Generative AI Weekly Research Highlights | Jan'24 Part 4

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Summary of Research Papers:

Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI [https://arxiv.org/pdf/2401.14019.pdf]

Towards Goal-oriented Large Language Model Prompting: A Survey [https://arxiv.org/pdf/2401.14043.pdf]

ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases [https://arxiv.org/pdf/2401.14003.pdf]

LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools [https://arxiv.org/pdf/2401.12576.pdf]

FAIR ENOUGH: Develop and Assess a FAIR-Compliant Dataset for Large Language Model Training? [https://arxiv.org/pdf/2401.11033.pdf]

Privacy Issues in Large Language Models: A Survey [https://arxiv.org/pdf/2312.06717.pdf]

XAI for All: Can Large Language Models Simplify Explainable AI? [https://arxiv.org/pdf/2401.13110.pdf]

From Understanding to Utilization: A Survey on Explainability for Large Language Models [https://arxiv.org/pdf/2401.12874.pdf]

00:00 Intro

00:20 Customizing Text for LLMs

00:48 Goal-Oriented LLM Prompting

01:09 Enhancing LLM with Constraint Checker

01:30 Conversational LLM Examination Tools

01:50 FAIR Principles in LLM Training

02:11 Privacy Concerns in LLMs

02:31 Making Explainable AI Understandable

02:53 Categorizing LLM Explainability

03:24 End

#generativeai,#promptengineering,#largelanguagemodels,#openai,#chatgpt,#gpt4,#ai,#abcp,#prompt,#responsibleai,