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The techqa dataset
TechQA: a dataset for question answering on technical support articles -
VAULT: VAriable Unified Long Text representation for Machine Reading Comprehen...
VAULT: a light-weight and parallel-efficient paragraph representation for Machine Reading Comprehension (MRC) based on contextualized representation from long document input -
MS MARCO Dev (small)
The MS MARCO Dev (small) dataset is a small version of the MS MARCO passage dev set. -
TREC 2020 Deep Learning (Passage Subtask)
The TREC 2020 Deep Learning (Passage Subtask) dataset consists of 54 queries with manual judgments from NIST annotators (211 relevance assessments per query, on average). -
TREC 2019 Deep Learning (Passage Subtask)
The TREC 2019 Deep Learning (Passage Subtask) dataset consists of 43 manually-judged queries using four relevance grades (215 relevance assessments per query, on average). -
SemEval-2013 Task 13
The SemEval-2013 task 13 dataset, containing 20 nouns, 20 verbs, and 10 adjectives in WordNet-sense-tagged contexts. -
DSTC2 dialog dataset
The DSTC2 dialog dataset consists of 5 different tasks, each of which has 1,000 synthetically-generated goal-oriented dialogs between a user and the system in the domain of... -
bAbI dialog dataset
The bAbI dialog dataset consists of 5 different tasks, each of which has 1,000 synthetically-generated goal-oriented dialogs between a user and the system in the domain of... -
bAbI story-based QA dataset
The bAbI story-based QA dataset is composed of 20 different tasks, each of which has 1,000 synthetically-generated story-question pairs. A story can be as short as two sentences... -
Context versus Prior Knowledge in Language Models
The dataset used in the paper to test the persuasion and susceptibility scores of language models. -
WikiWebQuestions
WikiWebQuestions: A dataset for semantic question answering over Wikidata. -
COMPMIX: A Benchmark for Heterogeneous Question Answering
COMPMIX: A benchmark for heterogeneous question answering. -
SPAGHETTI: Open-Domain Question Answering
SPAGHETTI: A hybrid open-domain question-answering system that combines semantic parsing and information retrieval to handle structured and unstructured data. -
LIPID dataset
The LIPID dataset is a template-free dataset for probing models with prompts from the biomedical domain. -
Google-RE (Templates) dataset
The Google-RE (Templates) dataset contains 6.11K template-based prompts from Wikipedia and 3 relations. -
Comparing Template-based and Template-free Language Model Probing
Template-based probing uses expert-made templates to create prompts, while template-free probing uses naturally-occurring text.
