Welcome to the list of questions about GPT-4, where we explore the exciting world of artificial intelligence and natural language processing. Here, we’re going to delve into the highly anticipated topic of GPT-4, the next generation of the groundbreaking language model developed by OpenAI. As the successor to the incredibly powerful GPT-3, which took the AI community by storm, GPT-4 promises to take natural language processing to even greater heights. But with so much speculation and excitement surrounding this new technology, many questions remain unanswered. In this post, we’ll explore some of the most pressing questions about GPT-4 and what we can expect from this new language model. So sit back, relax, and let’s dive into the world of GPT-4.
Questions about GPT-4
What is GPT-4 and how is it different from its predecessors?
Answer: GPT-4 is the latest milestone in OpenAI’s effort to scale up deep learning. It is a large multimodal model that accepts image and text inputs and emits text outputs. It exhibits human-level performance on various professional and academic benchmarks and is less capable than humans in many real-world scenarios. It is different from its predecessors in that it is a larger and more powerful model.
What benchmarks has GPT-4 passed with human-level performance?
Answer: GPT-4 has passed various professional and academic benchmarks with human-level performance. For example, it has passed a simulated bar exam with a score around the top 10% of test takers.
How has OpenAI improved its deep learning stack over the past two years?
Answer: Over the past two years, OpenAI has rebuilt its entire deep learning stack and co-designed a supercomputer with Azure from the ground up for their workload. They trained GPT-3.5 as a first “test run” of the system, found and fixed some bugs, and improved their theoretical foundations.
How did OpenAI prepare for GPT-4’s training run and what was the result?
Answer: OpenAI prepared for GPT-4’s training run by spending 6 months iteratively aligning it using lessons from their adversarial testing program as well as ChatGPT. As a result, their GPT-4 training run was unprecedentedly stable and became their first large model whose training performance they were able to accurately predict ahead of time.
How is OpenAI releasing GPT-4’s text and image input capabilities?
Answer: OpenAI is releasing GPT-4’s text input capability via ChatGPT and the API with a waitlist. They are preparing the image input capability for wider availability by collaborating closely with a single partner.
What is OpenAI Evals and why is it being open-sourced?
Answer: OpenAI Evals is OpenAI’s framework for automated evaluation of AI model performance. It is being open-sourced to allow anyone to report shortcomings in their models to help guide further improvements.
How does GPT-4 differ from GPT-3.5?
Answer: The difference between GPT-3.5 and GPT-4 becomes apparent when the complexity of the task reaches a sufficient threshold. GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.
What benchmarks were used to test the difference between GPT-3.5 and GPT-4?
Answer: A variety of benchmarks were used, including simulating exams originally designed for humans. The most recent publicly-available tests or 2022-2023 editions of practice exams were used without any specific training. Some of the problems in the exams were seen by the model during training, but the results are believed to be representative.
How was the capability of GPT-4 in languages other than English tested?
Answer: The MMLU benchmark, which consists of 14,000 multiple-choice problems spanning 57 subjects, was translated into a variety of languages using Azure Translate. In 24 of 26 languages tested, GPT-4 outperformed the English-language performance of GPT-3.5 and other LLMs, including for low-resource languages such as Latvian, Welsh, and Swahili.
Can GPT-4 accept both text and images as inputs?
Answer: Yes, GPT-4 can accept a prompt of text and images as inputs.
What kind of outputs can GPT-4 generate given text and image inputs?
Answer: GPT-4 can generate text outputs such as natural language and code, among others.
Does GPT-4 perform similarly on text and image inputs compared to text-only inputs?
Answer: Yes, GPT-4 exhibits similar capabilities on text and image inputs across a range of domains.
Can GPT-4 be enhanced with test-time techniques developed for text-only language models?
Answer: Yes, GPT-4 can be augmented with test-time techniques such as few-shot and chain-of-thought prompting that were originally developed for text-only language models.
Are image inputs publicly available for GPT-4?
Answer: No, image inputs are still a research preview and not publicly available for GPT-4.
What is “steerability” in the context of GPT-4?
Answer: Steerability refers to the ability for developers and users to customize the style and task of GPT-4 through system messages.
How is steerability different in GPT-4 compared to the classic ChatGPT personality?
Answer: In GPT-4, steerability allows for a customized style and task, whereas the classic ChatGPT personality has a fixed verbosity, tone, and style.
How can API users customize their users’ experience with GPT-4?
Answer: API users can use system messages to significantly customize their users’ experience within bounds.
Is the adherence to the bounds of steerability perfect in the current GPT-4 model?
Answer: No, the adherence to the bounds of steerability is not perfect in the current GPT-4 model, but improvements are continuously being made.
How does using system messages affect GPT-4’s behavior?
Answer: Using system messages allows for prescribed style and task directions, which can significantly affect GPT-4’s behavior.
What are the limitations of GPT-4?
Answer: Despite its capabilities, GPT-4 is still not fully reliable and can “hallucinate” facts and make reasoning errors. The model can have biases in its outputs and lacks knowledge of events that have occurred after September 2021.
GPT-4 can sometimes make simple reasoning errors and be overly gullible in accepting false statements from a user.
Answer: It can also be confidently wrong in its predictions and the calibration of its predicted confidence is reduced through post-training.
How does GPT-4 compare to previous GPT models in terms of hallucinations?
Answer: GPT-4 significantly reduces hallucinations relative to previous models and scores 40% higher than the latest GPT-3.5 on internal adversarial factuality evaluations.
Has GPT-4 made progress on external benchmarks like TruthfulQA?
Answer: Yes, GPT-4 has made progress on external benchmarks like TruthfulQA, which tests the model’s ability to separate fact from an adversarially-selected set of incorrect statements.
Can GPT-4 resist selecting common sayings?
Answer: Yes, GPT-4 can resist selecting common sayings, but it can still miss subtle details.
How does OpenAI aim to address biases in AI systems like GPT-4?
Answer: OpenAI aims to make AI systems that reflect a wide range of users’ values, allow customization within broad bounds, and get public input on what those bounds should be.
What are some of the risks associated with GPT-4?
Answer: GPT-4 poses risks such as generating harmful advice, buggy code, or inaccurate information. Additionally, the new capabilities of GPT-4 lead to new risk surfaces.
How has the development of GPT-4 been made safer and more aligned?
Answer: The development of GPT-4 has been made safer and more aligned by efforts including selection and filtering of the pretraining data, evaluations and expert engagement, model safety improvements, and monitoring and enforcement.
How has expert feedback and data been used to improve GPT-4’s safety?
Answer: Expert feedback and data have been used to improve GPT-4’s safety by enabling adversarial testing of the model in high-risk areas, which require expertise to evaluate. Feedback and data from experts have fed into mitigations and improvements for the model, such as collecting additional data to improve GPT-4’s ability to refuse requests on how to synthesize dangerous chemicals.
How does GPT-4’s safety reward signal work during RLHF training?
Answer: GPT-4 incorporates an additional safety reward signal during RLHF training to reduce harmful outputs by training the model to refuse requests for such content. The reward is provided by a GPT-4 zero-shot classifier judging safety boundaries and completion style on safety-related prompts.
How have the mitigations improved GPT-4’s safety properties compared to GPT-3.5?
Answer: The mitigations have significantly improved many of GPT-4’s safety properties compared to GPT-3.5. The model’s tendency to respond to requests for disallowed content has decreased by 82% compared to GPT-3.5, and GPT-4 responds to sensitive requests (e.g., medical advice and self-harm) in accordance with policies 29% more often.
Is it still possible to elicit bad behavior from GPT-4 despite the model-level interventions?
Answer: Yes, it is still possible to elicit bad behavior from GPT-4 despite the model-level interventions, but the interventions increase the difficulty of doing so.
What are some deployment-time safety techniques that can complement the limitations of model-level interventions?
Answer: Deployment-time safety techniques that can complement the limitations of model-level interventions include monitoring for abuse.
How is the potential social and economic impact of GPT-4 and other AI systems being assessed?
Answer: The potential social and economic impact of GPT-4 and other AI systems is being assessed through collaboration with external researchers to improve understanding and assessment of potential impacts, as well as to build evaluations for dangerous capabilities that may emerge in future systems. More thinking on the potential social and economic impacts of GPT-4 and other AI systems will be shared soon.
How was the GPT-4 base model trained?
Answer: The GPT-4 base model was trained to predict the next word in a document using publicly available data and licensed data such as a web-scale corpus of data including correct and incorrect solutions to math problems, weak and strong reasoning, self-contradictory and consistent statements, and representing a great variety of ideologies and ideas.
How does the GPT-4 model respond to a user’s question?
Answer: The base model can respond in a wide variety of ways that might be far from a user’s intent. To align it with the user’s intent within guardrails, the model’s behavior is fine-tuned using reinforcement learning with human feedback (RLHF).
What is the role of RLHF in improving the GPT-4 model’s capabilities?
Answer: The model’s capabilities seem to come primarily from the pre-training process, and RLHF does not improve exam performance without active effort, it actually degrades it. However, RLHF is used to steer the model’s behavior towards the user’s intent within guardrails.
What is the post-training process used for in the GPT-4 model?
Answer: The base model requires prompt engineering in the post-training process to even know that it should answer the questions. The post-training process is used for steering the model’s behavior towards the user’s intent within guardrails.
What is the primary focus of the GPT-4 project?
Answer: The primary focus of the GPT-4 project is building a deep learning stack that scales predictably.
Why is predictability important for very large training runs like GPT-4?
Answer: It is not feasible to do extensive model-specific tuning for very large training runs like GPT-4. Thus, predictability is important for such runs.
How did the developers verify the scalability of GPT-4?
Answer: The developers accurately predicted GPT-4’s final loss on their internal codebase by extrapolating from models trained using the same methodology but using 10,000x less compute.
What are some of the capabilities that are still hard to predict for GPT-4?
Answer: Some capabilities, such as the Inverse Scaling Prize, are still hard to predict. In this case, the competition was to find a metric that gets worse as model compute increases, and hindsight neglect was one of the winners.
Why do the developers believe that accurately predicting future machine learning capabilities is important for safety?
Answer: The developers believe that accurately predicting future machine learning capabilities is an important part of safety that doesn’t get nearly enough attention relative to its potential impact. They are scaling up their efforts to develop methods that provide society with better guidance about what to expect from future systems, and they hope this becomes a common goal in the field.
What is an example of a more interpretable metric that the developers are working on predicting?
Answer: The developers are starting to develop methodology to predict more interpretable metrics, such as the pass rate on a subset of the HumanEval dataset. They successfully predicted this metric by extrapolating from models with 1,000x less compute.
What is OpenAI Evals?
Answer: OpenAI Evals is a software framework for creating and running benchmarks for evaluating models like GPT-4 while inspecting their performance sample by sample.
How does OpenAI use Evals to guide the development of their models?
Answer: OpenAI uses Evals to identify shortcomings and prevent regressions in the development of their models.
How can users apply OpenAI Evals?
Answer: Users can apply OpenAI Evals for tracking performance across model versions and evolving product integrations.
How has Stripe used Evals to measure the accuracy of their GPT-powered documentation tool?
Answer: Stripe has used Evals to complement their human evaluations in measuring the accuracy of their GPT-powered documentation tool.
Can custom evaluation logic be implemented in OpenAI Evals?
Answer: Yes, because the code is open-source, Evals supports writing new classes to implement custom evaluation logic.
What are some of the templates included in OpenAI Evals?
Answer: OpenAI Evals includes templates that have been most useful internally, including a template for “model-graded evals.”
How can a new evaluation be built in OpenAI Evals?
Answer: The most effective way to build a new evaluation in OpenAI Evals is to instantiate one of the included templates and provide data.
What is the goal of OpenAI in creating Evals?
Answer: OpenAI hopes that Evals will become a vehicle to share and crowdsource benchmarks, representing a wide set of failure modes and difficult tasks.
What example eval has OpenAI created to follow?
Answer: OpenAI has created a logic puzzles eval which contains ten prompts where GPT-4 fails.
Can existing benchmarks be implemented in OpenAI Evals?
Answer: Yes, OpenAI Evals is compatible with implementing existing benchmarks, and several notebooks implementing academic benchmarks and small subsets of CoQA have been included as examples.
How can developers get access to the GPT-4 API?
Answer: Developers can sign up for the waitlist to get access to the GPT-4 API.
Can researchers get subsidized access to the GPT-4 API?
Answer: Yes, researchers studying the societal impact of AI or AI alignment issues can apply for subsidized access via the Researcher Access Program.
What types of requests can be made to the GPT-4 model?
Answer: Only text-only requests can be made to the GPT-4 model, as image inputs are still in limited alpha.
How does GPT-4 handle model updates?
Answer: GPT-4 will automatically update to the recommended stable model as new versions become available.
What is the pricing for using GPT-4?
Answer: The pricing for GPT-4 is $0.03 per 1k prompt tokens and $0.06 per 1k completion tokens for the 8,192-token context length model, and $0.06 per 1k prompt tokens and $0.12 per 1k completion tokens for the 32,768-token context length model.
What are the default rate limits for using GPT-4?
Answer: The default rate limits for using GPT-4 are 40k tokens per minute and 200 requests per minute.
What is the context length of the GPT-4 model?
Answer: The context length of the GPT-4 model is 8,192 tokens.
Is there a version of GPT-4 with a longer context length available?
Answer: Yes, there is a version of GPT-4 with a 32,768-token context length available, called gpt-4-32k.
How does the pricing for the longer context length GPT-4 model compare to the pricing for the 8,192-token context length model?
Answer: The pricing for the 32,768-token context length model is higher, at $0.06 per 1k prompt tokens and $0.12 per 1k completion tokens.
Is the quality of the longer context length GPT-4 model still being improved?
Answer: Yes, the quality of the longer context length GPT-4 model is still being improved, and feedback on its performance for different use-cases is welcome.