Feb. 15, 2024, 5:46 a.m. | Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady

cs.CL updates on arXiv.org arxiv.org

arXiv:2402.09259v1 Announce Type: new
Abstract: To harness the power of large language models in safety-critical domains we need to ensure the explainability of their predictions. However, despite the significant attention to model interpretability, there remains an unexplored domain in explaining sequence-to-sequence tasks using methods tailored for textual data. This paper introduces SyntaxShap, a local, model-agnostic explainability method for text generation that takes into consideration the syntax in the text data. The presented work extends Shapley values to account for parsing-based …

abstract arxiv attention cs.ai cs.cl data domain domains explainability harness interpretability language language models large language large language models model interpretability paper power predictions safety safety-critical syntax tasks text text generation textual type

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Intern - Robotics Industrial Engineer Summer 2024

@ Vitesco Technologies | Seguin, US