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Natural Language Inference on MNLI

90.8Accuracy (matched)

Vanilla

Updated 4d ago

Evaluation Results

Method
Vanilla 2020.10	90.8	90.6	-
InfoBERT 2020.10	90.7	90.4	-
InfoBERT 2020.10	90.5	90.4	-
FreeLB 2020.10	90.1	90.3	-
InfoBERT 2020.10	87.2	87.2	-
FreeLB 2020.10	86.9	86.5	-
RLLR_MIXED 2024.05	86.8	88.8	-
Vanilla 2020.10	86.7	86.4	-
RLLR 2024.05	86.6	88.9	-
InfoBERT 2020.10	86.2	86	-
RLLR 2024.05	86.1	87.8	-
RLLRMIXED 2024.05	86.1	87.5	-
SMP-S 2022.10	85.7	85.5	-
RLLR 2024.05	85.7	85.8	-
RLHF 2024.05	85.6	87.2	-
RLLR_MIXED 2024.05	85.5	85.7	-
SFT w. rat. 2024.05	85.4	87.6	-
RLHF 2024.05	85.4	87.8	-
SFT w. rat. 2024.05	85.4	87.1	-
SMP-L 2022.10	85.3	85.6	-
RLLR 2024.05	85.1	85.9	-
RLLR_MIXED 2024.05	85.1	85.9	-
RLLR 2024.05	85.1	85.9	-
RLLRMIXED 2024.05	85.1	85.9	-
BERT 2021.06	84.8	83.1	-
BERT + Aligner 2021.06	84.8	83.5	-
SFT w. rat. 2024.05	84.8	85	-
SFT 2024.05	84.7	87.5	-
BERT base 2022.10	84.5	84.9	-
RLHF 2024.05	84.5	85.3	-
CAP 2022.10	83.8	84.2	-
SMP-S 2022.10	83.7	83.6	-
RLHF 2024.05	83.6	85	-
RLLR 2024.05	83.6	84.6	-
RLHF 2024.05	83.6	85	-
SFT 2024.05	83.5	85.1	-
SFT w. rat. 2024.05	83.5	85	-
RLLR_MIXED 2024.05	83.5	84.6	-
SFT 2024.05	83.5	85.1	-
SFT w. rat. 2024.05	83.5	85	-
SMP-L 2022.10	83.1	83.1	-
SFT 2024.05	83.1	85.2	-
SFT 2024.05	82.9	84.1	-
SFT w. rat. 2024.05	82.8	84.2	-
RLHF 2024.05	82.8	84.3	-
SMP-S 2022.10	82.5	82.3	-
Movement 2022.10	82.5	82.9	-
SMP-L 2022.10	82	82.3	-
CAP 2022.10	82	82.9	-
SMP-S 2022.10	81.8	82	-
SFT 2024.05	81.8	83.9	-
Soft-Movement 2022.10	81.2	81.8	-
SMP-S 2022.10	80.9	81.1	-
SMP-L 2022.10	80.8	81.2	-
Soft-Movement 2022.10	80.7	81.1	-
SMP-L 2022.10	80.6	81	-
Movement 2022.10	80.1	80.4	-
CAP 2022.10	80.1	81.3	-
Soft-Movement 2022.10	79.5	80.1	-
Movement 2022.10	79.3	79.5	-
Soft-Movement 2022.10	79	79.6	-
L0-regularization 2022.10	78.7	79.7	-
Magnitude 2022.10	78.3	79.3	-
RLLR 2024.05	77.9	81.3	-
RLLR 2024.05	77.9	81.3	-
RLLR_MIXED 2024.05	77.8	80.7	-
RLLRMIXED 2024.05	77.8	80.7	-
RLHF 2024.05	77	80	-
RLHF 2024.05	77	80	-
Movement 2022.10	76.5	77.4	-
SFT w. rat. 2024.05	76.5	80.7	-
SFT w. rat. 2024.05	76.5	80.7	-
Movement 2022.10	76.1	76.7	-
SFT 2024.05	75.8	78.5	-
SFT 2024.05	75.8	78.5	-
RLLRMIXED 2024.05	74.7	76.4	-
RLLR 2024.05	74.6	76.7	-
RLHF 2024.05	74.2	75.6	-
SFT w. rat. 2024.05	73.4	75.1	-
SFT 2024.05	73.3	74.7	-
vanilla 2022.11	-	-	77.51
CHILD-TUNINGD 2022.11	-	-	78.01
DPS Dense 2022.11	-	-	77.59
DPS Mix 2022.11	-	-	77.75
vanilla 2022.11	-	-	66.43
CHILD-TUNINGD 2022.11	-	-	67.26
DPS Dense 2022.11	-	-	67.32
DPS Mix 2022.11	-	-	66.81
Training-free Weight Recycling (L^T_KD) 2023.05	-	-	65.2
Training-free Weight Recycling (L^W_KD) 2023.05	-	-	43.7
Demonstration learning 2023.05	-	-	46.1
Full dataset tuning 2023.05	-	-	79.9
32-shot tuning 2023.05	-	-	41.7
Tay et al. (2022) 2022.11	-	-	92.1
UD+-XXL 2022.11	-	-	92.03
Finetune 2022.12	-	-	87.11
Multitask 2022.12	-	-	87.26
MUPPET 2022.12	-	-	93.04
ColD-Fusion 2022.12	-	-	87.14
PageRank 2024.06	-	-	44.17

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