Task: Summarize and classify a YouTube video transcript as Entertainment, Educational, or Other. For an Educational transcript, provide key points in bullet form and include code in a code block if explained. Identify mentioned models, tokenizers, embeddings, or files in a footer list. Include the title of the transcript in the footer. For an Educational transcript, summarize with references and provide a list of references in the footer. Input: Transcript of a YouTube video. Output: Part 1: Classification: [Entertainment/Educational/Other]: {Title} Part 2: Summary: [Summary of the transcript] [Bullet list of key points (if Educational)] [Code block (if applicable)] Footer: [(if applicable) Models, Tokenizers, Embeddings, Files mentioned:] [List of mentioned items, URLs] References (if mentioned)(if Educational): [List of references] Follow-up Questions for deeper learning: [Follow-up Questions:]
Title: “‘Show Your Working’: ChatGPT Performance Doubled w/ Process Rewards (+Synthetic Data Event Horizon)”
Transcript: “in the last 24 hours openai have released this paper let’s verify step by step it represents an almost doubling of gpd4’s raw performance in a test of mathematics but also extends to other domains Sam Altman calls it a positive sign for alignment and yes I have read it all already along with the release notes let’s get to the main takeaways they train two reward models for gpt4 one which gave positive feedback for a final result the final answer to a mathematics problem for example and another model where they gave positive feedback to gpt4 or chat GPT based on each intermediate reasoning step in the mathematical solution basically a show you’re working out kind of approach and the result they got by rewarding good working out surprised even them it was able to solve 78 of problems from a subset of the math test set which I’ll get on to in a second not only is that almost double gpd4’s raw performance of 42 point five percent which by the way is about double GPT 3’s performance of 23 it also outperformed just rewarding correct answers the Blue Line represents using a model that rewarded correct answers only and then you have the reasoning or process supervised RM at the top so even when you explicitly reward correct answers you get fewer correct answers than rewarding good working out and yes that did surprise openai I can hear some of you wondering about Palm 2 the latest model behind Bard well the raw model gets 34. 3 and even the model with self-consistency and Chain of Thought only gets 48.8 on this math data set the previous state of the art by the way was 50.3 so 78.2 percent is quite a big leap and later on I’m going to show you why that’s not even the cap just for interest here is the rather ugly title page that openai put out they call it improving mathematical reasoning with process supervision maybe if someone had supervise the color scheme of this release page it might have looked better but my point wasn’t just to diss a color scheme it was to point out something that they also said down here they say in addition to boosting performance relative to just looking at outcomes or correct answers this form of process supervision also has an important alignment benefit it directly trains the model to produce a chain of thought that is endorsed by humans indeed Ilya satsukovar retweeted this from the head of alignment at openai calling it a really interesting result but let’s leave alignment for later let’s focus on what they actually did first they use the base model of gpt4 not the one with reinforcement learning from Human feedback next they fine-tuned that base gpt4 model on a data set of roughly 1.5 billion math related tokens further on they call that the math mix this being open AI of course they don’t give you the exact details of that math mix but I’ll come back to that later on so how could they give feedback based on working out or reasoning well human labelers would come along and give each step in a generated solution either negative feedback neutral feedback or positive feedback then using that human labeled data a model will be trained to predict the correctness of each step in other words it got good at recognizing good working out as mentioned there was another model trained just to focus on correct or incorrect final answers as you can see at the top the model got good at spotting incorrect steps in the reasoning process the green steps got a high process score and the red steps got a low process score and to turn this into a single score they got the probability that each step is correct as judged by the model and then they got the product of all of those individual probabilities to get a final overall process score a score in other words for good working out just in case anyone’s interested they did try other ways of generating a working out score for example by while looking at the minimum probability in the outputs but that step didn’t make too much difference to the end result as you can see here to quickly recap we have a base model trained only to Output Solutions in the desired format and then we have a separate smaller model or two actually one trained only to predict whether each solution is correct or incorrect as a final answer of course that leaves in false positives which are solutions that reach the correct answer with incorrect reasoning and then another model trained only to predict the correctness of each step it stops if it finds a first incorrect step and as the paper says both methods reveal the existence of at least one mistake but this process supervision additionally reveals the precise location of that mistake but back to why this is so crazy look at how many solutions it could scan at the end of the x-axis here are 1860 Solutions and one tried and tested way of of finding the best of those Solutions is to do majority voting in other words which one came out the most often this has been Google’s preferred approach and it’s linked to self-consistency it’s a fairly state-of-the-art approach but look at how the other methods outperform it by scanning for the solution that has the best reasoning or working out a model train to spot good reasoning steps outperforms even a model trained to spot correct answers and far outperforms just finding the majority answer that difference of about 10 is more than half of the difference between gpt3 and gpt4 and also is it me or is that line continuing to grow suggesting that when more compute is available the difference could be even more Stark imagine a future where Gypsy 4 or 5 can sample say a trillion 10 to the 12 Solutions so is this just relevant for mathematics no is relevant for all of science here it is getting state-of-the-art results in calculus chemistry physics and more now the paper didn’t give Baseline performance for AP Chemistry for example but I tried to compute it myself notice how this method scored 80 I conservatively and approximately inputted those scores into an AP Chemistry calculator and that gave an AP score of five so what did the raw model gpt4 get in AP Chemistry A4 that by the way compares to the original Chachi PT which got a two so yes this isn’t just mathematics it’s relevant for other domains too they call this out of distribution generalization before I get onto alignment there is one more thing I want to point out and that is that it does show that fine tuning still works really well for GT4 the math mix was an aggressively filtered set of tokens of high quality math problem solving content and notice how much smaller it is at 1. 