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On the Opportunities and Risks of Foundation Models Rishi Bommasani* Drew A. Hudson Ehsan Adeli Russ Altman Simran Arora Sydney von Arx Michael S. Bernstein Jeannette Bohg Antoine Bosselut Emma Brunskill Erik Brynjolfsson Shyamal Buch Dallas Card Rodrigo Castellon Niladri Chatterji Annie Chen Kathleen Creel Jared Quinc... |
2 Center for Research on Foundation Models (CRFM) Contents Contents 2 1 Introduction 3 1. 1 Emergence and homogenization 3 1. 2 Social impact and the foundation models ecosystem 7 1. 3 The future of foundation models 9 1. 4 Overview of this report 12 2 Capabilities 21 2. 1 Language 22 2. 2 Vision 28 2. 3 Robotics 34 2.... |
On the Opportunities and Risks of Foundation Models 3 1 INTRODUCTION This report investigates an emerging paradigm for building artificial intelligence (AI) systems based on a general class of models which we term foundation models. 2A foundation model is any model that is trained on broad data (generally using self-su... |
4 Center for Research on Foundation Models (CRFM) represented a step towards homogenization: a wide range of applications could now be powered by a single generic learning algorithm such as logistic regression. Despite the ubiquity of machine learning within AI, semantically complex tasks in natural lan-guage processin... |
On the Opportunities and Risks of Foundation Models 5 There had been considerable progress in self-supervised learning dating back to word embeddings [Turian et al. 2010; Mikolov et al. 2013; Pennington et al. 2014], which associated each word with a context-independent vector, provided the basis for a wide range of NL... |
6 Center for Research on Foundation Models (CRFM) Fig. 2. A foundation model can centralize the information from all the data from various modalities. This one model can then be adapted to a wide range of downstream tasks. Homogenization and emergence interact in a potentially unsettling way. Homogenization could poten... |
On the Opportunities and Risks of Foundation Models 7 Fig. 3. Before reasoning about the social impact of foundation models, it is important to understand that they are part of a broader ecosystem that stretches from data creation to deployment. At both ends, we highlight the role of people as the ultimate source of da... |
8 Center for Research on Foundation Models (CRFM) We must thus pause and ask: What is the nature of this social impact? In this report, we address many aspects of this question: the potential exacerbation of social inequities (§5. 1: fairness ), the economic impact due to increased capabilities (§5. 5: economics ), the... |
On the Opportunities and Risks of Foundation Models 9 (3)Training : Training foundation models on these curated datasets8is the celebrated centerpiece in AI research, though it is only one of many stages. (4)Adaptation : In the context of machine learning research, adaptation is about creating a new model based on the ... |
10 Center for Research on Foundation Models (CRFM) Disciplinary diversity. The technology behind foundation models is based on decades of research in machine learning, optimization, NLP, computer vision, and other fields. These technical contri-butions have come from both academia and industrial research labs. However,... |
On the Opportunities and Risks of Foundation Models 11 to publicly release code and datasets, and packages such as Tensor Flow [Abadi et al. 2016] and Py Torch [Paszke et al. 2019] made it much easier for people to collaborate and build off of each other's work. Initiatives like the ML Reproducibility Challenge10as wel... |
12 Center for Research on Foundation Models (CRFM) Another complementary approach is to rely on volunteer computing, in which any of the billions of computing devices (nodes) can connect to a central server and contribute computation. The Folding@home project has successfully implemented this approach for simulating pr... |
On the Opportunities and Risks of Foundation Models 13 2. Capabilities 5. Society 4. Technology Language Vision Robotics Reasoning Interaction Philosophy Inequity Misuse Environment Legality Economics Ethics Modeling Training Adaptation Evaluation Systems Data Security Robustness AI Safety & Alignment Theory Interpreta... |
14 Center for Research on Foundation Models (CRFM) Author Contributions Percy Liang initiated and conceptualized the framing and structure of the overall report. He and Rishi Bommasani worked together to lead the decentralized writing effort and provided guidance on individual sections. Drew A. Hudson created all the f... |
