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Sunday, December 1, 2024

Performance of Multi-Trial Models and LLMs: A Direct Showdown between AI and Human Engineers

December 01, 2024

With the rapid development of generative AI, particularly Large Language Models (LLMs), the capabilities of AI in code reasoning and problem-solving have significantly improved. In some cases, after multiple trials, certain models even outperform human engineers on specific tasks. This article delves into the performance trends of different AI models and explores the potential and limitations of AI when compared to human engineers.

Performance Trends of Multi-Trial Models

In code reasoning tasks, models like O1-preview and O1-mini have consistently shown outstanding performance across 1-shot, 3-shot, and 5-shot tests. Particularly in the 3-shot scenario, both models achieved a score of 0.91, with solution rates of 87% and 83%, respectively. This suggests that as the number of prompts increases, these models can effectively improve their comprehension and problem-solving abilities. Furthermore, these two models demonstrated exceptional resilience in the 5-shot scenario, maintaining high solution rates, highlighting their strong adaptability to complex tasks.

In contrast, models such as Claude-3.5-sonnet and GPT-4.0 performed slightly lower in the 3-shot scenario, with scores of 0.61 and 0.60, respectively. While they showed some improvement with fewer prompts, their potential for further improvement in more complex, multi-step reasoning tasks was limited. Gemini series models (such as Gemini-1.5-flash and Gemini-1.5-pro), on the other hand, underperformed, with solution rates hovering between 0.13 and 0.38, indicating limited improvement after multiple attempts and difficulty handling complex code reasoning problems.

The Impact of Multiple Prompts

Overall, the trend indicates that as the number of prompts increases from 1-shot to 3-shot, most models experience a significant boost in score and problem-solving capability, particularly O1 series and Claude-3.5-sonnet. However, for some underperforming models, such as Gemini-flash, even with additional prompts, there was no substantial improvement. In some cases, especially in the 5-shot scenario, the model's performance became erratic, showing unstable fluctuations.

These performance differences highlight the advantages of certain high-performance models in handling multiple prompts, particularly in their ability to adapt to complex tasks and multi-step reasoning. For example, O1-preview and O1-mini not only displayed excellent problem-solving ability in the 3-shot scenario but also maintained a high level of stability in the 5-shot case. In contrast, other models, such as those in the Gemini series, struggled to cope with the complexity of multiple prompts, exhibiting clear limitations.

Comparing LLMs to Human Engineers

When comparing the average performance of human engineers, O1-preview and O1-mini in the 3-shot scenario approached or even surpassed the performance of some human engineers. This demonstrates that leading AI models can improve through multiple prompts to rival top human engineers. Particularly in specific code reasoning tasks, AI models can enhance their efficiency through self-learning and prompts, opening up broad possibilities for their application in software development.

However, not all models can reach this level of performance. For instance, GPT-3.5-turbo and Gemini-flash, even after 3-shot attempts, scored significantly lower than the human average. This indicates that these models still need further optimization to better handle complex code reasoning and multi-step problem-solving tasks.

Strengths and Weaknesses of Human Engineers

AI models excel in their rapid responsiveness and ability to improve after multiple trials. For specific tasks, AI can quickly enhance its problem-solving ability through multiple iterations, particularly in the 3-shot and 5-shot scenarios. In contrast, human engineers are often constrained by time and resources, making it difficult for them to iterate at such scale or speed.

However, human engineers still possess unparalleled creativity and flexibility when it comes to complex tasks. When dealing with problems that require cross-disciplinary knowledge or creative solutions, human experience and intuition remain invaluable. Especially when AI models face uncertainty and edge cases, human engineers can adapt flexibly, while AI may struggle with significant limitations in these situations.

Future Outlook: The Collaborative Potential of AI and Humans

While AI models have shown strong potential for performance improvement with multiple prompts, the creativity and unique intuition of human engineers remain crucial for solving complex problems. The future will likely see increased collaboration between AI and human engineers, particularly through AI-Assisted Frameworks (AIACF), where AI serves as a supporting tool in human-led engineering projects, enhancing development efficiency and providing additional insights.

As AI technology continues to advance, businesses will be able to fully leverage AI's computational power in software development processes, while preserving the critical role of human engineers in tasks requiring complexity and creativity. This combination will provide greater flexibility, efficiency, and innovation potential for future software development processes.

