGM.

115.* Y. Bahri, E. Dyer, J. Kaplan, J. Lee, U. Sharma, Explaining Neural Scaling Laws, arXiv:2102.06701 [cs.LG] (2021).
https://doi.org/10.48550/arXiv.2102.06701.

116.* A. Maloney, D. A. Roberts, J. Sully, A Solvable Model of Neural Scaling Laws, arXiv:2210.16859 [cs.LG] (2022).
http://arxiv.org/abs/2210.16859.

117. U. Sharma, J. Kaplan, Scaling laws from the data manifold dimension. J. Mach. Learn. Res. 23, 343-376 (2022).
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192. B. Hutchinson, N. Rostamzadeh, C. Greer, K. Heller, V. Prabhakaran, "Evaluation Gaps in Machine Learning Practice" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 1859-1876. https://doi.org/10.1145/3531146.3533233.

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195.* M. R. Morris, J. Sohl-dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, S. Legg, "Levels of AGI: Operationalizing Progress on the Path to AGI" (Google DeepMind, 2024); http://arxiv.org/abs/2311.02462.

196. S. Shankar, Y. Halpern, E. Breck, J. Atwood, J. Wilson, D. Sculley, "No classification without representation: Assessing geodiversity issues in open data sets for the developing world" in *31st Conference on Neural Information Processing Systems (NIPS 2017) Machine learning for the developing world workshop* (2017).

197. L. Aroyo, C. Welty, Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation. *AI Mag.* **36**, 15-24 (2015). https://doi.org/10.1609/aimag.v36i1.2564.

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211. R. Shah, Q. F. Montixi, S. Pour, A. Tagade, J. Rando, "Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Socially Responsible Language Modelling Research Workshop (SoLaR) (2023). https://openreview.net/forum?id=x3Ltqz1UFg.

212. A. Rao, S. Vashistha, A. Naik, S. Aditya, M. Choudhury, "Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks" in 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (2024). https://doi.org/10.48550/arXiv.2305.14965.

213. V. Ojewale, R. Steed, B. Vecchione, A. Birhane, I. D. Raji, Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling, arXiv:2402.17861 [cs.CY] (2024). https://doi.org/10.48550/arXiv.2402.17861.

214. V. Turri, R. Dzombak, "Why We Need to Know More: Exploring the State of AI Incident Documentation Practices" in Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23) (ACM, 2023) pp. 576–583.
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215. S. Costanza-Chock, I. D. Raji, J. Buolamwini, "Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem" in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22) (Association for Computing Machinery, 2022) pp. 1571–1583.
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218. S. Casper, T. Bu, Y. Li, J. Li, K. Zhang, K. Hariharan, D. Hadfield-Menell, "Red Teaming Deep Neural Networks with Feature Synthesis Tools" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023).
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219. S. Friedler, R. Singh, B. Blili-Hamelin, J. Metcalf, B. J. Chen, "AI Red-Teaming Is Not a One-Stop Solution to AI Harms: Recommendations for Using Red-Teaming for AI Accountability" (Data & Society, 2023);
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230. T. de Vries, I. Misra, C. Wang, L. van der Maaten, "Does object recognition work for everyone?" in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops (2019). https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/de_Vries_DoesObjectRecognition Work_for_Everyone_CVPRW_2019_paper.pdf.

231. The New York Times Company v. Microsoft Corporation et al. (US District Court for the Southern District of New York, 2023). https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec2023.pdf.

232. J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell, M. Gardner, "Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus" in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) (Association for Computational Linguistics, 2021) pp. 1286-1305. https://doi.org/10.18653/v1/2021.emnlp-main.98.

233. A. S. Luccioni, F. Corry, H. Sridharan, M. Ananny, J. Schultz, K. Crawford, "A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication" in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22) (Association for Computing Machinery, 2022) pp. 199-212. https://doi.org/10.1145/3531146.3533086.

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236. S. Longpre, R. Mahari, A. Chen, N. Obeng-Marnu, D. Sileo, W. Brannon, N. Muennighoff, N. Khazam, J. Kabbara, K. Perisetla, X. Wu, E. Shippole, K. Bollacker, T. Wu, L. Villa, S. Pentland, S. Hooker, The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI, arXiv:2310.16787 [cs.CL] (2023). http://arxiv.org/abs/2310.16787.

237. E. Perez, S. Ringer, K. Lukosiute, K. Nguyen, E. Chen, S. Heiner, C. Pettit, C. Olsson, S. Kundu, S. Kadavath, A. Jones, A. Chen, B. Mann, B. Israel, B. Seethor, C. McKinnon, C. Olah, D. Yan, D. Amodei, ... J. Kaplan, "Discovering Language Model Behaviors with Model-Written Evaluations" in Findings of the Association for Computational Linguistics: ACL 2023 (Association for Computational Linguistics, 2023) pp. 13387-13434. https://doi.org/10.18653/v1/2023.findings-acl.847.

238. M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. R. Bowman, E. Durmus, Z. Hatfield-Dodds, S. R. Johnston, S. M. Kravec, T. Maxwell, S. McCandlish, K. Ndousse, O. Rausch, N. Schiefer, D. Yan, M. Zhang, E. Perez, "Towards
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222. P. Henderson, X. Li, D. Jurafsky, T. Hashimoto, M. A. Lemley, P. Liang, Foundation Models and Fair Use, arXiv:2303.15715 [cs.CY] (2023). http://arxiv.org/abs/2303.15715.

223. A. Luccioni, J. Viviano, "What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus" in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Association for Computational Linguistics, 2021) pp. 182-189. https://doi.org/10.18653/v1/2021.acl-short.24.

224. A. Birhane, V. Prabhu, S. Han, V. N. Boddeti, A. S. Luccioni, "Into the LAION's Den: Investigating Hate in Multimodal Datasets" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=6URyQ9QhYv&notId=6URyQ9QhYv.

225. Y. Qu, X. Shen, X. He, M. Backes, S. Zannettou, Y. Zhang, "Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models" in Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS '23) (Association for Computing Machinery, 2023) pp. 3403-3417. https://doi.org/10.1145/3576915.3616679.

226. A. Birhane, V. U. Prabhu, E. Kahembwe, Multimodal datasets: misogyny, pornography, and malignant stereotypes, arXiv:2110.01963 [cs.CY] (2021). http://arxiv.org/abs/2110.01963.

227. N. Shahbazi, Y. Lin, A. Asudeh, H. V. Jagadish, Representation Bias in Data: A Survey on Identification and Resolution Techniques. ACM Comput. Surv. 55, 293:1-293:39 (2023). https://doi.org/10.1145/3588433.

228. D. Thiel, "Identifying and Eliminating CSAM in Generative ML Training Data and Models" (Stanford Internet Observatory, 2023); https://purl.stanford.edu/kh752sm9123.

229. J. Kreutzer, I. Caswell, L. Wang, A. Wahab, D. van Esch, N. Ulzii-Orshikh, A. Tapo, N. Subramani, A. Sokolov, C. Sikasote, M. Setyawan, S. Sarin, S. Samb, B. Sagot, C. Rivera, A. Rios, I. Papadimitriou, S. Osei, P. O. Suarez, ... M. Adeyemi, Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Trans. Assoc. Comput. Linguist. 10, 50-72 (2022). https://doi.org/10.1162/tacl_a_00447.

230. T. de Vries, I. Misra, C. Wang, L. van der Maaten, "Does object recognition work for everyone?" in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops (2019). https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/de_Vries_DoesObjectRecognition Work_for_Everyone_CVPRW_2019_paper.pdf.

231. The New York Times Company v. Microsoft Corporation et al. (US District Court for the Southern District of New York, 2023). https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec2023.pdf.

232. J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell, M. Gardner, "Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus" in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) (Association for Computational Linguistics, 2021) pp. 1286-1305. https://doi.org/10.18653/v1/2021.emnlp-main.98.

233. A. S. Luccioni, F. Corry, H. Sridharan, M. Ananny, J. Schultz, K. Crawford, "A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication" in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22) (Association for Computing Machinery, 2022) pp. 199-212. https://doi.org/10.1145/3531146.3533086.

234. K. Peng, A. Mathur, A. Narayanan, "Mitigating dataset harms requires stewardship: Lessons from 1000 papers" in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) round2 (2021). https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/077e29b11be80ab57e1a2ecabb7da330-Abstract-round2.html.

235. B. Magagna, D. Goldfarb, P. Martin, M. Atkinson, S. Koulouzis, Z. Zhao, "Data Provenance" in Towards Interoperable Research Infrastructures for Environmental and Earth Sciences: A Reference Model Guided Approach for Common Challenges, Z. Zhao, M. Hellström, Eds. (Springer International Publishing, Cham, 2020), pp. 208-225.

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253. I. D. Raji, P. Xu, C. Honigsberg, D. Ho, "Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance" in *Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (Association for Computing Machinery, 2022)* pp. 557–571. https://doi.org/10.1145/3514094.3534181.

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255. R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, J. Ortega-Garcia, Deepfakes and beyond: A Survey of face manipulation and fake detection. *Information Fusion* **64**, 131–148 (2020). https://doi.org/10.1016/j.inffus.2020.06.014.

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241. Z. Song, T. Cai, J. D. Lee, W. J. Su, "Reward Collapse in Aligning Large Language Models: A Prompt-Aware Approach to Preference Rankings" in *ICML 2023 Workshop The Many Facets of Preference-Based Learning* (2023).
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243. V. H. Maluleke, N. Thakkar, T. Brooks, E. Weber, T. Darrell, A. A. Efros, A. Kanazawa, D. Guillory, "Studying Bias in GANs Through the Lens of Race" in *Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIII* (Springer-Verlag, 2022) pp. 344–360. https://doi.org/10.1007/978-3-031-19778-9_20.

244. R. Bommasani, K. Klyman, S. Longpre, S. Kapoor, N. Maslej, B. Xiong, D. Zhang, P. Liang, "The Foundation Model Transparency Index" (Center for Research on Foundation Models (CRFM) and Institute on Human-Centered Artificial Intelligence (HAI), 2023); http://arxiv.org/abs/2310.12941.

245. V. Lai, C. Chen, A. Smith-Renner, Q. V. Liao, C. Tan, "Towards a Science of Human-Al Decision Making: An Overview of Design Space in Empirical Human-Subject Studies" in *Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23)* (Association for Computing Machinery, 2023) pp. 1369–1385.
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246. G. Bansal, B. Nushi, E. Kamar, W. S. Lasecki, D. S. Weld, E. Horvitz, *Beyond Accuracy: The Role of Mental Models in Human-Al Team Performance*. *HCOMP 7*, 2–11 (2019). https://doi.org/10.1609/hcomp.v7i1.5285.

247. R. Geirhos, K. Meding, F. A. Wichmann, "Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency" in *Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS '20)* (Curran Associates Inc., 2020) pp. 13890–13902.
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248. A. Albright, If you give a judge a risk score: evidence from Kentucky bail decisions. *Law, Economics, and Business Fellows' Discussion Paper Series* 85, 2019–1 (2019). http://www.law.harvard.edu/programs/olin_center/Prizes/2019-1.pdf.

249. M. Hoffman, L. B. Kahn, D. Li, Discretion in Hiring*. *The Quarterly Journal of Economics* **133**, 765–800 (2018).
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250. E. Brynjolfsson, D. Li, L. Raymond, "Generative Al at Work" (National Bureau of Economic Research, 2023); https://doi.org/10.3386/w31161.

251. V. Marda, S. Narayan, "Data in New Delhi's predictive policing system" in *Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery, 2020)* pp. 317–324.
https://doi.org/10.1145/3351095.3372865.

252. U. Ehsan, R. Singh, J. Metcalf, M. Riedl, "The Algorithmic Imprint" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 1305–1317.
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253. I. D. Raji, P. Xu, C. Honigsberg, D. Ho, "Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance" in *Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (Association for Computing Machinery, 2022)* pp. 557–571. https://doi.org/10.1145/3514094.3534181.

254. X. Shen, Z. Chen, M. Backes, Y. Shen, Y. Zhang, "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models, arXiv:2308.03825 [cs.CR] (2023). http://arxiv.org/abs/2308.03825.

255. R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, J. Ortega-Garcia, Deepfakes and beyond: A Survey of face manipulation and fake detection. *Information Fusion* **64**, 131–148 (2020). https://doi.org/10.1016/j.inffus.2020.06.014.

256. M. Mustak, J. Salminen, M. Mäntymäki, A. Rahman, Y. K. Dwivedi, Deepfakes: Deceptions, mitigations, and opportunities. *Journal of Business Research* **154**, 113368 (2023). https://doi.org/10.1016/j.jbusres.2022.113368.

257. M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, T. Gebru, "Model Cards for Model Reporting" in *Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM, 2019)* pp. 220–229. https://doi.org/10.1145/3287560.3287596.

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262. M. Arnold, R. K. E. Bellamy, M. Hind, S. Houde, S. Mehta, A. Mojsilović, R. Nair, K. N. Ramamurthy, A. Olteanu, D. Piorkowski, D. Reimer, J. Richards, J. Tsay, K. R. Varshney, FactSheets: Increasing trust in AI services through supplier's declarations of conformity. *IBM Journal of Research and Development* **63**, 6:1–6:13 (2019).
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264. I. D. Raji, A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, P. Barnes, "Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing" in *Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20)* (Association for Computing Machinery, 2020) pp. 33–44. https://doi.org/10.1145/3351095.3372873.

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266. M. Danilevsky, K. Qian, R. Aharonov, Y. Katsis, B. Kawas, P. Sen, "A Survey of the State of Explainable AI for Natural Language Processing" in *Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL 2020)* (Association for Computational Linguistics, 2020) pp. 447–459. https://aclanthology.org/2020.aacl-main.46.

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