SOTAVerified

General Knowledge

This task aims to evaluate the ability of a model to answer general-knowledge questions.

Source: BIG-bench

Papers

Showing 341350 of 399 papers

TitleStatusHype
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?Code0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual EnvironmentCode0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
BnMMLU: Measuring Massive Multitask Language Understanding in BengaliCode0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
Learning to Understand Phrases by Embedding the DictionaryCode0
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor DecompositionCode0
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge SubtractionCode0
SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction TasksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy94.3Unverified
2Gopher-280B (few-shot, k=5)Accuracy93.9Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy 85.7Unverified
4Gopher-280B (few-shot, k=5)Accuracy 84.8Unverified
5Gopher-280B (few-shot, k=5)Accuracy84.2Unverified
6Gopher-280B (few-shot, k=5)Accuracy 84.1Unverified
7Gopher-280B (few-shot, k=5)Accuracy 83.9Unverified
8Gopher-280B (few-shot, k=5)Accuracy83.3Unverified
9Gopher-280B (few-shot, k=5)Accuracy 81.8Unverified
10Gopher-280B (few-shot, k=5)Accuracy 81Unverified