| ---
|
| pretty_name: HUIT-Embedding TopK-PercPos hard negatives (Vietnamese)
|
| language:
|
| - vi
|
| license: other
|
| task_categories:
|
| - sentence-similarity
|
| - feature-extraction
|
| configs:
|
| - config_name: csconda-f2llm
|
| data_files:
|
| - split: train
|
| path: data/csconda-f2llm/train.parquet
|
| - config_name: csconda-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/csconda-f2llm/train.parquet
|
| - config_name: csconda-p095
|
| data_files:
|
| - split: train
|
| path: data/csconda-p095/train.parquet
|
| - config_name: csconda-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/csconda-p095/train.parquet
|
| - config_name: csconda-p099
|
| data_files:
|
| - split: train
|
| path: data/csconda-p099/train.parquet
|
| - config_name: csconda-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/csconda-p099/train.parquet
|
| - config_name: isedsc2024-f2llm
|
| data_files:
|
| - split: train
|
| path: data/isedsc2024-f2llm/train.parquet
|
| - config_name: isedsc2024-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/isedsc2024-f2llm/train.parquet
|
| - config_name: isedsc2024-p090
|
| data_files:
|
| - split: train
|
| path: data/isedsc2024-p090/train.parquet
|
| - config_name: isedsc2024-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/isedsc2024-p090/train.parquet
|
| - config_name: isedsc2024-p095
|
| data_files:
|
| - split: train
|
| path: data/isedsc2024-p095/train.parquet
|
| - config_name: isedsc2024-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/isedsc2024-p095/train.parquet
|
| - config_name: isedsc2024-p099
|
| data_files:
|
| - split: train
|
| path: data/isedsc2024-p099/train.parquet
|
| - config_name: isedsc2024-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/isedsc2024-p099/train.parquet
|
| - config_name: large_vi_legal_queries-f2llm
|
| data_files:
|
| - split: train
|
| path: data/large_vi_legal_queries-f2llm/train.parquet
|
| - config_name: large_vi_legal_queries-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/large_vi_legal_queries-f2llm/train.parquet
|
| - config_name: large_vi_legal_queries-p090
|
| data_files:
|
| - split: train
|
| path: data/large_vi_legal_queries-p090/train.parquet
|
| - config_name: large_vi_legal_queries-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/large_vi_legal_queries-p090/train.parquet
|
| - config_name: large_vi_legal_queries-p095
|
| data_files:
|
| - split: train
|
| path: data/large_vi_legal_queries-p095/train.parquet
|
| - config_name: large_vi_legal_queries-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/large_vi_legal_queries-p095/train.parquet
|
| - config_name: large_vi_legal_queries-p099
|
| data_files:
|
| - split: train
|
| path: data/large_vi_legal_queries-p099/train.parquet
|
| - config_name: large_vi_legal_queries-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/large_vi_legal_queries-p099/train.parquet
|
| - config_name: mmarco_vi-f2llm
|
| data_files:
|
| - split: train
|
| path: data/mmarco_vi-f2llm/train.parquet
|
| - config_name: mmarco_vi-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/mmarco_vi-f2llm/train.parquet
|
| - config_name: mmarco_vi-p090
|
| data_files:
|
| - split: train
|
| path: data/mmarco_vi-p090/train.parquet
|
| - config_name: mmarco_vi-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/mmarco_vi-p090/train.parquet
|
| - config_name: mmarco_vi-p095
|
| data_files:
|
| - split: train
|
| path: data/mmarco_vi-p095/train.parquet
|
| - config_name: mmarco_vi-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/mmarco_vi-p095/train.parquet
|
| - config_name: mmarco_vi-p099
|
| data_files:
|
| - split: train
|
| path: data/mmarco_vi-p099/train.parquet
|
| - config_name: mmarco_vi-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/mmarco_vi-p099/train.parquet
|
| - config_name: multi_wiki_qa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/multi_wiki_qa-f2llm/train.parquet
|
| - config_name: multi_wiki_qa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/multi_wiki_qa-f2llm/train.parquet
|
| - config_name: multi_wiki_qa-p090
|
| data_files:
|
| - split: train
|
| path: data/multi_wiki_qa-p090/train.parquet
|
| - config_name: multi_wiki_qa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/multi_wiki_qa-p090/train.parquet