On the Opportunities and Risks of Foundation Models 15 §2. 4: Reasoning and search. Reasoning and search problems such as theorem proving and pro-gram synthesis have been long-standing challenges in AI. The combinatorial search space renders traditional search-based methods intractable. However, humans are known to ope... |
16 Center for Research on Foundation Models (CRFM) matters of data sources and privacy as well as model interpretability and explainability, alongside effective regulation of the use of foundation models for both healthcare and biomedicine. §3. 2: Law. Legal applications require that attorneys read and produce long coh... |
On the Opportunities and Risks of Foundation Models 17 full potential envisioned for foundation models will hinge on modelling advances to fulfill these desiderata. §4. 2: Training. Training objectives mathematically specify how models should learn and acquire capabilities from their training data. The current status q... |
18 Center for Research on Foundation Models (CRFM) §4. 6: Data. Data is the lifeblood of foundation models; the training data of these models largely determines what these capabilities these models can acquire. The centrality of data is not unique to foundation models; recent calls for data-centric AI [Press 2021; Ré 2... |
On the Opportunities and Risks of Foundation Models 19 phase within the foundation model regime pinpoints the insufficiency of existing theory, since these phases correspond to (potentially) completely different tasks and data distributions. Nevertheless, we endeavor that advances in theory to address this discrepancy,... |
20 Center for Research on Foundation Models (CRFM) create and personalize for misuse purposes. For example, disinformation actors may use them to quickly generate collections of articles targeted across different demographic groups (e. g., national-ity, political party, religion, etc. ). While these new capabilities ma... |
On the Opportunities and Risks of Foundation Models 21 2 CAPABILITIES Foundation models acquire capabilities, some that surprisingly emerge from their learning pro-cess, that power downstream applications (§3: applications ). Specifically, we discuss linguistic (§2. 1: language ) and visual (§2. 2: vision ) capabilitie... |
22 Center for Research on Foundation Models (CRFM) 2. 1 Language Authors: Isabel Papadimitriou, Christopher D. Manning 2. 1. 1 The nature of human language. Language is the basis of most human communication and interaction. However, it is not just a means for humans to achieve shared goals: language is central to human... |
On the Opportunities and Risks of Foundation Models 23 Fig. 5. Only a tiny percentage of the world's languages are currently represented in foundation models. There are over 6,000 languages in the world, with estimates varying due to the inherent uncertainty of what constitutes a separate language [Nordhoff and Hammars... |
24 Center for Research on Foundation Models (CRFM) models has also led to a flowering of research for language generation tasks like summarization and dialogue generation. The rise of the foundation model paradigm has begun to play a similar role in spoken language as well as written. Modern automatic speech recognitio... |
On the Opportunities and Risks of Foundation Models 25 2020], and whether their apparent multilingual performance relies more on assimilation [Lauscher et al. 2020; Virtanen et al. 2019; Artetxe et al. 2020]. Multilingual models show better performance in languages that are similar to the highest-resource languages in ... |
26 Center for Research on Foundation Models (CRFM) Fig. 6. Language Acquisition for humans and foundation models. While there are certainly different inductive biases between the human brain and foundation models, the ways that they learn language are also very different. Most saliently, humans interact with a physical... |
On the Opportunities and Risks of Foundation Models 27 sentences, and humans often adapt their grammatical patterns to fit in with different social groups [Rickford et al. 1994]. On the other hand, the linguistic system of foundation models is mostly set by the training data, and is relatively static [Lazaridou et al. ... |
28 Center for Research on Foundation Models (CRFM) 2. 2 Vision Authors: Shyamal Buch, Drew A. Hudson, Frieda Rong, Alex Tamkin, Xikun Zhang, Bohan Wu, Ehsan Adeli, Stefano Ermon, Ranjay Krishna, Juan Carlos Niebles, Jiajun Wu, Li Fei-Fei Fig. 7. By harnessing self-supervision at scale, foundation models for vision have... |
On the Opportunities and Risks of Foundation Models 29 The field of computer vision and the challenges we define draw inspiration in many ways from human perception capabilities. Several classical theories [e. g., Biederman 1972; Mc Clelland and Rumelhart 1981; Marr 1982] suggested that humans may perceive real world s... |
