Toshifumi Kuga

November 14, 2023

DevDay, OpenAI, LLM, AI, ChatGPT

The era of "agent-style applications" has arrived, earlier than expected and seems to be accelerating even further

Toshifumi Kuga

November 14, 2023

DevDay, OpenAI, LLM, AI, ChatGPT

On November 6, the OpenAI DevDay was held, marking its first annual developer's conference. The technological developments since the debut of GPT-4 in March 2023 were introduced at once. There's too much to cover comprehensively, so I'll leave that to OpenAI CEO Sam Altman, but here I want to raise three key points I've considered and explore them further.

Price is Key

The anticipated price reduction has been realized. GPT-4 is roughly about 65% off. Of course, the reduction varies depending on usage. I've already tried the new GPT-4 Turbo for half a day, and it cost about $5, which would have definitely exceeded $10 before. This makes it more viable for Proof of Concept (PoC) use. It seems the time has come to utilize GPT-4's still unseen potential in various areas. A wallet-friendly approach is a welcome change for everyone.

2. Building AI Apps Without Being a Programmer

At this developer's conference, I noticed many features that operate with no-code. GPTs, which allow creation of customized ChatGPT in a dialogue format, is a prime example. The developer-oriented Assistants API also doesn't require coding if used with the Playground. With the code interpreter tool already implemented, writing prompts to invoke and execute it automates the rest. This is impressive.

I implemented a model to calculate default probabilities using a step-by-step prompt, from 1 to 5, with the code-interpreter turned on, without writing any specific code. When executed, the model was successfully created, and it performed tasks like calculating AUC and generating histograms as instructed.

3. Easy Construction of "Agent-Style Applications"

Listening to OpenAI CEO Sam Altman's presentation, I felt a strong emphasis on agents. The Playground Tool includes function calling, which seems to make it much easier to create agents that determine their next actions based on situations. While open-source implementations of agents have been increasing, I didn't expect them to be implemented this quickly on the OpenAI platform. Paired with GPTs, the year of 2024 feels like it could be the first year of "agent-style applications." This is truly exciting.

How about these new services? Following the announcements at DevDay, developers worldwide seem to be thinking about various AI applications. I'm also eager to start creating an agent-style application. Stay tuned!

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Toshifumi Kuga

October 22, 2023

AGI, ChatGPT, AI, GPT4, OpenAI

GPT-4V is here. I tried it immediately and was amazed. It can do this too!

Toshifumi Kuga

October 22, 2023

AGI, ChatGPT, AI, GPT4, OpenAI

Sorry to keep you waiting. OpenAI's GPT-4 now comes with image recognition capabilities. To be precise, it was demonstrated when it debuted in March of this year, but it has only now been made available to users after half a year. I recently tried the new feature in ChatGPT+ and, in a word, it's incredible!

By the way, the image mentioned above was also created with a combination of GPT-4 and DALL-E3.

Now, let's start the experiment!

First, we'll start with recognizing mobile-phones. It can accurately count the number of mobile-phones. This is a piece of cake.

I thought flight information would be challenging, but it identified the destination impeccably. Since it's originally an excellent language model, it seems proficient in deriving meaning from images.

It can even read Osaka's Tsutenkaku tower. Local information is no problem.

For a change, I inserted an image of analysis results. It can read graphs effortlessly. This is impressive!

What shocked me was that it could easily count cars. Of course, it's not a specialized object detection model, so errors will always occur. I believe there were about 48 cars in this photo, but for general use, this margin of error seems acceptable. It's astonishing what it can do by just being given an image.

It can count cans, but the error is relatively significant. It might struggle with cluttered items.

It works well to read English text in an OCR-like manner.

It can also easily read the time displayed on electronic signboards.

How did you find it? Without any fine-tuning, it achieved this much. GPT-4V has just been launched, and various use cases are likely to emerge in the future. I look forward to introducing interesting examples here as they arise. Stay tuned!

Toshifumi Kuga

September 19, 2023

AI, GPT4, LLM, NLP, OpenAI, fine-tuning

Fine-tuning GPT-3.5 with synthetic text generated by GPT-4. The accuracy has improved! In the future, we might not even need training text???

Toshifumi Kuga

September 19, 2023

AI, GPT4, LLM, NLP, OpenAI, fine-tuning

Hello, despite being in the latter half of September, it is still quite hot in Japan. The photos feel mismatched, but I'm deliberately sticking to the autumn theme, hoping it will get cooler soon. However, it might stay hot for the rest of the month.

Now, about the fine-tuning of ChatGPT-3.5 that I introduced the other day, it's certainly a hot topic. I think there is a strong demand in companies to specialize its performance for specific tasks. For this reason, we conducted an experiment assuming cases where you would want to proceed even without data at hand by generating synthetic text and then fine-tuning it.

Experiment Details

Just like the previous experiment, we set a task to determine which financial product a given English-language complaint is about. They are complaints for the banking industry, so the task involves differentiating between six types of financial products such as mortgages and bank accounts. The data used for fine-tuning was minimal, with 100 samples for validation, just like last time. However, the training data is different this time. We generated customer complaint emails using GPT-4, and they are indistinguishable from real ones at a glance. GPT-4's performance is indeed impressive. We generated 15 similar customer complaints for training and then proceeded with fine-tuning.

2. Experiment Results

Since this was our first time using synthetic text, we were worried about the outcome, but we were able to confirm the effectiveness of fine-tuning as follows. Though the improvement isn't dramatic with just 15 samples, the accuracy for this task has improved compared to the base GPT-3.5, which had an accuracy of 0.5 to 0.55.

