13 datasets found

This dataset contains a large corpus of text for training and evaluating large language models.

This dataset contains a large corpus of text for training and evaluating large language models.

Buffer of Thoughts is a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).

Parameter-efficient methods are able to use a single frozen pre-trained large language model to perform many tasks by learning task-specific soft prompts that modulate model...

The TruthfulQA dataset is a dataset that contains 817 questions designed to evaluate language models' preference to mimic some human falsehoods.

The paper presents the results of the OpenAI Codex evaluation on generating Python code.

The dataset used in this study to analyze the self-assessment behavior of Large language models.

The dataset used for continual pre-training of large language models, with a focus on balancing the text distribution and mitigating overfitting.

The dataset used for training large language models, with a focus on balancing the text distribution and mitigating overfitting.

The dataset used for probing LLMs for hate speech detection, including HateXplain, implicit hate, and ToxicSpans datasets.

TruthX: Alleviating Hallucinations by Editing Large Language Models

The Orca approach involves leveraging explanation tuning to generate detailed responses from a large language model.

The Evol-Instruct pipeline involves automatically evolving instruction datasets using large language models.