Conclusion

The comparison of multi-trial models and LLMs highlights both the significant advancements and the challenges AI faces in the coding domain. While AI performs exceptionally well in certain tasks, particularly after multiple prompts, top models can surpass some human engineers. However, in scenarios requiring creativity and complex problem-solving, human engineers still maintain an edge. Future success will rely on the collaborative efforts of AI and human engineers, leveraging each other's strengths to drive innovation and transformation in the software development field.

The Future of Large Language Models: Technological Evolution and Application Prospects from GPT-3 to Llama 3

June 19, 2024

At the 2024 Zhiyuan Conference, Meta research scientist and the author of Llama 2 and Llama 3, Dr. Thomas Scialom, delivered a keynote speech titled "The Past, Present, and Future of Large Language Models." In his presentation, he thoroughly discussed the development trajectory and future prospects of large language models. By analyzing flagship products from companies such as OpenAI, DeepMind, and Meta, Thomas delved into the technical details and significance of key technologies like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) used in models like Llama 2. He also shared his views on the future development of large language models from the perspectives of multimodality, Agents, and robotics.

Development Trajectory of Large Language Models

Thomas began by highlighting the pivotal moments in the history of large models, reflecting on their rapid development in recent years. The emergence of GPT-3, for instance, marked a milestone indicating that AI had achieved functional utility, thereby broadening the scope and application of AI technology. The development of large language models can essentially be seen as a collection of weights based on the Transformer architecture, trained through self-supervised learning to predict the next token with minimal loss based on vast amounts of data.

Two Ways to Scale Model Size

There are two primary ways to scale the size of large language models: increasing the number of model parameters and increasing the amount of training data. In their research on GPT-3, OpenAI discovered that enlarging the model parameters significantly enhanced performance, prompting a substantial increase in model size. However, DeepMind's research highlighted the importance of training strategies and data volume, introducing the Chinchilla model, which optimizes computational resources to achieve excellent performance even with smaller parameter sizes.

Optimization of the Llama Series Models

In the training process of the Llama series models, researchers rethought how to optimize computational resources to ensure efficiency in both training and inference phases. Although Llama 2's pre-training parameter scale is similar to Llama 1, it includes more training data tokens and employs a longer context length. Additionally, Llama 2 incorporates SFT and RLHF technologies during the post-training phase, further enhancing its ability to follow instructions.

Supervised Fine-Tuning (SFT)

SFT is a method used to align models with instructions by having annotators generate content based on given prompts. Thomas's team invested significant resources to have annotators produce high-quality content, which was then used to fine-tune the model. Although costly, SFT significantly improves the model's ability to handle complex tasks.

Reinforcement Learning from Human Feedback (RLHF)

Compared to SFT, RLHF involves annotators comparing different model-generated answers and selecting the better one. This feedback is then used to train a reward model, which improves the model's accuracy. By expanding the dataset and adjusting the model size, Thomas's team continuously optimized the reward model, ultimately achieving performance that surpasses GPT-4.

Combining Human and AI Capabilities

Thomas emphasized that the real strength of humans lies in judging the quality of answers rather than creating them. Therefore, the true magic of RLHF is in combining human feedback with AI capabilities to create models that surpass human performance. The collaboration between humans and AI is crucial in this process.

The Future of Large Language Models

Thomas believes that the future of large language models lies in multimodality, integrating images, sounds, videos, and other diverse information to enhance their processing capabilities. Additionally, Agent technology and robotics research will be significant areas of future development. By combining language modeling with multimodal technologies, we can build more practical Agent systems and robotic entities.

Importance of Computational Power

Thomas stressed the critical role of computational power in AI development. As computational resources increase, AI model performance improves significantly. From the ImageNet competition to AlphaGo's conquest of Go, AI technology has made rapid strides. In the future, as computational resources continue to expand, the AI field is poised to witness more unexpected breakthroughs.

Through Thomas's insightful speech, we not only gained a comprehensive understanding of the development trajectory and future direction of large language models but also recognized the pivotal role of technological innovation and computational resources in advancing AI. The research and application of large language models will continue to have profound impacts across technological, commercial, and social domains.

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