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From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs (2402.18157v1)

Published 28 Feb 2024 in cs.AI, cs.CL, and cs.CV
From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs

Abstract: The distinction between humans and animals lies in the unique ability of humans to use and create tools. Tools empower humans to overcome physiological limitations, fostering the creation of magnificent civilizations. Similarly, enabling foundational models like LLMs with the capacity to learn external tool usage may serve as a pivotal step toward realizing artificial general intelligence. Previous studies in this field have predominantly pursued two distinct approaches to augment the tool invocation capabilities of LLMs. The first approach emphasizes the construction of relevant datasets for model fine-tuning. The second approach, in contrast, aims to fully exploit the inherent reasoning abilities of LLMs through in-context learning strategies. In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs. This pipeline mirrors the human task-solving process, addressing complicated real-life user queries. At each step, we guide LLMs to summarize the achieved results and determine the next course of action. We term this pipeline `from Summary to action', Sum2Act for short. Empirical evaluations of our Sum2Act pipeline on the ToolBench benchmark show significant performance improvements, outperforming established methods like ReAct and DFSDT. This highlights Sum2Act's effectiveness in enhancing LLMs for complex real-world tasks.

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Enhancing LLMs with "Sum2Act": A Novel Approach for Complex Tasks via Open World APIs

Introduction to Sum2Act

The paper introduces a novel framework titled "From Summary to Action" (Sum2Act), crafted to empower LLMs with the ability to utilize massive real-world APIs effectively. This work is inspired by human strategy for task-solving, which involves summarizing achieved results and determining the next steps. The Sum2Act pipeline emerges as a sophisticated tool invocation methodology, significantly enhancing LLMs' capacity to address complex real-world challenges.

Overview of Tool Invocation in LLMs

The ability to integrate and utilize external tools or APIs is crucial for the advancement of LLMs towards achieving artificial general intelligence. Traditional approaches in tool invocation for LLMs have predominantly focused on dataset construction for model fine-tuning or leveraging the LLMs’ inherent reasoning capabilities. However, Sum2Act breaks new ground by introducing a mechanism in which LLMs refine their interaction with external tools by summarizing outcomes at each step and making informed decisions on subsequent actions.

Core Contributions

The paper makes several pivotal contributions to the field of AI and tool learning:

  • Introduction of Sum2Act Pipeline: A novel framework that facilitates the handling of complex tasks by harnessing the power of open-world APIs. The system's architecture, consisting of a router and a state manager, enables dynamic interaction and decision-making, reflecting a significant advancement over traditional methodologies.
  • Empirical Validation: Through rigorous evaluation on the ToolBench benchmark, which encompasses over 16,000 real-world APIs across 49 categories, Sum2Act demonstrates superior performance against existing baselines such as ReAct and DFSDT. This validation underscores the effectiveness and potential of the proposed framework in practical applications.
  • Integration with Visual APIs: Beyond textual APIs, Sum2Act shows promising adaptability in handling vision tasks, including image generation and editing. This capability highlights the framework's versatility and its potential to cater to a broader range of applications, thereby enhancing LLMs' utility in multimodal scenarios.

Theoretical and Practical Implications

The introduction of Sum2Act heralds several theoretical advancements and practical applications in the field of artificial intelligence. Theoretically, it refines our understanding of tool learning, presenting a framework that mimics human cognitive processes in task resolution. Practically, its ability to effectively integrate and utilize an expansive set of real-world APIs signifies a leap toward more sophisticated and autonomous AI systems. This could find applications in numerous domains ranging from automated customer service and data analysis to complex problem-solving in scientific research.

Future Directions in AI and Tool Learning

Sum2Act's innovative approach lays a foundation for future explorations in AI tool learning. Future research could explore the integration of even more diverse sets of APIs, including those involving more advanced scientific computations or real-time data processes. Additionally, refining the router and state manager components for even more nuanced decision-making and failure analysis could further enhance the model's efficacy and efficiency.

Conclusion

Sum2Act represents a significant stride towards realizing the full potential of LLMs in engaging with and solving complex real-world tasks. By endowing LLMs with a structured mechanism for action based on summary and reflection, this work opens up new vistas in artificial intelligence research and applications. Its success on the ToolBench benchmark not only underscores its immediate utility but also sets a precedent for future endeavors in the domain of tool learning and AI development.

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References (35)
  1. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35:23716–23736, 2022.
  2. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392–18402, 2023.
  3. Visualgpt: Data-efficient adaptation of pretrained language models for image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18030–18040, 2022.
  4. Visual programming: Compositional visual reasoning without training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14953–14962, 2023.
  5. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. arXiv preprint arXiv:2305.11554, 2023.
  6. Segment anything. arXiv:2304.02643, 2023.
  7. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199–22213, 2022.
  8. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888–12900. PMLR, 2022.
  9. BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023a.
  10. Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244, 2023b.
  11. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023a.
  12. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499, 2023b.
  13. Controlllm: Augment language models with tools by searching on graphs. arXiv preprint arXiv:2305.10601, 2023c.
  14. OpenAI. Chatgpt: A large-scale transformer-based language model, 2022. https://www.openai.com/chatgpt.
  15. OpenAI. Gpt-4: A large-scale transformer-based language model, 2023. https://www.openai.com/gpt-4.
  16. Gorilla: Large language model connected with massive apis. arXiv preprint arXiv:2305.15334, 2023.
  17. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023.
  18. Tool learning with foundation models, 2023a.
  19. Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023b.
  20. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1–67, 2020.
  21. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.
  22. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023.
  23. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580, 2023.
  24. Toolalpaca: Generalized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv:2306.05301, 2023.
  25. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  26. Multimodal few-shot learning with frozen language models. Advances in Neural Information Processing Systems, 34:200–212, 2021.
  27. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  28. Visual chatgpt: Talking, drawing and editing with visual foundation models. arXiv preprint arXiv:2303.04671, 2023.
  29. Mm-react: Prompting chatgpt for multimodal reasoning and action. arXiv preprint arXiv:2303.11381, 2023.
  30. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022.
  31. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.
  32. mplug-owl: Modularization empowers large language models with multimodality. arXiv preprint arXiv:2304.14178, 2023.
  33. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023.
  34. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022.
  35. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
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Authors (8)
  1. Yulong Liu (48 papers)
  2. Yunlong Yuan (4 papers)
  3. Chunwei Wang (13 papers)
  4. Jianhua Han (49 papers)
  5. Yongqiang Ma (12 papers)
  6. Li Zhang (690 papers)
  7. Nanning Zheng (146 papers)
  8. Hang Xu (204 papers)
Citations (4)
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