|
| - config_name: multi_wiki_qa-p095
|
| data_files:
|
| - split: train
|
| path: data/multi_wiki_qa-p095/train.parquet
|
| - config_name: multi_wiki_qa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/multi_wiki_qa-p095/train.parquet
|
| - config_name: multi_wiki_qa-p099
|
| data_files:
|
| - split: train
|
| path: data/multi_wiki_qa-p099/train.parquet
|
| - config_name: multi_wiki_qa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/multi_wiki_qa-p099/train.parquet
|
| - config_name: table_markdown_retrieval-f2llm
|
| data_files:
|
| - split: train
|
| path: data/table_markdown_retrieval-f2llm/train.parquet
|
| - config_name: table_markdown_retrieval-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/table_markdown_retrieval-f2llm/train.parquet
|
| - config_name: table_markdown_retrieval-p090
|
| data_files:
|
| - split: train
|
| path: data/table_markdown_retrieval-p090/train.parquet
|
| - config_name: table_markdown_retrieval-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/table_markdown_retrieval-p090/train.parquet
|
| - config_name: table_markdown_retrieval-p095
|
| data_files:
|
| - split: train
|
| path: data/table_markdown_retrieval-p095/train.parquet
|
| - config_name: table_markdown_retrieval-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/table_markdown_retrieval-p095/train.parquet
|
| - config_name: table_markdown_retrieval-p099
|
| data_files:
|
| - split: train
|
| path: data/table_markdown_retrieval-p099/train.parquet
|
| - config_name: table_markdown_retrieval-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/table_markdown_retrieval-p099/train.parquet
|
| - config_name: vianli-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vianli-f2llm/train.parquet
|
| - config_name: vianli-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vianli-f2llm/train.parquet
|
| - config_name: vianli-p090
|
| data_files:
|
| - split: train
|
| path: data/vianli-p090/train.parquet
|
| - config_name: vianli-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vianli-p090/train.parquet
|
| - config_name: vianli-p095
|
| data_files:
|
| - split: train
|
| path: data/vianli-p095/train.parquet
|
| - config_name: vianli-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vianli-p095/train.parquet
|
| - config_name: vianli-p099
|
| data_files:
|
| - split: train
|
| path: data/vianli-p099/train.parquet
|
| - config_name: vianli-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vianli-p099/train.parquet
|
| - config_name: vicov19qa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vicov19qa-f2llm/train.parquet
|
| - config_name: vicov19qa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vicov19qa-f2llm/train.parquet
|
| - config_name: vicov19qa-p090
|
| data_files:
|
| - split: train
|
| path: data/vicov19qa-p090/train.parquet
|
| - config_name: vicov19qa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vicov19qa-p090/train.parquet
|
| - config_name: vicov19qa-p095
|
| data_files:
|
| - split: train
|
| path: data/vicov19qa-p095/train.parquet
|
| - config_name: vicov19qa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vicov19qa-p095/train.parquet
|
| - config_name: vicov19qa-p099
|
| data_files:
|
| - split: train
|
| path: data/vicov19qa-p099/train.parquet
|
| - config_name: vicov19qa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vicov19qa-p099/train.parquet
|
| - config_name: vietnamese_law_hn_mined-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vietnamese_law_hn_mined-f2llm/train.parquet
|
| - config_name: vietnamese_law_hn_mined-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vietnamese_law_hn_mined-f2llm/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p090
|
| data_files:
|
| - split: train
|
| path: data/vietnamese_law_hn_mined-p090/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vietnamese_law_hn_mined-p090/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p095
|
| data_files:
|
| - split: train
|
| path: data/vietnamese_law_hn_mined-p095/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vietnamese_law_hn_mined-p095/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p099
|
| data_files:
|
| - split: train
|
| path: data/vietnamese_law_hn_mined-p099/train.parquet
