Riddle Sense
5 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Riddle Sense
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BIG-bench
RiddleSense
Situation Puzzle
Most implemented papers
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
michiyasunaga/qagnn
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NAACL 2021
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Deep Bidirectional Language-Knowledge Graph Pretraining
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.
