How do LLMs improve the adaptability and effectiveness of communicaiton repair strategies?
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Large language models (LLMs) enhance adaptability and effectiveness of communication repair strategies by enabling multilingual repair engines like RING, which utilize prompt-based strategies for program repair. Integrating LLMs into multiagent systems improves communication expressiveness, crucial for self-adaptive systems, as seen in the proposal by Nascimento et al.. Additionally, LLMs offer valuable knowledge for robotic task learning, with prompt engineering complemented by cognitive-agent approaches to evaluate and repair responses, leading to successful task completion without extensive human oversight. These findings collectively demonstrate how LLMs empower diverse systems with advanced repair capabilities and efficient communication, ultimately enhancing adaptability and effectiveness in various contexts.
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| Papers (4) | Insight |
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11 Jun 2023 | LLMs enhance adaptability and effectiveness by expanding response options and implementing repair strategies within autonomous robots, enabling >75% task completion in one-shot learning without constant human oversight. |
24 Aug 2022 | LLMs enhance adaptability and effectiveness in communication repair strategies by enabling a prompt-based approach for multilingual program repair, outperforming language-specific engines in various languages. |
| LLMs enhance adaptability and effectiveness in communication repair strategies by enabling a multilingual repair engine like RING, which excels in suggesting fixes across various programming languages with minimal effort. | |
| LLMs enhance adaptability by integrating with MASs, leveraging the MAPE-K model for self-adaptation, and improving communication in multiagent systems, thus advancing autonomous systems' capabilities. |
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