30 Center for Research on Foundation Models (CRFM) of still or moving objects, and include tasks of depth estimation, structure-from-motion, surface normal detection, curvature line and keypoint estimation, to name a few [e. g., Laina et al. 2016; Agarwal et al. 2011; Wang et al. 2015a; Zamir et al. 2018; Ullman 1979].... |
On the Opportunities and Risks of Foundation Models 31 events, and perception for social affordances. Each of these have been goals for fully-supervised systems, but have proven challenging in part due to the difficulty of annotating these tasks at scale. For instance, standard systems for visual-question answering str... |
32 Center for Research on Foundation Models (CRFM) geometric understanding in perception models may provide guidance for ongoing foundation model development [Yi et al. 2019; Bakhtin et al. 2019; Li et al. 2020b]. Indeed, the continued incorporation of multiple modalities (e. g., audio) in foundation models may prove b... |
On the Opportunities and Risks of Foundation Models 33 parallel ones in natural language processing (e. g., metrics like BLEU do not correlate with causal judgements from humans). Having human judgements as part of evaluation may be one route, but incurs significant cost and may not be as scalable [Zhou et al. 2019; Kh... |
34 Center for Research on Foundation Models (CRFM) 2. 3 Robotics Authors: Siddharth Karamcheti, Annie Chen, Suvir Mirchandani, Suraj Nair, Krishnan Srinivasan, Kyle Hsu, Jeannette Bohg, Dorsa Sadigh, Chelsea Finn Fig. 8. Building new types of foundation models for robotics will require massive datasets spanning diverse... |
On the Opportunities and Risks of Foundation Models 35 a description of a task capturing what a user might like the robot to do (e. g., “make breakfast”) — learn a corresponding policy to generate the desired robot actions. While policies can be parameterized in different ways, a common choice is that of a function tha... |
36 Center for Research on Foundation Models (CRFM) Foundation models for task specification. Before robots can learn how to solve tasks in a general purpose way, they must understand what the desired task is: for example, to be useful in a new kitchen, a robot needs to know what we would like it to cook, as well as beh... |
On the Opportunities and Risks of Foundation Models 37 a robotic foundation model could be trained to predict observations of one sensor modality from another or to predict whether two streams of sensory observations are from the same segment of time. These kinds of self-supervised objectives can leverage multi-modal c... |
38 Center for Research on Foundation Models (CRFM) Given the challenging closed-loop nature of learning control, it is possible that collecting datasets of size comparable to those used in vision and language is insufficient for robotics. One exciting option is to additionally leverage external, non-robotic sources of ... |
On the Opportunities and Risks of Foundation Models 39 by such models [Berkenkamp et al. 2017]. As the development and study of these new types of foundation models progresses, solutions to these challenges will be crucial. Conclusion. While the promise of robotic foundation models are many — spanning multiple levels o... |
40 Center for Research on Foundation Models (CRFM) 2. 4 Reasoning and search Authors: Yuhuai Wu, Frieda Rong, Hongyu Ren, Sang Michael Xie, Xuechen Li, Andy Shih, Drew A. Hudson, Omar Khattab Fig. 9. Multimodality can allow foundation models to not only reason with formal symbolic language, but also exploit visual aspe... |
On the Opportunities and Risks of Foundation Models 41 Fig. 10. Left: A reaction route for 1,6-Heptadiene-3,5-dione predicted by machine learning-based drug retrosynthesis planner Ai Zynth Finder [Genheden et al. 2020; Yoshikawa et al. 2021]. Right: A sample proof tree in propositional logic where the formulas outlined... |
42 Center for Research on Foundation Models (CRFM) Jakubuv 2020; Rabe et al. 2021; Li et al. 2021b], synthesizing programs from natural language [Chen et al. 2021f; Ling et al. 2016], repairing, generating and understanding code [Yasunaga and Liang 2021; Lu et al. 2021b; Guo et al. 2020; Svyatkovskiy et al. 2020; Kim e... |
On the Opportunities and Risks of Foundation Models 43 2021; Peng et al. 2021; Li et al. 2021a] allow the model to understand the inner workings behind the high-level code scripts, and further assist downstream tasks. 2. 4. 3 Future challenges in reasoning. Due to the intrinsic difficulty of these problems, high-qualit... |
