arXiv AI

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.

arXiv Machine Learning

Aug 12

KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory

arXiv:2606. 29243v2 Announce Type: replace Abstract: We introduce KrishokChat, an 85,979-instance Bengali agricultural benchmark built from 284 government publications, 13 institutions, and six regional dialects.

By Khan Raiyan Ibne Reza, Sumaiya Tabassum Nimi, Omar Ibne Shahid

fine-tuning benchmarks safety

arXiv AI

Aug 13

OpenAg: Democratizing Agricultural Intelligence

arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.

By Srikanth Thudumu, Jason Fisher

llms rag agents fine-tuning

arXiv AI

Sep 23

Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan

arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.

By Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura

llms agents multimodal safety

arXiv Machine Learning

Jun 30

KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory

arXiv:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.

By Khan Raiyan Ibne Reza, Omar Ibne Shahid

rag fine-tuning benchmarks safety

arXiv Computer Vision

Sep 23

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...

By Sangam Kumar Jena, Pandarasamy Arjunan

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 30

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

arXiv:2508. 17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis.

By Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain, Shifat E. Arman

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Translating Natural Language to Strategic Temporal Specifications via LLMs

arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).

By Marco Aruta, Francesco Improta, Vadim Malvone, Aniello Murano, Vladana Perlic

llms agents fine-tuning

arXiv AI

Jun 26

Know2Guess: A Contamination-Aware Multi-Zone Benchmark for Knowledge-Boundary Evaluation in Large Language Models

arXiv:2606. 26101v1 Announce Type: cross Abstract: Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contamination, prompt idiosyncrasy, or generic refusal behavior.

By Renwei Meng, Bowen Zhang, Jian Wang, Xican Wang, Haoyi Wu, Xuanyan Qiu, Shengan Yang

llms benchmarks

arXiv Machine Learning

Jun 29

Non-Linear Model-Based Sequential Decision-Making in Agriculture

arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.

By Sakshi Arya, Wentao Lin

reinforcement-learning

arXiv Machine Learning

Aug 5

SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA

arXiv:2509. 25459v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors.

By Haozhou Xu, Dongxia Wu, Matteo Chinazzi, Ruijia Niu, Rose Yu, Yi-An Ma

llms rag nlp benchmarks

arXiv AI

Aug 21

Qworld: Question-Specific Evaluation Criteria for LLMs

arXiv:2603. 23522v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context.

By Shanghua Gao, Yuchang Su, Pengwei Sui, Curtis Ginder, Marinka Zitnik

llms

arXiv AI

Jul 2

Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation

arXiv:2607. 00454v1 Announce Type: new Abstract: Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties.

By Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari

llms agents

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