|
| - config_name: vietnamese_law_hn_mined-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vietnamese_law_hn_mined-p099/train.parquet
|
| - config_name: vifactcheck-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vifactcheck-f2llm/train.parquet
|
| - config_name: vifactcheck-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vifactcheck-f2llm/train.parquet
|
| - config_name: vifactcheck-p090
|
| data_files:
|
| - split: train
|
| path: data/vifactcheck-p090/train.parquet
|
| - config_name: vifactcheck-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vifactcheck-p090/train.parquet
|
| - config_name: vifactcheck-p095
|
| data_files:
|
| - split: train
|
| path: data/vifactcheck-p095/train.parquet
|
| - config_name: vifactcheck-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vifactcheck-p095/train.parquet
|
| - config_name: vifactcheck-p099
|
| data_files:
|
| - split: train
|
| path: data/vifactcheck-p099/train.parquet
|
| - config_name: vifactcheck-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vifactcheck-p099/train.parquet
|
| - config_name: vihealthqa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vihealthqa-f2llm/train.parquet
|
| - config_name: vihealthqa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vihealthqa-f2llm/train.parquet
|
| - config_name: vihealthqa-p090
|
| data_files:
|
| - split: train
|
| path: data/vihealthqa-p090/train.parquet
|
| - config_name: vihealthqa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vihealthqa-p090/train.parquet
|
| - config_name: vihealthqa-p095
|
| data_files:
|
| - split: train
|
| path: data/vihealthqa-p095/train.parquet
|
| - config_name: vihealthqa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vihealthqa-p095/train.parquet
|
| - config_name: vihealthqa-p099
|
| data_files:
|
| - split: train
|
| path: data/vihealthqa-p099/train.parquet
|
| - config_name: vihealthqa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vihealthqa-p099/train.parquet
|
| - config_name: vimedaqa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vimedaqa-f2llm/train.parquet
|
| - config_name: vimedaqa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimedaqa-f2llm/train.parquet
|
| - config_name: vimedaqa-p090
|
| data_files:
|
| - split: train
|
| path: data/vimedaqa-p090/train.parquet
|
| - config_name: vimedaqa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimedaqa-p090/train.parquet
|
| - config_name: vimedaqa-p095
|
| data_files:
|
| - split: train
|
| path: data/vimedaqa-p095/train.parquet
|
| - config_name: vimedaqa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimedaqa-p095/train.parquet
|
| - config_name: vimedaqa-p099
|
| data_files:
|
| - split: train
|
| path: data/vimedaqa-p099/train.parquet
|
| - config_name: vimedaqa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimedaqa-p099/train.parquet
|
| - config_name: vimqa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vimqa-f2llm/train.parquet
|
| - config_name: vimqa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimqa-f2llm/train.parquet
|
| - config_name: vimqa-p090
|
| data_files:
|
| - split: train
|
| path: data/vimqa-p090/train.parquet
|
| - config_name: vimqa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimqa-p090/train.parquet
|
| - config_name: vimqa-p095
|
| data_files:
|
| - split: train
|
| path: data/vimqa-p095/train.parquet
|
| - config_name: vimqa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimqa-p095/train.parquet
|
| - config_name: vimqa-p099
|
| data_files:
|
| - split: train
|
| path: data/vimqa-p099/train.parquet
|
| - config_name: vimqa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vimqa-p099/train.parquet
|
| - config_name: vinli-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vinli-f2llm/train.parquet
|
| - config_name: vinli-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli-f2llm/train.parquet
|
| - config_name: vinli-p090
|
| data_files:
|
| - split: train
|
| path: data/vinli-p090/train.parquet
|
| - config_name: vinli-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli-p090/train.parquet
|
| - config_name: vinli-p095
|
| data_files:
|
| - split: train
|
| path: data/vinli-p095/train.parquet
|