44 Center for Research on Foundation Models (CRFM) 2. 5 Interaction Authors: Joon Sung Park, Chris Donahue, Mina Lee, Siddharth Karamcheti, Dorsa Sadigh, Michael S. Bernstein Fig. 11. Foundation models will bring significant opportunities to developers by lowering the difficulty threshold for building AI-infused applic... |
On the Opportunities and Risks of Foundation Models 45 compounded by the fact that AI responses can be unpredictable, and models can produce a vast generative output space, making it difficult for people to build effective mental models of their performance. There has already been some progress on tackling these challe... |
46 Center for Research on Foundation Models (CRFM) draw on such associations in their behavior without the community being aware. This will place a large burden on the groups utilizing foundation models to monitor their models' behavior, and to the extent possible, adapt them to act in appropriate ways. While the desig... |
On the Opportunities and Risks of Foundation Models 47 model designed to identify problematic comments for one online community might work well for that community but will fail in others whose norms and cultures may differ significantly (e. g., NSFW communities on Reddit might be more tolerant of certain content, while... |
48 Center for Research on Foundation Models (CRFM) 2. 6 Philosophy of understanding Authors: Christopher Potts, Thomas Icard, Eva Portelance, Dallas Card, Kaitlyn Zhou, John Etchemendy What could a foundation model come to understand about the data it is trained on? An answer to this question would be extremely informa... |
On the Opportunities and Risks of Foundation Models 49 sequence of pixel values and a database entry. These associations might reflect important aspects of the world we inhabit and the language we use to talk about it. 2. 6. 2 What is at stake? Before considering analyses of what understanding is, it is worth reflectin... |
50 Center for Research on Foundation Models (CRFM) Metaphysics of understanding. Philosophy of language offers a number of alternatives for what it is to understand natural language. 25Simplifying the landscape for the sake of brevity, the following three broad classes of views all have connections with research lines ... |
On the Opportunities and Risks of Foundation Models 51 referentialism, there is still a further question of how these proxies relate to the actual world, but the same question arises for human language users as well. Bender and Koller [2020] give an interesting argument that combines referentialism with prag-matism. Th... |
52 Center for Research on Foundation Models (CRFM) If we take internalism or referentialism as the ultimate target-our gold standard for what understanding is-then behavioral tests will always be at best imperfect as a means of assessing whether understanding has been achieved. The imperfections are two-fold. First, be... |
On the Opportunities and Risks of Foundation Models 53 3 APPLICATIONS The capabilities (§2: capabilities ) of foundation models indicate that they have the potential to transform various sectors and industries, extending the role AI plays in society (§5: society ). Among the myriad applications where foundation models ... |
54 Center for Research on Foundation Models (CRFM) 3. 1 Healthcare and biomedicine Authors: Michihiro Yasunaga, Jing Huang, Camilo Ruiz, Yuhui Zhang, Giray Ogut, Saahil Jain, William Wang, Yusuf Roohani, Hongyu Ren, Antoine Bosselut, Ehsan Adeli, Jure Leskovec, Russ Altman Fig. 12. Foundation models in healthcare and b... |
On the Opportunities and Risks of Foundation Models 55 answering app used by patients [Klasnja and Pratt 2012; Zhu et al. 2019; Daniel et al. 2019; Liu et al. 2020a], clinical trial matching system [Ni et al. 2015; Harrer et al. 2019; Beck et al. 2020] accessed by researchers and patients; Figure 12 right). This way, f... |
56 Center for Research on Foundation Models (CRFM) we note that the interface must guarantee factual accuracy to ensure public trust in medical advice [Kreps and Kriner 2020] (see §3. 1. 3: healthcare-biomed-challenge ). 3. 1. 2 Opportunities in biomedicine. Foundation models may facilitate biomedical research such as ... |
On the Opportunities and Risks of Foundation Models 57 and Varmus 2015; Ashley 2016]. For instance, given a set of drugs and a patient genome, foundation models may help predict which drug is likeliest to treat the patient with minimal side effects [Whirl-Carrillo et al. 2012; Tatonetti et al. 2012; Gerstung et al. 201... |
