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How to model query quality, use bootstrapping, and report realistic RAG performance with RAGAS metrics and confidence intervals.

MLE-Bench introduces a new benchmark to evaluate AI agents on real-world ML engineering tasks using Kaggle competitions. This post highlights key findings, including resource scaling effects, debugging challenges, and the performance of different agent frameworks.

This post explores a neural network model designed for the Causal Discovery Challenge organized by ADIA Lab. It highlights the use of a Transformer-based architecture with two layers of scaled dot-product attention and layer normalization, achieving a multi-balanced accuracy of 47.986%.

I will provide my solution to the Trojan Detection Challenge 2023 (LLM Edition), a competition at NeurIPS 2023, which aims to advance our understanding and development of methods for detecting hidden functionality in large language models (LLMs). The primary task is to reverse-engineer the trigger prompts associated with a given target string.

The BLEU (Bilingual Evaluation Understudy) score is a metric used in Natural Language Processing (NLP) to evaluate the quality of text generated by machines, such as translations. It operates as a precision-based metric, examining the overlap of words or n-grams between the generated text and a reference text.