| - config_name: vinli-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli-p095/train.parquet
|
| - config_name: vinli-p099
|
| data_files:
|
| - split: train
|
| path: data/vinli-p099/train.parquet
|
| - config_name: vinli-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli-p099/train.parquet
|
| - config_name: vinli_simcse_v2-f2llm
|
| data_files:
|
| - split: train
|
| path: data/vinli_simcse_v2-f2llm/train.parquet
|
| - config_name: vinli_simcse_v2-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli_simcse_v2-f2llm/train.parquet
|
| - config_name: vinli_simcse_v2-p090
|
| data_files:
|
| - split: train
|
| path: data/vinli_simcse_v2-p090/train.parquet
|
| - config_name: vinli_simcse_v2-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli_simcse_v2-p090/train.parquet
|
| - config_name: vinli_simcse_v2-p095
|
| data_files:
|
| - split: train
|
| path: data/vinli_simcse_v2-p095/train.parquet
|
| - config_name: vinli_simcse_v2-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli_simcse_v2-p095/train.parquet
|
| - config_name: vinli_simcse_v2-p099
|
| data_files:
|
| - split: train
|
| path: data/vinli_simcse_v2-p099/train.parquet
|
| - config_name: vinli_simcse_v2-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/vinli_simcse_v2-p099/train.parquet
|
| - config_name: virhe4qa-f2llm
|
| data_files:
|
| - split: train
|
| path: data/virhe4qa-f2llm/train.parquet
|
| - config_name: virhe4qa-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/virhe4qa-f2llm/train.parquet
|
| - config_name: virhe4qa-p090
|
| data_files:
|
| - split: train
|
| path: data/virhe4qa-p090/train.parquet
|
| - config_name: virhe4qa-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/virhe4qa-p090/train.parquet
|
| - config_name: virhe4qa-p095
|
| data_files:
|
| - split: train
|
| path: data/virhe4qa-p095/train.parquet
|
| - config_name: virhe4qa-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/virhe4qa-p095/train.parquet
|
| - config_name: virhe4qa-p099
|
| data_files:
|
| - split: train
|
| path: data/virhe4qa-p099/train.parquet
|
| - config_name: virhe4qa-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/virhe4qa-p099/train.parquet
|
| - config_name: viwikifc-f2llm
|
| data_files:
|
| - split: train
|
| path: data/viwikifc-f2llm/train.parquet
|
| - config_name: viwikifc-f2llm-ids
|
| data_files:
|
| - split: train
|
| path: ids/viwikifc-f2llm/train.parquet
|
| - config_name: viwikifc-p090
|
| data_files:
|
| - split: train
|
| path: data/viwikifc-p090/train.parquet
|
| - config_name: viwikifc-p090-ids
|
| data_files:
|
| - split: train
|
| path: ids/viwikifc-p090/train.parquet
|
| - config_name: viwikifc-p095
|
| data_files:
|
| - split: train
|
| path: data/viwikifc-p095/train.parquet
|
| - config_name: viwikifc-p095-ids
|
| data_files:
|
| - split: train
|
| path: ids/viwikifc-p095/train.parquet
|
| - config_name: viwikifc-p099
|
| data_files:
|
| - split: train
|
| path: data/viwikifc-p099/train.parquet
|
| - config_name: viwikifc-p099-ids
|
| data_files:
|
| - split: train
|
| path: ids/viwikifc-p099/train.parquet
|
| - config_name: documents-factcheck
|
| data_files:
|
| - split: train
|
| path: documents/factcheck.parquet
|
| - config_name: documents-health
|
| data_files:
|
| - split: train
|
| path: documents/health.parquet
|
| - config_name: documents-law
|
| data_files:
|
| - split: train
|
| path: documents/law.parquet
|
| - config_name: documents-mmarco
|
| data_files:
|
| - split: train
|
| path: documents/mmarco.parquet
|
| - config_name: documents-mrc
|
| data_files:
|
| - split: train
|
| path: documents/mrc.parquet
|
| - config_name: documents-nli
|
| data_files:
|
| - split: train
|
| path: documents/nli.parquet
|
| - config_name: documents-table
|
| data_files:
|
| - split: train
|
| path: documents/table.parquet
|
| ---
|
|
|
| # HUIT-Embedding — TopK-PercPos hard negatives (Vietnamese)
|
|
|
| Hard negatives mined for [QuangDuy/supervised_data](https://huggingface.co/datasets/QuangDuy/supervised_data) with the method of [NV-Retriever](https://arxiv.org/abs/2407.15831) and [F2LLM](https://arxiv.org/abs/2510.02294). 67 configs. Generated on 2026-10-25. Candidates come from [QuangDuy/supervised_data-hn-candidates](https://huggingface.co/datasets/QuangDuy/supervised_data-hn-candidates).
