HyperAI
HyperAI초신경
홈
플랫폼
문서
뉴스
연구 논문
튜토리얼
데이터셋
백과사전
SOTA
LLM 모델
GPU 랭킹
컨퍼런스
전체 검색
소개
한국어
HyperAI
HyperAI초신경
Toggle sidebar
전체 사이트 검색...
⌘
K
Command Palette
Search for a command to run...
홈
SOTA
질문 응답
Question Answering On Social Iqa
Question Answering On Social Iqa
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
Unicorn 11B (fine-tuned)
83.2
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
LLaMA-2 13B + MixLoRA
82.5
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
CompassMTL 567M with Tailor
82.2
Task Compass: Scaling Multi-task Pre-training with Task Prefix
CompassMTL 567M
81.7
Task Compass: Scaling Multi-task Pre-training with Task Prefix
LLaMA-3 8B+MoSLoRA (fine-tuned)
81.0
Mixture-of-Subspaces in Low-Rank Adaptation
DeBERTa-Large 304M
80.2
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
DeBERTa-Large 304M (classification-based)
79.9
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
UnifiedQA 3B
79.8
UnifiedQA: Crossing Format Boundaries With a Single QA System
ExDeBERTa 567M
79.6
Task Compass: Scaling Multi-task Pre-training with Task Prefix
LLaMA-3 8B + MixLoRA
78.8
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
LLaMA-2 7B + MixLoRA
78
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
RoBERTa-Large 355M (fine-tuned)
76.7
RoBERTa: A Robustly Optimized BERT Pretraining Approach
BERT-large 340M (fine-tuned)
64.5
SocialIQA: Commonsense Reasoning about Social Interactions
BERT-base 110M (fine-tuned)
63.1
SocialIQA: Commonsense Reasoning about Social Interactions
GPT-1 117M (fine-tuned)
63
SocialIQA: Commonsense Reasoning about Social Interactions
phi-1.5-web 1.3B (zero-shot)
53.0
Textbooks Are All You Need II: phi-1.5 technical report
phi-1.5 1.3B (zero-shot)
52.6
Textbooks Are All You Need II: phi-1.5 technical report
LLaMA 65B (zero-shot)
52.3
LLaMA: Open and Efficient Foundation Language Models
Chinchilla (zero-shot)
51.3
Training Compute-Optimal Large Language Models
Gopher (zero-shot)
50.6
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
0 of 24 row(s) selected.
Previous
Next