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SOTA
问答
Question Answering On Newsqa
Question Answering On Newsqa
评估指标
EM
F1
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
EM
F1
Paper Title
Repository
OpenAI/o3-mini-2025-01-31-high
96.52
92.13
o3-mini vs DeepSeek-R1: Which One is Safer?
OpenAI/o1-2024-12-17-high
81.44
88.7
0/1 Deep Neural Networks via Block Coordinate Descent
-
xAI/grok-2-1212
70.57
88.24
XAI for Transformers: Better Explanations through Conservative Propagation
deepseek-r1
80.57
86.13
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Riple/Saanvi-v0.1
72.61
85.44
Time-series Transformer Generative Adversarial Networks
Anthropic/claude-3-5-sonnet
74.23
82.3
Claude 3.5 Sonnet Model Card Addendum
-
OpenAI/GPT-4o
70.21
81.74
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
-
Google/Gemini 1.5 Flash
68.75
79.91
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
SpanBERT
-
73.6
SpanBERT: Improving Pre-training by Representing and Predicting Spans
LinkBERT (large)
-
72.6
LinkBERT: Pretraining Language Models with Document Links
DyREX
-
68.53
DyREx: Dynamic Query Representation for Extractive Question Answering
DecaProp
53.1
66.3
Densely Connected Attention Propagation for Reading Comprehension
BERT+ASGen
54.7
64.5
-
-
AMANDA
48.4
63.7
A Question-Focused Multi-Factor Attention Network for Question Answering
MINIMAL(Dyn)
50.1
63.2
Efficient and Robust Question Answering from Minimal Context over Documents
FastQAExt
43.7
56.1
Making Neural QA as Simple as Possible but not Simpler
0 of 16 row(s) selected.
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