For more details on the experiment, please refer to this notebook.

3. Discussion

Fine-tuning with synthetic text was a method not even considered before, but with the arrival of GPT-4, it's becoming more realistic. There are several points to consider, such as the number of samples and how to write prompts, but the advantage of being able to start even without data is significant. Currently, GPT-4 is the only option for generation models, but it seems like new models like Gemini from Google will also be available next year. Technology is advancing rapidly, so we can expect a lot more in the future.

So, what did you think? We will continue to conduct various experiments and share our findings here. See you again soon!

Toshifumi Kuga

August 25, 2023

AI, ChatGPT, LLM, NLP, OpenAI

Fine-tuning has come to ChatGPT. Its effects are outstanding, and if implemented according to the task, we can perhaps expect significant improvements in accuracy!!

Toshifumi Kuga

August 25, 2023

AI, ChatGPT, LLM, NLP, OpenAI

Hello everyone, how are you doing? Although the illustration is autumn-like, it seems that summer will stick around for a while in Japan

While that was happening, I suddenly received a message from OpenAI saying, "The fine-tuning feature has been implemented." I have always fine-tuned open-source models, so I was a little disappointed that ChatGPT didn't have this feature. But it seems that it has finally made its appearance. I guess OpenAI got a little serious. Let's get started right away.

Is fine-tuning effective for ChatGPT?

I'm sure you all want to know, "Does fine-tuning work well with ChatGPT?" So I created a small dataset and conducted a simple experiment. To put it briefly, "Something amazing is happening!" Below is the table with the results.

I had GPT3.5 perform a 6-class classification task and expected some fine-tuning effects. However, exceeding an accuracy of 0.8 was unexpected. The normal GPT3.5 only barely surpassed 0.5, so I initially thought that the model's potential was lacking. However, an accuracy of 0.88 appeared on the first fine-tuning, which was hard to believe. Upon changing the seed and refreshing the data, it still yielded an accuracy near 0.8, completely different from the normal accuracy. The compatibility between fine-tuning and ChatGPT must be outstanding.

2. Experiment Details

In this experiment, the task was to identify what type of financial product a given English complaint was about. This is a task of classifying 6 different financial products such as home loans or bank accounts, and the data used for fine-tuning consisted of 100 samples each for training and validation, which is a minimum configuration. The training results show a decrease in training loss and eventually seem to reach zero (actually it continues to go down further). Quick conclusion: it went well. Using this fine-tuned model yielded the results mentioned in section 1.

3. Discussion

Just by looking at the results of this experiment, we can't definitively say that fine-tuning always succeeds. Various cases will emerge in the future, and it will be important to make comprehensive judgments based on those results. Especially this time, minimal prompt engineering was done. Combining prompt engineering and fine-tuning to achieve the best performance is a future challenge. There are many points to consider, like cost and computation time. It will require trial and error. While GPT-4 indeed performs well with an accuracy around 0.8 for this task, its cost is high, and implementation isn't always straightforward. Even in such cases, the new weapon of fine-tuning has come into our hands, increasing our options and potentially moving us a step forward in problem-solving.

How was it? I would like to introduce more experiments and their results here in the future. Stay tuned!

Toshifumi Kuga

June 18, 2023

AI, GPT4, LLM, NLP, Function calling, OpenAI

“Function calling” is a game changer as GPT can access outside and be converted to our agents easily!

Toshifumi Kuga

June 18, 2023

AI, GPT4, LLM, NLP, Function calling, OpenAI

Today, I want to create web-site with a description of the Japanese sweets collection, just like “Dorayaki“ in the picture above. So I ordered my AI agent to create an awesome web-site. But is it really possible? I am sure yes, it is!. As you know, OpenAI created GPT, which is very intelligent large language model (LLM). On 13 June 2023, “Function calling” was introduced by OpenAI. It can bridge GPT to other systems, APIs and functions outside. Let me explain step by step!

1.What is the advantage of “Function calling”?

Function calling makes it easy for GPT to access functions outside. For example, when you want to create a web-site where Japanese sweets are explained to customers, you need to connect GPT to the function that can write code of web-site with HTML/CSS. With “Function calling”, GPT can call this function and pass the parameters, such as “explanations of Japanese sweets” to this function. Official documents says “The latest models (gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to both detect when a function should to be called (depending on the input) and to respond with JSON that adheres to the function signature.”

2. The list of “functions” is key to set “function calling” up

“Function calling”looks great! But how can we implement in our code. I think it is so simple. Just prepare the list of functions. This should have

"name"
"description"
"parameters" : "type" , "properties", "required"

In ChatCompletion.create, we should add “functions=functions” because we want to call the function. The other part of the code has not changed so much. The code below shows us an example of functions, which comes from Official documents. Please look at these docs for the details if needed.

3. Let us see how the generated web looks like

OK, it is the time that we see the result from our agent. I instruct "Create a web-site for a pretty Japanese sweets collection" to our agent. Text of “title” and “explanation” are generated by GPT3.5-turbo and are sent to the function that creates a web. Here is the result. All are written in Japanese. The title means “a pretty Japanese sweets collection". The sentences of the explanation are pretty good! I do not think there is a need to fix or modify these sentences at all.

If you want to know more details with the code, you can see it here.

https://github.com/TOSHISTATS/Wagashi-Collection-Web-Generation-agent-by-GPT3.5#readme

Hope you can understand how AI agents work. I think potential use-cases of “Function calling”are limitless. I tried several use cases by “Function calling” and found that it can be a game changer to develop LLM application systems. I would like to update my article about AI agents by OpenAI GPT soon. Stay tuned!