# LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models

arXiv: https://arxiv.org/abs/2312.00909 · HTML: https://mlsanity.com/paper/2312.00909/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [Frustratingly Short Attention Spans in Neural Language Modeling](https://mlsanity.com/paper/1702.04521/) (2017)
- [AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT](https://mlsanity.com/paper/2211.07499/) (2022)

## Built on by (most cited first, 8 of 8)

- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models](https://mlsanity.com/paper/2406.09008/) (2024)
- [CARTS: Collaborative Agents for Recommendation Textual Summarization](https://mlsanity.com/paper/2506.17765/) (2025)
- [Urania: Differentially Private Insights into AI Use](https://mlsanity.com/paper/2506.04681/) (2025)
- [Self-Explained Keywords Empower Large Language Models for Code Generation](https://mlsanity.com/paper/2410.15966/) (2024)
- [A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models](https://mlsanity.com/paper/2507.22913/) (2025)
- [SPAR: Session-based Pipeline for Adaptive Retrieval on Legacy File Systems](https://mlsanity.com/paper/2512.12938/) (2025)
- [Construction of a Battery Research Knowledge Graph using a Global Open Catalog](https://mlsanity.com/paper/2604.20241/) (2026)

## Abstract (arXiv)

Keyword extraction is one of the core tasks in natural language processing. Classic extraction models are notorious for having a short attention span which make it hard for them to conclude relational connections among the words and sentences that are far from each other. This, in turn, makes their usage prohibitive for generating keywords that are inferred from the context of the whole text. In this paper, we explore using Large Language Models (LLMs) in generating keywords for items that are inferred from the items textual metadata. Our modeling framework includes several stages to fine grain the results by avoiding outputting keywords that are non informative or sensitive and reduce hallucinations common in LLM. We call our LLM-based framework Theme-Aware Keyword Extraction (LLM TAKE). We propose two variations of framework for generating extractive and abstractive themes for products in an E commerce setting. We perform an extensive set of experiments on three real data sets and show that our modeling framework can enhance accuracy based and diversity based metrics when compared with benchmark models.