# AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models

arXiv: https://arxiv.org/abs/2601.08283 · HTML: https://mlsanity.com/paper/2601.08283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System](https://mlsanity.com/paper/2503.09600/) (2025)
- [Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data](https://mlsanity.com/paper/2308.03107/) (2023)
- [ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models](https://mlsanity.com/paper/2405.18638/) (2024)
- [ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources](https://mlsanity.com/paper/2409.13537/) (2024)
- [AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application](https://mlsanity.com/paper/2503.04788/) (2025)
- [Generative AI for automatic topic labelling](https://mlsanity.com/paper/2408.07003/) (2024)
- [Comprehensive Evaluation of Large Language Models for Topic Modeling](https://mlsanity.com/paper/2406.00697/) (2024)

## Abstract (arXiv)

As the volume of unstructured text continues to grow across domains, there is an urgent need for scalable methods that enable interpretable organization, summarization, and retrieval of information. This work presents a unified framework for interpretable topic modeling, zero-shot topic labeling, and topic-guided semantic retrieval over large agricultural text corpora. Leveraging BERTopic, we extract semantically coherent topics. Each topic is converted into a structured prompt, enabling a language model to generate meaningful topic labels and summaries in a zero-shot manner. Querying and document exploration are supported via dense embeddings and vector search, while a dedicated evaluation module assesses topical coherence and bias. This framework supports scalable and interpretable information access in specialized domains where labeled data is limited.