21 datasets found

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

The Towards Expert-Level Medical Question Answering with Large Language Models dataset contains a large-scale dataset for medical question answering using large language models.

ALCUNA is a benchmark for evaluating the ability of large language models (LLMs) to handle new knowledge.

The dataset used in the paper is a benchmark contamination detection dataset, which contains questions and answers from various benchmarks.

The dataset used in the paper is a benchmark contamination detection dataset, which contains questions and answers from various benchmarks.

The dataset consists of 119 clusters, with a total of 584 questions, which include 4 different linguistic forms per query, so we have approximately 146 semantically different...

A comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset.

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

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

The Multimodal Visual Patterns (MMVP) benchmark is a dataset used to evaluate the visual question answering capabilities of multimodal large language models (MLLMs).

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current answer correctness...

FKTC is a test set for evaluating the factual knowledge of large language models. It contains 210,158 prompts in total.

Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine’s comprehension and reasoning abilities, where large language models (LLMs) have widely...

AdvBench is a dataset for evaluating the safety of large language models.

TruthX: Alleviating Hallucinations by Editing Large Language Models

The dataset used in the paper "Scaling Laws of Synthetic Images for Model Training". The dataset is used for supervised image classification and zero-shot classification tasks.