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InfiAgent-DABench: Evaluating LLM-Based Agents on Data Analysis Tasks

Q: How do proprietary models compare to open-source models in terms of performance?

Proprietary models, such as GPT-4, Gemini-Pro, and Claude-2.1, generally outperform open-source models in terms of performance on data analysis tasks. These proprietary models have been developed with significant resources and expertise, allowing them to achieve higher accuracy rates compared to their open-source counterparts. The gap in performance between proprietary and open-source models highlights the need for further advancements in open-source LLMs for data analysis tasks.

Q: What are the implications of DAAgent surpassing GPT-3.5 in performance?

The fact that DAAgent surpasses GPT-3.5 by 3.9% on data analysis tasks has significant implications for the field of large language model-based agents (LLMs). This achievement demonstrates that specialized agents like DAAgent can be tailored to excel at specific tasks, showcasing the potential for fine-tuning LLMs for enhanced performance on targeted applications. The success of DAAgent also suggests that instruction-tuning datasets like DAInstruct play a crucial role in improving model capabilities beyond what is achieved by even state-of-the-art proprietary models like GPT-3.5.

Q: How can instruction-tuning datasets like DAInstruct improve model capabilities beyond proprietary models?

Instruction-tuning datasets like DAInstruct provide a structured framework for training specialized agents focused on specific tasks such as data analysis. By leveraging these datasets during training, LLMs can learn task-specific nuances and optimize their responses accordingly. One key advantage of instruction-tuning datasets is their ability to enhance an LLM's understanding and proficiency in executing complex instructions related to a particular domain or task. This targeted training approach allows the model to develop specialized skills that may not be fully addressed by general-purpose training alone. Additionally, instruction-tuning datasets enable researchers and developers to fine-tune existing LLMs or build new specialized agents with improved capabilities tailored specifically for challenging real-world applications like data analysis tasks.

Kernekoncepter

The author introduces InfiAgent-DABench, a benchmark specifically designed to evaluate LLM-based agents on data analysis tasks, highlighting the challenges faced by current models and the development of a specialized agent that outperforms GPT-3.5.

Resumé

InfiAgent-DABench is introduced as the first benchmark for evaluating LLM-based agents on data analysis tasks. The paper outlines the challenges faced by these agents in handling complex data analysis tasks and presents DAAgent, a specialized agent that surpasses GPT-3.5 in performance. The dataset DAEval consists of realistic CSV files and closed-form questions generated based on key concepts in data analysis. The process involves human assessment, filtering, and an agent framework for evaluation. Results show that GPT-4 outperforms other models, with open-source models catching up quickly. DAAgent achieves better performance than GPT-3.5 through instruction-tuning with DAInstruct.

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Citater

"Our extensive benchmarking of 34 LLMs uncovers the current challenges encountered in data analysis tasks."
"We propose InfiAgent-DABench, which is the first benchmark for evaluating agents on data analysis tasks."
"DAAgent achieves a better performance over GPT-3.5 by 3.9% on DABench."

Vigtigste indsigter udtrukket fra

InfiAgent-DABench

by Xueyu Hu,Ziy... kl. arxiv.org 03-12-2024

https://arxiv.org/pdf/2401.05507.pdf

Dybere Forespørgsler

How do proprietary models compare to open-source models in terms of performance?

Proprietary models, such as GPT-4, Gemini-Pro, and Claude-2.1, generally outperform open-source models in terms of performance on data analysis tasks. These proprietary models have been developed with significant resources and expertise, allowing them to achieve higher accuracy rates compared to their open-source counterparts. The gap in performance between proprietary and open-source models highlights the need for further advancements in open-source LLMs for data analysis tasks.

What are the implications of DAAgent surpassing GPT-3.5 in performance?

The fact that DAAgent surpasses GPT-3.5 by 3.9% on data analysis tasks has significant implications for the field of large language model-based agents (LLMs). This achievement demonstrates that specialized agents like DAAgent can be tailored to excel at specific tasks, showcasing the potential for fine-tuning LLMs for enhanced performance on targeted applications.
The success of DAAgent also suggests that instruction-tuning datasets like DAInstruct play a crucial role in improving model capabilities beyond what is achieved by even state-of-the-art proprietary models like GPT-3.5.

How can instruction-tuning datasets like DAInstruct improve model capabilities beyond proprietary models?

Instruction-tuning datasets like DAInstruct provide a structured framework for training specialized agents focused on specific tasks such as data analysis. By leveraging these datasets during training, LLMs can learn task-specific nuances and optimize their responses accordingly.
One key advantage of instruction-tuning datasets is their ability to enhance an LLM's understanding and proficiency in executing complex instructions related to a particular domain or task. This targeted training approach allows the model to develop specialized skills that may not be fully addressed by general-purpose training alone.
Additionally, instruction-tuning datasets enable researchers and developers to fine-tune existing LLMs or build new specialized agents with improved capabilities tailored specifically for challenging real-world applications like data analysis tasks.