Omkar2118/datasets huggingface dataset:
VNBench

VNBench is a comprehensive benchmark suite for video generative models, which evaluates video generation quality across specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods. The suite includes 16 dimensions for evaluating Text-to-Video (T2V) models, such as subject consistency, motion smoothness, and overall consistency. VNBench also supports evaluating Image-to-Video (I2V) models and has recently introduced VBench-Long for evaluating longer videos¹. It's designed to align with human perceptions and provide valuable insights for future developments in video generation.

# Diversity of “Needle” Types
Edit: Using artificially added subtitles as the "needle". These subtitles are embedded in video frames to simulate the scenario of finding specific textual information in a video.

Insert: Using images as the "needle". These images are inserted as static segments between video frames to assess the model's ability to recognize and remember static images in a video.

Level Classification: Classified into two levels based on image recognizability. The first level uses common objects (e.g., fruit images), while the second level uses more challenging landmark or object images, increasing the task's difficulty.

# Diversity of Video “Haystack”
Temporal Distribution: The video "haystack" used by VNBench comes from different data sources, with video durations ranging from 10 seconds to 180 seconds. This covers short, medium, and long video lengths to evaluate the model's adaptability to different video lengths.

Content Coverage: The video content includes various scenes, ensuring the evaluation's broadness and the diversity of video sources.

# Diversity of Queries
Retrieval Task: Requires the model to retrieve specific "needles" from videos, assessing the model's fine-grained understanding and information extraction ability.

Ordering Task: Requires the model to identify and order the timestamps of all inserted "needles" in the video, assessing the model's understanding of video temporal dynamics and event sequences.

Counting Task: Requires the model to count the occurrences of specific objects in the video, including recognizing and tracking repetitive patterns within and across frames. This assesses the model's understanding of spatial and temporal dimensions.

(1) Vchitect/VBench: [CVPR2024 Highlight] VBench - GitHub. https://github.com/Vchitect/VBench.
(2) VBench: Comprehensive Benchmark Suite for Video Generative Models. https://arxiv.org/abs/2311.17982.
(3) Linux中vdbench的安装与使用_vdbench参数详解-CSDN博客. https://blog.csdn.net/SweeNeil/article/details/95338293.
(4) Cinebench 2024 Downloads - Maxon. https://www.maxon.net/zh/downloads/cinebench-2024-downloads.
(5) undefined. https://doi.org/10.48550/arXiv.2311.17982.

Introducing paper: Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs
OlympicArena

OlympicArena is a benchmark to evaluate advanced capabilities of language models across a broad spectrum of Olympic-level challenges. 

# Comprehensive: 
The benchmark includes a comprehensive set of 11,163 problems from 62 distinct Olympic competitions, structured with 13 answer types. It spans seven core disciplines: mathematics, physics, chemistry, biology, geography, astronomy, and computer science, encompassing a total of 34 specialized branches
# High-challenging: 
The benchmark focuses on Olympic-level problems and covers 8 types of logical reasoning abilities and 5 types of visual reasoning abilities.
# Rigorous: 
Given the increasing scale of pre-training corpora, it is crucial to detect potential benchmark leakage. We employ a recently proposed instance-level leakage detection metric to validate our benchmark’s effectiveness.
# Fine-grained Evaluation: 
We conduct comprehensive evaluations from both the answer-level and process-level perspectives. Additionally, we perform fine-grained evaluations and analyses on different types of cognitive reasoning, from both logical and visual perspectives to better interpret the current capabilities of AI.

Introducing paper: OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
BenBench

BenBench is designed to benchmark the potential for data leakage in benchmark datasets, which can lead to biased and inequitable comparisons. In this work, we are not pursuing technical contributions in system development; instead, we are attempting to encourage the healthy development of this field, particularly through the lens of mathematical reasoning tasks, in the following aspects:

1. Summaries of various pre-training behaviors and challenges for detecting benchmark leakage;
2. Proposal of a detection pipeline for estimating pre-training behaviors;
3. Leakage analysis of existing models;
4. Recommendation for model documentation (i.e., introducing Benchmark Transparency Card), benchmark setup and future evaluations.

Introducing paper: Benchmarking Benchmark Leakage in Large Language Models
VietMed-NER

Spoken Named Entity Recognition (NER) aims to extracting named entities from speech and categorizing them into types like person, location, organization, etc. In this work, we present VietMed-NER - the first spoken NER dataset in the medical domain. To our best knowledge, our real-world dataset is the largest spoken NER dataset in the world in terms of the number of entity types, featuring 18 distinct types. Secondly, we present baseline results using various state-of-the-art pre-trained models: encoder-only and sequence-to-sequence. We found that pre-trained multilingual models XLM-R outperformed all monolingual models on both reference text and ASR output. Also in general, encoders perform better than sequence-to-sequence models for the NER task. By simply translating, the transcript is applicable not just to Vietnamese but to other languages as well. All code, data and models are made publicly available here: https://github.com/leduckhai/MultiMed

Introducing paper: Medical Spoken Named Entity Recognition
Twitter-SMNER

This task aims to extract named entities and entity types while further predicting segmentation masks of visual objects.

Introducing paper: Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation
real-fake-news

This the dataset for Every Language Counts: Learn and Unlearn in Multilingual LLMs.

Each of the 100 row contains a GPT generated 'real' news article, a corresponding 'fake' news article with injected fake information, and the 'fake' keyword. It contains 10 Q&A pairs on 'real' news for instruction tunning. We also provide one question to evaluate 'real' news understanding and another question to count the appearance of 'fake' detail.

Introducing paper: Learn and Unlearn in Multilingual LLMs
TADAC
Full name: Text Annotated Distortion, Appearance and Content Dataset

We have developed a systematic method for constructing large text annotated image databases designed for exploiting vision-language modeling for image quality assessment and present the Text Annotated Distortion, Appearance and Content (TADAC) database containing over 1.6 million images annotated with texts about their semantic contents, distortion characteristics and appearance properties. We used existing labels or automatic image captioning to annotate the semantic content, designed a list of suitable textual phrases for describing the distortion characteristics, and developed automatic algorithms for computing the appearance properties and annotated these properties with suitable textual descriptions. The TADAC database is the first of its kind that is annotated with all three types of quality relevant texts to enable the learning of high level knowledge about all possible factors affecting image quality. TADAC has enabled the development of the first BIQA model (SLIQUE) that jointly models semantic content, distortion and appearance. We will make TADAC publicly available.

Introducing paper: Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment