Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

• URL: http://arxiv.org/abs/2404.09491v2
• Date: Mon, 6 May 2024 08:00:00 GMT
• Title: Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
• Authors: Sungwon Han, Jinsung Yoon, Sercan O Arik, Tomas Pfister,
• Abstract summary: We propose a novel in-context learning framework, FeatLLM, which employs Large Language Models as feature engineers. FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT.
• Score: 35.03338699349037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many real-world applications. In this paper, we propose a novel in-context learning framework, FeatLLM, which employs LLMs as feature engineers to produce an input data set that is optimally suited for tabular predictions. The generated features are used to infer class likelihood with a simple downstream machine learning model, such as linear regression and yields high performance few-shot learning. The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time. Compared to existing LLM-based approaches, FeatLLM eliminates the need to send queries to the LLM for each sample at inference time. Moreover, it merely requires API-level access to LLMs, and overcomes prompt size limitations. As demonstrated across numerous tabular datasets from a wide range of domains, FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT.

Related papers

• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data [39.29778853025738]
  Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks. This paper proposes a framework, Learning to Reduce, that fine-tunes a language model with On-Policy Learning to generate a reduced version of an input structured data.
  arXiv  Detail & Related papers  (2024-07-03T01:51:50Z)
• LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
  Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
  arXiv  Detail & Related papers  (2024-07-02T22:23:40Z)
• Verbalized Machine Learning: Revisiting Machine Learning with Language Models [63.10391314749408]
  We introduce the framework of verbalized machine learning (VML) VML constrains the parameter space to be human-interpretable natural language. We conduct several studies to empirically evaluate the effectiveness of VML.
  arXiv  Detail & Related papers  (2024-06-06T17:59:56Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• LLM-augmented Preference Learning from Natural Language [19.700169351688768]
  Large Language Models (LLMs) are equipped to deal with larger context lengths. LLMs can consistently outperform the SotA when the target text is large. Few-shot learning yields better performance than zero-shot learning.
  arXiv  Detail & Related papers  (2023-10-12T17:17:27Z)
• From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models [18.219485459836285]
  Generative Tabular Learning (GTL) is a novel framework that integrates the advanced functionalities of large language models (LLMs) Our empirical study spans 384 public datasets, rigorously analyzing GTL's scaling behaviors. GTL-LLaMA-2 model demonstrates superior zero-shot and in-context learning capabilities across numerous classification and regression tasks.
  arXiv  Detail & Related papers  (2023-10-11T09:37:38Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [12.708117108874083]
  Large Language Models (LLMs) generate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning. Previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples. In this paper, we deliver a comprehensive study of. PEFT techniques for LLMs under the automated code generation scenario.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• Language models are weak learners [71.33837923104808]
  We show that prompt-based large language models can operate effectively as weak learners. We incorporate these models into a boosting approach, which can leverage the knowledge within the model to outperform traditional tree-based boosting. Results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
  arXiv  Detail & Related papers  (2023-06-25T02:39:19Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.