ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources

• URL: http://arxiv.org/abs/2409.13537v1
• Date: Fri, 20 Sep 2024 14:30:45 GMT
• Title: ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
• Authors: Shuting Yang, Zehui Liu, Wolfgang Mayer,
• Abstract summary: ShizishanGPT is an intelligent question answering system for agriculture based on the Retrieval Augmented Generation framework and agent architecture. ShizishanGPT consists of five key modules: including a generic GPT-4 based module for answering general questions; a search engine module that compensates for the problem that the large language model's own knowledge cannot be updated in a timely manner.
• Score: 1.1493479235601496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments in large language models (LLMs) have led to significant improvements in intelligent dialogue systems'ability to handle complex inquiries. However, current LLMs still exhibit limitations in specialized domain knowledge, particularly in technical fields such as agriculture. To address this problem, we propose ShizishanGPT, an intelligent question answering system for agriculture based on the Retrieval Augmented Generation (RAG) framework and agent architecture. ShizishanGPT consists of five key modules: including a generic GPT-4 based module for answering general questions; a search engine module that compensates for the problem that the large language model's own knowledge cannot be updated in a timely manner; an agricultural knowledge graph module for providing domain facts; a retrieval module which uses RAG to supplement domain knowledge; and an agricultural agent module, which invokes specialized models for crop phenotype prediction, gene expression analysis, and so on. We evaluated the ShizishanGPT using a dataset containing 100 agricultural questions specially designed for this study. The experimental results show that the tool significantly outperforms general LLMs as it provides more accurate and detailed answers due to its modular design and integration of different domain knowledge sources. Our source code, dataset, and model weights are publicly available at https://github.com/Zaiwen/CropGPT.

Related papers

• Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models [0.0]
  This paper introduces a novel approach to enhancing closed-domain Question Answering (QA) systems. It focuses on the specific needs of the Lawrence Berkeley National Laboratory (LBL) Science Information Technology (ScienceIT) domain.
  arXiv  Detail & Related papers  (2024-10-24T00:49:46Z)
• AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning [30.034193330398292]
  We propose an approach to construct instruction-tuning data that harnesses vision-only data for the agriculture domain. We utilize diverse agricultural datasets spanning multiple domains, curate class-specific information, and employ large language models (LLMs) to construct an expert-tuning set. We expert-tuned and created AgroGPT, an efficient LMM that can hold complex agriculture-related conversations and provide useful insights.
  arXiv  Detail & Related papers  (2024-10-10T22:38:26Z)
• Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization [7.522493227357079]
  Large Language Models (LLMs) are pre-trained on large-scale corpora. LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. We introduce SMART-SLIC, a highly domain-specific LLM framework.
  arXiv  Detail & Related papers  (2024-10-03T17:40:55Z)
• R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models [51.468732121824125]
  Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Existing evaluation tools only provide a few baselines and evaluate them on various domains without mining the depth of domain knowledge. In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAGs.
  arXiv  Detail & Related papers  (2024-06-17T15:59:49Z)
• DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation [19.907074685082]
  Retrieval-Augmented Generation offers a promising solution to address various limitations of Large Language Models. Current studies often rely on general knowledge sources like Wikipedia to assess the models' abilities in solving common-sense problems. We identified six required abilities for RAG models, including the ability in conversational RAG.
  arXiv  Detail & Related papers  (2024-06-09T05:33:51Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models [76.30799731147589]
  We introduce UniRAG, a plug-and-play technique that adds relevant retrieved information to prompts as few-shot examples during inference. Unlike the common belief that Retrieval Augmentation (RA) mainly improves generation or understanding of uncommon entities, our evaluation results on the MSCOCO dataset with common entities show that both proprietary models like GPT-4o and Gemini-Pro significantly enhance their generation quality when their input prompts are augmented with relevant information retrieved by MM retrievers like UniIR models.
  arXiv  Detail & Related papers  (2024-05-16T17:58:45Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases [9.478012553728538]
  We propose an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) Our system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. Our experiments demonstrate the system's effectiveness in generating more accurate answers to domain-specific and time-sensitive inquiries.
  arXiv  Detail & Related papers  (2024-03-15T16:30:14Z)
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) They provide a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
  arXiv  Detail & Related papers  (2023-05-30T03:00:30Z)
• Modular Deep Learning [120.36599591042908]
  Transfer learning has recently become the dominant paradigm of machine learning. It remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference. Modular deep learning has emerged as a promising solution to these challenges.
  arXiv  Detail & Related papers  (2023-02-22T18:11:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.