Omkar2118/datasets huggingface dataset:
TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures
Full name: Emmanuel Balogun

Data URL: https://data.mendeley.com/datasets/9k892pzkfx/1

Introducing paper: TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures
HalluEditBench

HalluEditBench is a comprehensive benchmark for evaluating knowledge editing methods' effectiveness in correcting real-world hallucinations. HalluEdit features a rigorously constructed dataset spanning nine domains and 26 topics. It evaluates methods across five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness.

Introducing paper: Can Knowledge Editing Really Correct Hallucinations?
decompile-ghidra-100k

Release decompile-ghidra-100k, a subset of 100k training samples (25k per optimization level). We provide a training script that runs in ~3.5 hours on a single A100 40G GPU. It achieves a 0.26 re-executability rate, with a total cost of under $20 for quick replication of LLM4Decompile.

https://github.com/albertan017/LLM4Decompile/blob/main/train/README.md

Introducing paper: LLM4Decompile: Decompiling Binary Code with Large Language Models
MAHNOB-HCI
Full name: MAHNOB-HCI-Tagging database

Characterising multimedia content with relevant, reliable and discriminating tags is vital for multimedia information retrieval. With the rapid expansion of digital multimedia content, alternative methods to the existing explicit tagging are needed to enrich the pool of tagged content. Currently, social media websites encourage users to tag their content. However, the users’ intent when tagging multimedia content does not always match the information retrieval goals. A large portion of user defined tags are either motivated by increasing the popularity and reputation of a user in an online com-munity or based on individual and egoistic judgments. Moreover, users do not evaluate media content on the same criteria. Some might tag multimedia content with words to express their emotion while others might use tags to describe the content. For example, a picture receive different tags based on the objects in the image, the camera by which the picture was taken or the emotion a user felt looking at the picture.

The principle of implicit tagging is to replace the user input by automatically finding descriptive tags for multimedia content, derived from an observer’s natural response. For instance, the emotions someone shows. An overview of this difference with explicit tagging is illustrated in the diagram above.

In order to facilitate research on this new area of multimedia tagging, we have recorded a database of user responses to multimedia content.