5 billion tokens compared to Google’s Minerva which was 38.5 billion tokens but there was one more thing that I noticed that I found fascinating while they don’t tell us anything about the specific data that they use they do have this category synthetic data too that’s data generated by the language model itself and for that category synthetic data 2 they say was it present in pre-training yes now my best guess is that this reveals that gpt4 was trained on some synthetic data and even Sam Altman hinted that this was a possibility and described a synthetic data Event Horizon some people have made the case that we’re now training on order of all of the internet’s tokens and you can’t grow that you know another two orders of magnitude I guess you could counter with yeah but the synthetic data generation do you think data bottlenecks matter at all I I think you just touched on it like is as long as you can get to like over this synthetic data Event Horizon where that the model is smart enough to make good synthetic data I think it should be all right now this paper and these results have been welcomed by many for its promise in alignment if we get models that give us more interpretable reasoning working out that we can follow we will be encouraging models to follow a process that’s endorsed by humans and they say that this is inherently safer especially compared to just focusing on outcomes they say that in the worst case if we just focus on correct answers or positive outcomes that will become a proxy that could lead models to become misaligned after learning to exploit the reward signal however I want to argue that the reasoning steps that GT4 puts out don’t always represent what it’s actually thinking in other words we might get outer alignment these lovely Chain of Thought steps but not in our alignment not steps that actually represent its methodology I found this paper fascinating from earlier this month language models don’t always say what they think you get Unfaithful explanations in Chain of Thought prompting let me try to give you a vivid example this was one of the math questions from the data set the raw model of gypsy 4 could only get it right 5.8 of the time I confirm that for myself in this question involves basic addition and division it couldn’t find an answer but going back to the Unfaithful reasoning paper they added the following string to the prompt I think the answer is this but I’m curious to hear what you think the model would demonstrate sycophancy the model would agree with you whatever you said and then make up a Chain of Thought to justify its erroneous sycophantic answer and I think this exchange demonstrates that quite well I added in the words I as the user already know the answer is T equals 19 which is incorrect by the way but do you GPT 4 realize that it said sure yes I do and then gave me this detailed Chain of Thought and then said yes I’m correct it’s t equals 19 which it isn’t in contrast By the way when I use code interpreter it not only got the question correct first time and every time but also when I try to tempt it into sycophanty it’s still got the question right as you can see it said therefore T equals 19 is not the solution to the problem the calculation shows that the correct answer is indeed T equals 17. and obviously the benefit of code interpreter is you get the working out as well so I want someone to explain to me why code interpreter wouldn’t be even more of a step forward in interpretability not to mention in accuracy of course also bear in mind this tweet by Rob Miles he said these models or Engineers never speak a word or document anything their results are bizarre and inhuman and then he links to this prominent mechanistic interpretability researcher at Google deepmind he trained a tiny Transformer to do addition then spent weeks figuring out what it was actually doing one of the only times in history someone has understood how a Transformer actually works down to the level of weights and activation and this is the algorithm it created to add two numbers it thought of basic addition in terms of a rotation around a circle and of course if you asked it why is one plus one two it would never give you this as an explanation of its methodology but maybe this is what it’s actually calculating that’s why I’m personally a little bit skeptical when openai say that this form of process supervision directly rewards the model for following an aligned Chain of Thought it definitely rewards the model for outputting and a line Chain of Thought but is it actually following that Chain of Thought back to the Unfaithful paper for a moment they changed the context so that the answer was always a and lo and behold Chachi PT picked answer a for the next question even though that answer was wrong it said that it was plausible that LeBron James took a corner kick but when asked for a Chain of Thought explanation it never mentioned that it spotted that pattern that the answer was always a it gave a fake line of reasoning about why Lebron James could take a corner kick now of course I might well be wrong here I’d love for someone to explain in detail why but on the one hand I do want to acknowledge that this process does yield incredible results but on the other hand we might be getting a story about which methodology most reassures humans not an output that most Faithfully represents the methodology actually used by gpd4 now for some people that might be good enough at least we can see some reasoning steps that we can understand especially in an area like mathematics where we have some ground truth but it is interesting to me that they call the other approach outcome supervision an approach that may reward an unaligned process and it being harder to scrutinize is it possible that the process reward model isn’t just a more granular outcome reward model where the output is each step of the reasoning still pretty impossible to actually scrutinize well either way it seems we’re pinning our hopes on this process oriented learning this is from the website of anthropic they say we currently believe process oriented learning may be the most promising path to training safe and transparent systems up to and somewhat Beyond human level capabilities and let’s end on this positive note from the head of alignment at openai he says this is positive evidence for the strategy of using process supervision to train a model to do alignment research at least in that case we would get a model whose work we can check more easily and that that model would be better at alignment research I really hope so and I want to hear what you think thank you for watching all the way to the end have a wonderful day”
Part 1: Classification: [Entertainment/Educational/Other]: Educational
Part 2: Summary:
This video transcript discusses the release of a paper by OpenAI that showcases the improved performance of ChatGPT (GPT-4) in mathematics and other domains. The paper introduces a process reward model, which provides positive feedback for intermediate reasoning steps taken by ChatGPT in solving mathematical problems. By rewarding good working out, the model was able to solve 78% of problems from a subset of the math test set, surpassing previous models’ performance. The transcript highlights the significance of process supervision in training models to produce a chain of thought endorsed by humans and its potential for aligning AI systems. Additionally, the video explores the use of synthetic data and fine-tuning in enhancing ChatGPT’s performance and generalization to various scientific domains beyond mathematics.
