Omkar2118/datasets huggingface dataset:
Binette's 2022 Inventors Benchmark
Full name: Binette's 2022 Inventors Benchmark

Hand-disambiguation of a sample of U.S. patents inventor mentions from PatentsView.org.

Inventors we selected indirectly by sampling inventor mentions uniformly at random. This results in inventor sampled with probability proportional to their number of granted patents.

The time period considered is from 1976 to December 31, 2021, corresponding to the disambiguation labeled "disamb_inventor_id_20211230" in PatentsView's bulk data downloads "g_persistent_inventor.tsv" file (https://patentsview.org/download/data-download-tables). That is, the benchmark disambiguation intends to contain all inventor mentions for the sampled inventors from that time period. Note that the benchmark disambiguation contains a few extraneous mentions to patents granted outside of that time period. These should be ignored for evaluation purposes.

The methodology used for the hand-disambiguation is described in Binette et al. (2022) (https://arxiv.org/abs/2210.01230). We used one disambiguation of 200 inventors from Binette et al. (2022), as well as an additional disambiguation of 200 inventors provided by an additional staff member. The two disambiguations were reviewed and validated. However, they should be expected to contain errors due to the ambiguous nature of inventor disambiguation. Furthermore, given the use as the December 30, 2021, disambiguation from PatentsView as a starting point of the hand-labeling, a bias towards this disambiguation should be expected.

Introducing paper: PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation
MN-DS
Full name: Multilabeled News Dataset

**Multilabeled News Dataset** (**MN-DS**) is a dataset for news classification. It consists of 10,917 articles in 17 first-level and 109 second-level categories from 215 media sources.

Introducing paper: MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
HaDes

**HaDes** is a token-level, reference-free hallucination detection dataset named HAllucination DEtection dataSet. To create this dataset, a large number of text segments extracted from English language Wikipedia are perturbed, and then verified these with crowd-sourced annotations.

Introducing paper: A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
FaithDial

**FaithDial** is a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark.

FaithDial contains around 50K turns across 5.5K conversations. If trained on FaithDial, state-of-the-art dialogue models are significantly more faithful while also enhancing other dialogue aspects like cooperativeness, creativity and engagement.

Introducing paper: FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
MSI

Article: A novel hierarchical model based on different emotion induction modalities for EEG emotion recognition