58 Center for Research on Foundation Models (CRFM) ensure that the training and evaluation data for foundation models is sufficiently representative of different sexes, races, ethnicities and socioeconomic backgrounds; an area where medical datasets and clinical trials have had a long history of bias [Martinez-Martin e... |
On the Opportunities and Risks of Foundation Models 59 3. 2 Law Authors: Peter Henderson, Lucia Zheng, Jenny Hong, Neel Guha, Mark Krass, Julian Nyarko, Daniel E. Ho Fig. 13. An example of various steps of a civil case in the United States and where foundation models might help. At each stage different modalities might... |
60 Center for Research on Foundation Models (CRFM) can pose insurmountable obstacles to the successful deployment of traditional models. In contrast, their flexibility and capability to learn from few examples suggest that foundation models could be uniquely positioned to address the aforementioned challenges. Througho... |
On the Opportunities and Risks of Foundation Models 61 access to justice by providing information tailored to a client's particular needs [Cabral et al. 2012; Brescia et al. 2014; Queudot et al. 2020; Westermann et al. 2019]. Once a client speaks with an attorney, prior to civil litigation, the attorney may seek to avo... |
62 Center for Research on Foundation Models (CRFM) the current context from judges' prior published opinions. In the courtroom, foundation models might be used to examine audio and video of courtroom proceedings to determine if outcomes were biased against the defendant because of their race or dialect. 35 Once the tri... |
On the Opportunities and Risks of Foundation Models 63 accessibility of government services: labels are scarce, resources are constrained, and contexts are constantly shifting. As such, the adaptability and flexibility of foundation models are often required to improve efficiency and performance. To give an illustrativ... |
64 Center for Research on Foundation Models (CRFM) Fig. 14. An extract from a fictional brief written by one of the authors of this work. The prototypical form that law students are instructed to write a brief involves: (1) introducing the argument; (2) stating the legal rule in a persuasive manner; (3) applying the le... |
On the Opportunities and Risks of Foundation Models 65 These challenges can further be illustrated through a real exchange with GPT-3, demonstrat-ing that current models are unable to perform even comparatively simple tasks involving legal reasoning. Legal Reasoning with GPT-3. “Liquidated damages” are a form of moneta... |
66 Center for Research on Foundation Models (CRFM) Adaptation. Some gains have been observed from domain-adaptive pretraining on unlabeled legal corpora. These gains appear to be most pronounced when the pretraining corpus is highly relevant to the downstream task and labeled training data is limited (a setting which i... |
On the Opportunities and Risks of Foundation Models 67 3. 3 Education Authors: Ali Malik, Dorottya Demszky, Pang Wei Koh, Moussa Doumbouya, Drew A. Hudson, Allen Nie, Hamed Nilforoshan, Alex Tamkin, Emma Brunskill, Noah Goodman, Chris Piech Fig. 15. Foundation models in education could be trained on multiple data sourc... |
68 Center for Research on Foundation Models (CRFM) 2021], or even create personalised and adaptive learning experiences that tailor the learning process to individual students' needs and dispositions [Connor 2019]. Despite this potential, building technical solutions to effectively scale inclusively and quality of educ... |
On the Opportunities and Risks of Foundation Models 69 Fig. 16. The figure illustrates a system that embeds signals from various modalities (image, speech, sign, text) and languages into a universal feature space. Such a feature space allows ideas to be linked across modalities and languages. Pedagogically relevant lin... |
70 Center for Research on Foundation Models (CRFM) 3. 3. 2 Foundation models of student thought. When building AI tools for inclusive, and joyful education, there are many tasks where foundation models could be useful. Many of those tasks require us to first understand the learners whom we are trying to help, especiall... |
On the Opportunities and Risks of Foundation Models 71 While difficult, training an AI system to notice is an achievable goal. Across classrooms, and across learning tasks in a given domain, there are generalizable patterns in how students arrive at their answers. The labeled data that can directly be used for this ada... |
