Summary Using LLM Assistant More Affordably and Accurately arxiv.org
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One Line
EcoAssistant improves affordability and accuracy in code-driven question answering tasks through a conversational assistant, code executor, and query database.
Slides
Slide Presentation (10 slides)
Key Points
- EcoAssistant is a system designed to improve affordability and accuracy in code-driven question answering tasks using large language models (LLMs).
- It consists of three components: a conversational LLM assistant, an automatic code executor, and a database of past successful queries and solutions.
- The assistant interacts with the code executor in an iterative manner to generate accurate code based on execution results.
- EcoAssistant prioritizes the use of cost-effective assistants before resorting to more expensive models, reducing overall costs.
- Solution demonstration retrieves past successful query-code pairs from a database to guide the assistant in generating accurate and efficient responses.
- Empirical experiments show that EcoAssistant outperforms individual LLM assistants, surpassing GPT-4 in success rate while costing less.
- The system has limitations, including reliance on a pre-defined hierarchy of assistants and potential bottlenecks when processing large query volumes.
- Future work could explore adaptive selection mechanisms for the assistant hierarchy and advanced retrieval mechanisms for solution demonstration.
Summaries
20 word summary
EcoAssistant enhances LLM affordability and accuracy in code-driven question answering tasks with a conversational assistant, code executor, and query database.
70 word summary
EcoAssistant improves affordability and accuracy of large language models (LLMs) in code-driven question answering tasks. It includes a conversational LLM assistant, automatic code executor, and a database of successful queries and solutions. The assistant refines its code based on feedback, prioritizes cheaper LLMs, and retrieves past successful queries for guidance. It outperforms individual LLM assistants, costing less than GPT-4. Future work could explore adaptive selection mechanisms and user feedback integration.
152 word summary
EcoAssistant is a system that enhances the affordability and accuracy of large language models (LLMs) in code-driven question answering tasks. It consists of three components: a conversational LLM assistant, an automatic code executor, and a database of successful queries and solutions. The assistant interacts iteratively with the code executor, refining its code based on feedback to produce accurate answers. The system also employs an assistant hierarchy that prioritizes cheaper LLMs before resorting to more expensive ones, reducing overall costs. Solution demonstration retrieves past successful queries from the database to guide the assistant in generating accurate responses. Empirical experiments show that EcoAssistant outperforms individual LLM assistants, surpassing GPT-4 in success rate while costing less than half. However, limitations include the reliance on a pre-defined hierarchy and a bottleneck in processing large query volumes. Future work could explore adaptive selection mechanisms, advanced retrieval mechanisms, user feedback integration, and additional agents for collaborative task completion.
390 word summary
EcoAssistant is a system designed to make large language models (LLMs) more affordable and accurate in code-driven question answering tasks. It addresses the challenges of generating correct code and handling high query volumes using LLM assistants. The system consists of three components: a conversational LLM assistant, an automatic code executor, and a database of past successful queries and solutions.
The first component allows the LLM assistant to interact with the code executor in an iterative manner. The assistant generates code and receives feedback from the executor, refining its code based on the execution results. This iterative process enables the assistant to produce accurate answers to user queries.
The second component is an assistant hierarchy that prioritizes the use of cheaper LLMs before resorting to more expensive ones. By starting with cost-effective assistants and only escalating to more expensive models when necessary, the system reduces overall costs while still effectively addressing queries.
The third component is solution demonstration, which retrieves solutions from past successful queries stored in a database. When a new query enters the system, the most similar query and its associated code are retrieved and used as in-context demonstrations for the LLM assistant. This helps guide the assistant in generating accurate and efficient responses without redundant iterations.
Empirical experiments demonstrate that EcoAssistant offers distinct advantages in affordability and accuracy compared to individual LLM assistants. It surpasses GPT-4 by 10 points in success rate while costing less than half of GPT-4's expense.
The system is evaluated on various types of queries using different LLMs, including GPT-3.5-turbo, GPT-4, and LLAMA-2-13B-chat. The results show that the assistant hierarchy can significantly reduce costs while the solution demonstration improves performance. When combined in EcoAssistant, these strategies achieve superior performance with further cost reduction.
The system is not without limitations. It relies on a pre-defined hierarchy of LLM assistants, which may not always be optimal for all queries. The reliance on a database for storing past successful query-code pairs may become a bottleneck when processing millions of queries. Additionally, the system may struggle with deeply specialized or niche queries that require expert-level domain knowledge.
Future work could explore more adaptive selection mechanisms for the assistant hierarchy, as well as advanced retrieval mechanisms for solution demonstration. The system could also benefit from incorporating more informative user feedback and adding additional agents to enhance collaborative task completion.