mlsanityData to 14 Oct 2026

Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2

arXiv 2411.10050 ↗First posted 15 Nov 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fishermen in coastal areas. To fill this void in the Indian technology ecosystem, the authors introduce Jal Anveshak. This is an application framework written in Dart and Flutter that uses a Llama 2 based Large Language Model fine-tuned on pre-processed and augmented government data related to fishing yield and availability. Its main purpose is to help Indian fishermen safely get the maximum yield of fish from coastal areas and to resolve their fishing related queries in multilingual and multimodal ways.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 15 Nov 2024.

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6 of 18 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023A Comprehensive Overview of Large Language Modelscited by 648
  3. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 195
  4. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  5. 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 36
  6. 2023Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Constructioncited by 18

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