45 datasets found

The dataset is used for evaluating large language models on solving NLP problems.

Detecting Errors through Ensembling Prompts (DEEP) - an end-to-end large language model framework for detecting factual errors in text summarization.

Six-bit quantization can effectively reduce the size of large language models and preserve the model quality consistently across varied applications.

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there...

The dataset used in the paper is a large corpus of text data, which is used to train and evaluate natural language processing models.

The dataset used to evaluate the Thread of Thought (ThoT) strategy, which is designed to enhance the performance of Large Language Models (LLMs) in processing chaotic contextual...

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset.

The ZeroQuant dataset is a large language model dataset used in the paper.

The OPT dataset is a large language model dataset used in the paper.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset.

The paper investigates using a Large Language Model (LLM) to automatically perform web software tasks using click, scroll, and text input operations.

Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs).

Two datasets, MetaQA and FACTKG, are used to evaluate the effectiveness of the proposed method KELP. MetaQA is a critical benchmark dataset containing subsets of questions with...

LiveCodeBench is a benchmark for evaluating the performance of Large Language Models (LLMs) in code editing tasks, including debugging, translating, polishing, and requirement...

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.

Visual question answering dataset

Reasoning detection dataset for multimodal large language models

A tool for collecting traces from any Python codebase that uses OpenAI’s API.

A dataset of traces from the MACHIAVELLI environment, including API calls and their outcomes.

A structured collection of tests for input-output safeguards, including established failure tests, emerging failure tests, and next-gen architecture tests.