72 Center for Research on Foundation Models (CRFM) Beyond sound pedagogical techniques and instructional language, how might foundation models provide even more insightful forms of instruction? §2. 1: language of this paper highlights the fact that remarkably complex language can be acquired by babies in a short amount... |
On the Opportunities and Risks of Foundation Models 73 4 TECHNOLOGY The technological foundations of foundation models give rise to the capabilities (§2: capabilities ) that determine their potential. To understand the technology used in development, we consider the data (§4. 6: data ), model architectures (§4. 1: mode... |
74 Center for Research on Foundation Models (CRFM) 4. 1 Modeling Authors: Drew A. Hudson, Antoine Bosselut, Alex Tamkin, Omar Khattab, Jared Quincy Davis, Jiaxuan You, Trevor Gale Fig. 17. The five key properties of a foundation model: expressivity — to flexibly capture and represent rich information; scalability — to ... |
On the Opportunities and Risks of Foundation Models 75 4. 1. 1 Expressivity. Expressivity concerns with the theoretical and practical capacity of a network to model the data distribution it is trained over and represent it in a flexible manner. Prior works have proposed formal expressivity measures to characterize the ... |
76 Center for Research on Foundation Models (CRFM) General-Purpose Computation. A final notable advantage of attention over prior architectures stems from its stronger generality, where it is not strongly tied to a particular task or domain, as is the case for the local receptive field of convolution or the sequential ... |
On the Opportunities and Risks of Foundation Models 77 Optimization. Specifically, foundation models should both be: (1) easy-to-train (§4. 2: training ), by being resilient to noise or imperfections in the data, and robust against instabilities like vanishing [Helfrich et al. 2018; Glorot and Bengio 2010] or exploding... |
78 Center for Research on Foundation Models (CRFM) upwards and models are provided with the opportunity to base their learning less on structural priors and more on the data itself, general approaches that maintain only a handful of broad general assumptions prove in fact a lot more successful than task-specific altern... |
On the Opportunities and Risks of Foundation Models 79 using key-value structures [Miller et al. 2016] for accessing external memories has been shown to be very effective for modeling long-term dependencies [Henaff et al. 2016; Bosselut et al. 2018; Lample et al. 2019]. Transformers, the celebrated architecture underly... |
80 Center for Research on Foundation Models (CRFM) Computation. Models such as Module Networks [Andreas et al. 2016] and Mixture-of-Experts [Shazeer et al. 2017] go further along this direction, exhibiting not only structural modularity, but also compositional computation, supported by the specialization of sub-network... |
On the Opportunities and Risks of Foundation Models 81 4. 2 Training Authors: Alex Tamkin Training objectives are mathematical functions describing how to transform a model architecture and large amount of broad data into a foundation model. For example, GPT-3 was trained with a language modeling objective, which rewar... |
82 Center for Research on Foundation Models (CRFM) sizes, and compute [Hestness et al. 2017; Kaplan et al. 2020], a striking phenomenon which enables model developers to make choices based on clearer trends instead of more costly random searches. 4. 2. 2 Design trade-offs in current SSL methods. Current self-supervised... |
On the Opportunities and Risks of Foundation Models 83 during adaptation53(see §2. 5: interaction and §4. 3: adaptation ), and future models may enable an even richer set of interactions. 54 While generative training approaches have their benefits, several discriminative approaches have also recently gained traction. T... |
84 Center for Research on Foundation Models (CRFM) quality and availability of the data influence the training signal,56but the training algorithm itself could adaptively seek out or construct richer training examples as the model improves to accelerate learning [Tamkin et al. 2021c]. Goal-directed training of foundati... |
On the Opportunities and Risks of Foundation Models 85 4. 3 Adaptation Authors: Xiang Lisa Li*, Eric Mitchell*, Sang Michael Xie, Xuechen Li, Tatsunori Hashimoto Fig. 18. During adaptation, a foundation model is converted into an adapted model (bottom row) in order to reflect updated information, desired behaviors, or ... |
86 Center for Research on Foundation Models (CRFM) Factor 1: Compute budget. For foundation models with billions or trillions of parameters, fine-tuning all model parameters may demand prohibitively large memory. Also, separately fine-tuning for many tasks can incur unacceptable storage costs. There are many works that... |
