The field of large language models (LLMs) is rapidly evolving, with a focus on improving their performance, reliability, and safety. Recent research has highlighted the importance of data quality and cleaning in achieving accurate results, as well as the need for standardized methodologies and datasets for evaluating LLM-based systems. Noteworthy papers in this area include the proposal for a standardized methodology and dataset for evaluating LLM-based digital forensic timeline analysis, which demonstrated the effectiveness of the proposed approach in evaluating LLM-based forensic timeline analysis. Another notable work is the introduction of Unmasking the Canvas, a dynamic benchmark for image generation jailbreaking and LLM content safety, which combines structured prompt engineering, multilingual obfuscation, and evaluation using Groq-hosted LLaMA-3.
Advances in Large Language Model Applications and Evaluations
Sources
Evaluating the Impact of Data Cleaning on the Quality of Generated Pull Request Descriptions
Do We Need a Detailed Rubric for Automated Essay Scoring using Large Language Models?
Ensuring Reproducibility in Generative AI Systems for General Use Cases: A Framework for Regression Testing and Open Datasets
Towards a standardized methodology and dataset for evaluating LLM-based digital forensic timeline analysis
LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration
Unmasking the Canvas: A Dynamic Benchmark for Image Generation Jailbreaking and LLM Content Safety
ChatGPT for automated grading of short answer questions in mechanical ventilation
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models