Paper tables with annotated results for Augmenting Interpretable ...

... Paper. Augmenting Interpretable Models with LLMs during Training. Recent large language models (LLMs) have demonstrated remarkable prediction performance for ...

Paper tables with annotated results for Augmenting Interpretable Knowledge Tracing by Ability Attribute and Attention Mechanism.

Augmenting interpretable models with large language ... - Nature

Recent large language models (LLMs), such as ChatGPT, have demonstrated remarkable prediction performance for a growing array of tasks.

Augmenting Interpretable Models with LLMs during Training - arXiv

Abstract:Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.

JShollaj/awesome-llm-interpretability: A curated list of ... - GitHub

A curated list of amazingly awesome tools, papers, articles, and communities focused on Large Language Model (LLM) Interpretability. Table of Contents. Awesome ...

A-Paper-List-of-Awesome-Tabular-LLMs - GitHub

To automatically process numerous tables and gain valuable insights, researchers have proposed a series of deep-learning models for various table-based tasks, ...

Unsupervised and Inherently Interpretable Graph Embeddings

Similar re- sults have been achieved on graph data by leveraging carefully crafted augmentation (You et al., 2020). Despite promising results, the underlying ...

TAP4LLM: Table Provider on Sampling, Augmenting, and Packing ...

that requires further interpretation and clarification. As a result, direct reasoning on the raw tables may lead to misinterpretation and bias in the LLMs' ...

Towards Interpretable Natural Language ... - Review for NeurIPS paper

In particular, human annotation of natural language explanations can be expensive to obtain. The adaptation of a variational EM approach for joint learning both ...

(PDF) Augmenting Interpretable Models with LLMs during Training

PDF | Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.

Interpretable and explainable machine learning: A methods‐centric ...

We will consider concrete examples of state-of-the-art, including specially tailored rule-based, sparse, and additive classification models, ...

An interpretable data augmentation framework for improving ...

Tables 5, 6, and 7 present the results of the log-rank test for synthetic data produced using augmented training data com- pared to original clinical trial data ...

Improving the Usability of Tabular Data Through Data Annotation ...

Several methods have been proposed in the literature to solve the aforementioned issues. On the one hand, the use of Semantic Table Annotation ( ...

Interpretable and Informative Explanations of Outcomes

In this paper, we introduce the concept of Explanation Tables, which are concise summaries of multi-dimensional relations with a binary outcome attribute. The ...

DeepBIO: an automated and interpretable deep-learning platform for ...

... data processing and data augmentation). The sequence ... Table 2. Open in new tab. Result visualization analysis in the prediction module ...

A review: Data pre-processing and data augmentation techniques

This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning ...

Interpretable Low-Resource Legal Decision Making

Table 1: Clean subset represents cases annotated by lawyers, obtained from the CJEU. Augmented subset represents cases which are extracted from ...

Unsupervised and Inherently Interpretable Graph Embeddings

This paper studies graph representation learning and shows that data augmentation that preserves semantics can be learned and used to produce ...

Evaluation of post-hoc interpretability methods in time-series ...

Post-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc methods have emerged in recent ...

A survey on Image Data Augmentation for Deep Learning

In addition to augmentation techniques, this paper ... Table 1 Results of Taylor and Nitschke's Data Augmentation experiments on Caltech101 [63].