# Applications of Large Language Model Reasoning in Feature Generation

arXiv: https://arxiv.org/abs/2503.11989 · HTML: https://mlsanity.com/paper/2503.11989/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://mlsanity.com/paper/2501.09686/) (2025)
- [Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning](https://mlsanity.com/paper/2406.08527/) (2024)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Dynamic and Adaptive Feature Generation with LLM](https://mlsanity.com/paper/2406.03505/) (2024)
- [Prototypical Reward Network for Data-Efficient RLHF](https://mlsanity.com/paper/2406.06606/) (2024)
- [TFWT: Tabular Feature Weighting with Transformer](https://mlsanity.com/paper/2405.08403/) (2024)

## Built on by (most cited first, 2 of 2)

- [Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents](https://mlsanity.com/paper/2511.15074/) (2025)
- [A Cross-Lingual Analysis of Bias in Large Language Models Using Romanian History](https://mlsanity.com/paper/2510.02362/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have revolutionized natural language processing through their state of art reasoning capabilities. This paper explores the convergence of LLM reasoning techniques and feature generation for machine learning tasks. We examine four key reasoning approaches: Chain of Thought, Tree of Thoughts, Retrieval-Augmented Generation, and Thought Space Exploration. Our analysis reveals how these approaches can be used to identify effective feature generation rules without having to manually specify search spaces. The paper categorizes LLM-based feature generation methods across various domains including finance, healthcare, and text analytics. LLMs can extract key information from clinical notes and radiology reports in healthcare, by enabling more efficient data utilization. In finance, LLMs facilitate text generation, summarization, and entity extraction from complex documents. We analyze evaluation methodologies for assessing feature quality and downstream performance, with particular attention to OCTree's decision tree reasoning approach that provides language-based feedback for iterative improvements. Current challenges include hallucination, computational efficiency, and domain adaptation. As of March 2025, emerging approaches include inference-time compute scaling, reinforcement learning, and supervised fine-tuning with model distillation. Future directions point toward multimodal feature generation, self-improving systems, and neuro-symbolic approaches. This paper provides a detailed overview of an emerging field that promises to automate and enhance feature engineering through language model reasoning.