Summary of Stateflow: Enhancing Llm Task-solving Through State-driven Workflows, by Yiran Wu et al.
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
by Yiran Wu, Tianwei Yue, Shaokun Zhang, Chi Wang, Qingyun Wu
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary StateFlow, a novel Large Language Model (LLM)-based task-solving paradigm, conceptualizes complex task-solving processes as state machines. By distinguishing between “process grounding” and “sub-task solving”, StateFlow enhances control and interpretability of the task-solving procedure. The model represents states and state transitions using heuristic rules or LLM decisions, allowing for a dynamic and adaptive progression. Actions within a state involve calling LLMs with different prompts and utilizing external tools as needed. Our results show that StateFlow significantly enhances LLM efficiency, achieving higher success rates in InterCode SQL and ALFWorld benchmarks with less cost compared to ReAct. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StateFlow is a new way for big language models to solve complex problems. It thinks of these tasks as machines that go through different states. Each state has its own actions that the model takes, like asking the LLM questions or using tools. This helps the model make better decisions and do things more efficiently. In tests, StateFlow did much better than another approach called ReAct, solving problems 13% to 28% faster with less effort. |
Keywords
» Artificial intelligence » Grounding » Large language model
