SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 231240 of 399 papers

TitleStatusHype
GFDC: Graph Function Dependence for Logically Consistent Dialogue Response Beyond Persona Data0
GOT4Rec: Graph of Thoughts for Sequential Recommendation0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning0
Hierarchical Inductive Transfer for Continual Dialogue Learning0
Hierarchical Inductive Transfer for Continual Dialogue Learning0
How to Complete Domain Tuning while Keeping General Ability in LLM: Adaptive Layer-wise and Element-wise Regularization0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian0
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge0
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning0
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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