Question Answering On Drop Test

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	F1	Paper Title	Repository
QDGAT (ensemble)	88.38	Question Directed Graph Attention Network for Numerical Reasoning over Text	-
PaLM 2 (few-shot)	85.0	PaLM 2 Technical Report
GPT-3 175B (few-shot, k=32)	36.5	Language Models are Few-Shot Learners
GPT-4 (few-shot, k=3)	80.9	GPT-4 Technical Report
NeRd	81.71	Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension	-
NumNet	67.97	NumNet: Machine Reading Comprehension with Numerical Reasoning
Orca 2-7B	60.26	Orca 2: Teaching Small Language Models How to Reason	-
GPT 3.5 (few-shot, k=3)	64.1	GPT-4 Technical Report
Orca 2-13B	57.97	Orca 2: Teaching Small Language Models How to Reason	-
POET	87.6	Reasoning Like Program Executors
BERT	32.7	DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
BERT+Calculator (ensemble)	81.78	Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension	-
NAQA Net	47.01	DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
GenBERT (+ND+TD)	72.4	Injecting Numerical Reasoning Skills into Language Models
MTMSN Large	79.88	A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
TASE-BERT	80.7	A Simple and Effective Model for Answering Multi-span Questions

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