On the Opportunities and Risks of Foundation Models 87 of a foundation model's parameters for fine-tuning might result in ethical concerns. Moreover, most users do not have enough compute resources to exploit their full access. For example, the memory requirements of foundation models might preclude their direct fine-t... |
88 Center for Research on Foundation Models (CRFM) Temporal adaptation. Ideally, foundation models store knowledge that closely represents the state of the world, independent of modality. However, the world is constantly changing; new heads of state are elected, clothing styles change, social norms and beliefs shift (§... |
On the Opportunities and Risks of Foundation Models 89 neighborhood around a single input, without changing the model's behavior for unrelated inputs. For example, when a foundation model produces an especially problematic mistranslation for a particular input phrase and target language, it is desirable to correct this... |
90 Center for Research on Foundation Models (CRFM) objectives are likely to be necessary in order to do so. For example, while memory mechanisms have long been speculated as key to successful continual learning [French 1999] and have shown some promise for foundation models [Lewis et al. 2020b; Guu et al. 2020; Borgeau... |
On the Opportunities and Risks of Foundation Models 91 4. 4 Evaluation Authors: Rishi Bommasani, Kawin Ethayarajh, Omar Khattab 4. 4. 1 Introduction. Evaluation gives context to machine learning models: it serves as a means for (1) tracking progress — how do we we measure the performance of models and how do we design ... |
92 Center for Research on Foundation Models (CRFM) One approach is to evaluate foundation models in terms of the task associated with the training objective. For example, a language model like GPT-3, which was trained by predicting the next word given the preceding context, may be evaluated based on the probabilities i... |
On the Opportunities and Risks of Foundation Models 93 articulating the presence and intensity of capabilities, skills, and biases identifies concrete areas for improvement (progress), elucidates the current potential (understanding), and expresses relevant aspects efficiently (documentation). Such an approach also is ... |
94 Center for Research on Foundation Models (CRFM) measures (i. e., their ability to (statistically) predicted related downstream outcomes) may prove to be a central criterion. 4. 4. 3 Extrinsic evaluation and adaptation. Evaluating task-specific models has historically involved reporting the performance (generally mea... |
On the Opportunities and Risks of Foundation Models 95 contrast, by accounting for the resources and access requirements involved in adaptation, evaluation better enables research to identify which adaptation methods or processes make best use of the resources provided, i. e., signal is offered not just for the specifi... |
96 Center for Research on Foundation Models (CRFM) et al. 2019a], but significant concerns have been raised of whether this yields more general im-provements [e. g., Linzen 2020; Bowman and Dahl 2021]. 63As is true for all machine learning models, it is rarely the case that the desiderata for foundation models and thei... |
On the Opportunities and Risks of Foundation Models 97 4. 5 Systems Authors: Deepak Narayanan, Trevor Gale, Keshav Santhanam, Omar Khattab, Tianyi Zhang, Matei Zaharia Fig. 19. Plot showing the growth of number of parameters and number of training operations (FLOPs) of transformer-based language models (shown in blue),... |
98 Center for Research on Foundation Models (CRFM) parallelism [Huang et al. 2019; Narayanan et al. 2019] that limit communication while keeping devices busy, state-sharding optimizers to reduce memory usage [Rajbhandari et al. 2020], just-in-time (JIT) compilers to optimize the computation graph [Py Torch 2021], and o... |
On the Opportunities and Risks of Foundation Models 99 and efficient, but demand functionality not readily supported by popular ML frameworks and nearest-neighbor indexes such as FAISS [Johnson et al. 2019]. 4. 5. 2 Automated optimization. Another important challenge in systems is to automate the application of optimiz... |
100 Center for Research on Foundation Models (CRFM) training foundation models such as Hugging Face Transformers [Wolf et al. 2020], do not allow for fine-grained lineage information across entire model instances to be specified. Building and maintaining a cluster of thousands of accelerators also requires tremendous e... |
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