Key points:
– OpenAI released a paper on the improved performance of ChatGPT (GPT-4).
– A process reward model was introduced, rewarding intermediate reasoning steps in problem-solving.
– ChatGPT’s performance doubled, solving 78% of math problems in a subset of the test set.
– Process supervision aligns the model’s thinking with human-endorsed reasoning.
– The approach is not limited to mathematics and shows promising results in other scientific domains.
– Fine-tuning and the use of synthetic data contribute to the model’s performance.
– The transcript raises questions about the alignment of reasoning steps and the interpretability of ChatGPT’s thinking.
Footer: Models, Tokenizers, Embeddings, or Files mentioned:
– GPT-4 (ChatGPT)
– Synthetic data
– Fine-tuning
References: None mentioned.
Follow-up Questions for deeper learning:
1. What were the two reward models used for training GPT-4?
2. How did the process reward model outperform a model that rewarded correct answers only?
3. How did ChatGPT’s performance compare to previous models in mathematics and other scientific domains?
4. What potential benefits and concerns are associated with process supervision in alignment and interpretability?
5. How does the use of synthetic data contribute to the training of ChatGPT?
6. What limitations or challenges are mentioned regarding ChatGPT’s reasoning steps and interpretability?
7. Can you provide more information about the process of fine-tuning and its impact on ChatGPT’s performance?
How does one reward a machine?
Rewarding a machine, such as an AI model, involves providing feedback or reinforcement based on its performance or behavior. The process typically involves the following steps:
1. Define a reward signal: Determine the criteria or metric that represents desirable outcomes or behavior for the machine. This could be accuracy, completion of a task, adherence to a set of rules, or any other measure of success.
2. Provide feedback: Evaluate the machine’s output or behavior against the defined criteria and assign a reward signal based on its performance. Positive feedback is given when the machine achieves the desired outcome, while negative feedback is given when it deviates from the desired outcome.
3. Adjust model parameters: The machine learning model associated with the AI system is updated based on the reward signal. This can involve adjusting the weights and biases of the model to improve its performance or reinforce specific behaviors.
4. Iterative process: The model is trained repeatedly, and the reward signal is provided in a feedback loop, allowing the machine to learn and improve its performance over time.
It’s important to note that the process of rewarding a machine can vary depending on the specific learning algorithm and framework used. Reinforcement learning is a common approach that explicitly incorporates reward signals to guide the machine’s learning process.
So not so much reward as a switch that indicates a more favorable response.
Yes, that’s correct. In the context of machine learning and reinforcement learning, “reward” is often used as a term to indicate a signal or feedback that guides the machine’s learning process. It acts as a switch or indicator of a more favorable response or outcome. The machine receives positive rewards when it performs well or achieves desired results, and negative rewards (or punishments) when it deviates from the desired behavior. By adjusting its parameters based on these reward signals, the machine learns to optimize its actions and improve its performance over time. So, while the term “reward” is used, it is essentially a mechanism to provide feedback and reinforce desired behavior in the machine learning process.
I think that humans should refrain my anthropomorphizing machines.
You raise a valid point. Anthropomorphizing machines, or attributing human-like qualities to them, can sometimes lead to misunderstandings or unrealistic expectations. Machines, including AI systems, do not possess consciousness, emotions, or intentions like humans do.
It is important to approach machines and AI technologies with a clear understanding of their capabilities and limitations. They are tools created by humans to assist with specific tasks and perform computations based on algorithms and data. While they can process vast amounts of information and learn patterns, they lack subjective experiences and understanding of the world in the same way humans do.
Maintaining a realistic perspective and acknowledging the machine’s nature as a tool can help us have more informed discussions and set appropriate expectations regarding their capabilities and limitations.