paper-with-me

Cross-Lingual Question Answering 벤치마크

Cross-Lingual Question Answering on TyDiQA-GoldP

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EM

42.8 52.58 62.35 72.12 81.9 2020-10 2026-09 Decoupled — 42.8 (2020-10-24) ByT5 (fine-tuned) — 81.9 (2021-05-28) ByT5 XXL — 60.0 (2021-05-28) PaLM-540B (CoT) — 52.9 (2022-04-05) U-PaLM 62B (fine-tuned) — 78.4 (2022-10-20) Flan-U-PaLM 540B (direct-prompting) — 68.3 (2022-10-20) Flan-PaLM 540B (direct-prompting) — 67.8 (2022-10-20) U-PaLM-540B (CoT) — 54.6 (2022-10-20) Decoupled — 42.8 (2020-10-24) ByT5 (fine-tuned) — 81.9 (2021-05-28)
RankModel EMF1 PaperCodeYear
1 ByT5 (fine-tuned) 81.9– ByT5: Towards a token-free future with pre-trained byte-to-byte models huggingface/transformers · google-research/byt5 · ufal/multilexnorm2021 · +2 2021
2 U-PaLM 62B (fine-tuned) 78.488.5 Transcending Scaling Laws with 0.1% Extra Compute 2022
3 Flan-U-PaLM 540B (direct-prompting) 68.3– Scaling Instruction-Finetuned Language Models google-research/flan · declare-lab/flan-alpaca · formulamonks/llm-benchmarker-suite · +6 2022
4 Flan-PaLM 540B (direct-prompting) 67.8– Scaling Instruction-Finetuned Language Models google-research/flan · declare-lab/flan-alpaca · formulamonks/llm-benchmarker-suite · +6 2022
5 ByT5 XXL 60.075.3 ByT5: Towards a token-free future with pre-trained byte-to-byte models huggingface/transformers · google-research/byt5 · ufal/multilexnorm2021 · +2 2021
6 U-PaLM-540B (CoT) 54.6– Transcending Scaling Laws with 0.1% Extra Compute 2022
7 PaLM-540B (CoT) 52.9– PaLM: Scaling Language Modeling with Pathways lucidrains/CoCa-pytorch · lucidrains/PaLM-pytorch · google/paxml · +4 2022
8 Decoupled 42.858.1 Rethinking embedding coupling in pre-trained language models PaddlePaddle/PaddleNLP · yangyucheng000/University · yangyucheng000/University · +1 2020
9 PaLM 2-L (one-shot) –73.6 PaLM 2 Technical Report eternityyw/tram-benchmark 2023
10 PaLM 2-S (one-shot) –73.3 PaLM 2 Technical Report eternityyw/tram-benchmark 2023
11 PaLM 2-M (one-shot) –73.3 PaLM 2 Technical Report eternityyw/tram-benchmark 2023
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