|
|
|
| ## Method
|
|
|
| Positive-aware filtering: from the retrieval candidates (hardest first) keep those with score `s⁻ < p · s⁺`, where `s⁺` is the score of the positive, and take the 10 hardest. Queries left with fewer than 10 negatives are dropped. Configs `-p090`, `-p095`, `-p099` sweep `p`; `-f2llm` follows F2LLM (`p = 0.95`, additionally `s⁻ < 0.8`, and the top 5 candidates are skipped).
|
|
|
| - Retriever: [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) ([paper](https://arxiv.org/abs/2402.05672)), exact search over a per-domain corpus (law, NLI, reading comprehension, health, fact-check, tables) built from the positives and provided negatives of the source data, with evaluation overlaps removed.
|
| - Teacher: [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) ([paper](https://arxiv.org/abs/2402.03216)).
|
| - Scores in `negative_scores`: retriever cosine (teacher probability for `mmarco_vi-*`).
|
|
|
| ## Differences from the paper
|
|
|
| - Retriever is `intfloat/multilingual-e5-large` (NV-Retriever uses e5-mistral; F2LLM uses Qwen3-Embedding-0.6B).
|
| - F2LLM keeps 24 negatives; here 10, as in the other configs.
|
| - `mmarco_vi-*` configs filter on the `bge-reranker-v2-m3` scores of [hotchpotch/mmarco-hard-negatives-reranker-score](https://huggingface.co/datasets/hotchpotch/mmarco-hard-negatives-reranker-score) (64 BM25 negatives per query) instead of retriever scores. Those scores were computed on the unicamp-dl/mmarco translation while the texts here are GreenNode/msmarco-vn, so they are approximate.
|
| - Cosine scores of e5 are compressed (most candidates score 0.75–0.9), so `p = 0.90` keeps few queries.
|
|
|
| ## Two layouts
|
|
|
| - `<config>`: texts included (`query`, `positive`, `negatives`). Ready to train.
|
| - `<config>-ids`: ids and scores only; texts are in `documents-<group>` (`doc_id`, `text`). Much smaller.
|
|
|
| ## Usage
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| ds = load_dataset("QuangDuy/supervised_data-hn-percpos", "csconda-f2llm", split="train")
|
|
|
| # ids layout: join negatives with the documents table
|
| ids = load_dataset("QuangDuy/supervised_data-hn-percpos", "csconda-f2llm-ids", split="train")
|
| docs = load_dataset("QuangDuy/supervised_data-hn-percpos", "documents-health", split="train")
|
| text = dict(zip(docs["doc_id"], docs["text"]))
|
| row = ids[0]
|
| negatives = [text[d] for d in row["negative_ids"]]
|
| ```
|
|
|
| Training: sample 7 of the negatives per step (as in LightOn and NV-Retriever) and keep rows with the same `group_id` out of one batch.
