SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 8 provides a detailed comparison of zero-shot and few-shot performance metrics for various Large Language Models (LLMs)—GPT NeoX 20B, OPT 66B, and BLOOM—across multiple datasets. These metrics, obtained from a previous paper, serve as a valuable baseline for understanding the capabilities of these models in both zero-shot and few-shot scenarios under the FLARE benchmark.
Table 8: The zero-shot and few-shot performance of different LLMs on the FLARE benchmark. Results are referenced from previous paper.
Dataset	Metrics	GPT NeoX 20B	OPT 66B	BLOOM
FPB	F1	0.45*	0.49*	0.50*
Acc	0.38	-	-
FiQA-SA	F1	0.51*	0.52*	0.53*
Headlines	AvgF1	0.73*	0.79*	0.77*
NER	EntityF1	0.61*	0.57*	0.56*
FinQA	EmAcc	0.00	-	-
ConvFinQA	EmAcc	0.28*	0.30*	0.36*
BigData22	Acc	0.41	-	-
MCC	0.08	-	-
ACL18	Acc	0.35	-	-
MCC	0.00	-	-
CIKM18	Acc	0.25	-	-
MCC	-0.12	-	-
EDTSUM	Rouge-1	0.02	-	-
Rouge-2	0.01	-	-
Rouge-N	0.02	-	-
BertScore	0.48	-	-
BartScore	-5.72	-	-
ECTSUM	Rouge-1	0.00	-	-
Rouge-2	0.00	-	-
Rouge-N	0.00	-	-
BertScore	0.00	-	-
BartScore	-5.18	-	-
German	F1	0.17	-	-
MCC	0.02	-	-
Australian	F1	0.00	-	-
MCC	0.00	-	-
FOMC	F1	0.37	-	-
Acc	0.27	-	-