|
|
|
| ## Configs
|
|
|
| | Config | Source | Queries in | Queries out | Kept | Negatives / query |
|
| |---|---|---|---|---|---|
|
| | `csconda-f2llm` | `csconda` | 19 | 1 | 5.3% | 10.0 |
|
| | `csconda-p095` | `csconda` | 19 | 10 | 52.6% | 10.0 |
|
| | `csconda-p099` | `csconda` | 19 | 16 | 84.2% | 10.0 |
|
| | `isedsc2024-f2llm` | `isedsc2024` | 7,944 | 2,204 | 27.7% | 10.0 |
|
| | `isedsc2024-p090` | `isedsc2024` | 7,944 | 5,298 | 66.7% | 10.0 |
|
| | `isedsc2024-p095` | `isedsc2024` | 7,944 | 7,424 | 93.5% | 10.0 |
|
| | `isedsc2024-p099` | `isedsc2024` | 7,944 | 7,847 | 98.8% | 10.0 |
|
| | `large_vi_legal_queries-f2llm` | `large_vi_legal_queries` | 494,312 | 275 | 0.1% | 10.0 |
|
| | `large_vi_legal_queries-p090` | `large_vi_legal_queries` | 494,312 | 710 | 0.1% | 10.0 |
|
| | `large_vi_legal_queries-p095` | `large_vi_legal_queries` | 494,312 | 173,025 | 35.0% | 10.0 |
|
| | `large_vi_legal_queries-p099` | `large_vi_legal_queries` | 494,312 | 449,418 | 90.9% | 10.0 |
|
| | `mmarco_vi-f2llm` | `mmarco_vi` | 480,133 | 456,082 | 95.0% | 10.0 |
|
| | `mmarco_vi-p090` | `mmarco_vi` | 480,133 | 461,994 | 96.2% | 10.0 |
|
| | `mmarco_vi-p095` | `mmarco_vi` | 480,133 | 467,169 | 97.3% | 10.0 |
|
| | `mmarco_vi-p099` | `mmarco_vi` | 480,133 | 472,283 | 98.4% | 10.0 |
|
| | `multi_wiki_qa-f2llm` | `multi_wiki_qa` | 4,932 | 4,640 | 94.1% | 10.0 |
|
| | `multi_wiki_qa-p090` | `multi_wiki_qa` | 4,932 | 3,610 | 73.2% | 10.0 |
|
| | `multi_wiki_qa-p095` | `multi_wiki_qa` | 4,932 | 4,643 | 94.1% | 10.0 |
|
| | `multi_wiki_qa-p099` | `multi_wiki_qa` | 4,932 | 4,879 | 98.9% | 10.0 |
|
| | `table_markdown_retrieval-f2llm` | `table_markdown_retrieval` | 125,830 | 15,322 | 12.2% | 10.0 |
|
| | `table_markdown_retrieval-p090` | `table_markdown_retrieval` | 125,830 | 4,524 | 3.6% | 10.0 |
|
| | `table_markdown_retrieval-p095` | `table_markdown_retrieval` | 125,830 | 38,531 | 30.6% | 10.0 |
|
| | `table_markdown_retrieval-p099` | `table_markdown_retrieval` | 125,830 | 89,468 | 71.1% | 10.0 |
|
| | `vianli-f2llm` | `vianli` | 1,606 | 55 | 3.4% | 10.0 |
|
| | `vianli-p090` | `vianli` | 1,606 | 122 | 7.6% | 10.0 |
|
| | `vianli-p095` | `vianli` | 1,606 | 928 | 57.8% | 10.0 |
|
| | `vianli-p099` | `vianli` | 1,606 | 1,487 | 92.6% | 10.0 |
|
| | `vicov19qa-f2llm` | `vicov19qa` | 2,911 | 72 | 2.5% | 10.0 |
|
| | `vicov19qa-p090` | `vicov19qa` | 2,911 | 146 | 5.0% | 10.0 |
|
| | `vicov19qa-p095` | `vicov19qa` | 2,911 | 1,032 | 35.5% | 10.0 |
|
| | `vicov19qa-p099` | `vicov19qa` | 2,911 | 2,322 | 79.8% | 10.0 |
|
| | `vietnamese_law_hn_mined-f2llm` | `vietnamese_law_hn_mined` | 327,190 | 1,140 | 0.3% | 10.0 |
|
| | `vietnamese_law_hn_mined-p090` | `vietnamese_law_hn_mined` | 327,190 | 4,003 | 1.2% | 10.0 |
|
| | `vietnamese_law_hn_mined-p095` | `vietnamese_law_hn_mined` | 327,190 | 151,311 | 46.2% | 10.0 |
|
| | `vietnamese_law_hn_mined-p099` | `vietnamese_law_hn_mined` | 327,190 | 308,901 | 94.4% | 10.0 |
|
| | `vifactcheck-f2llm` | `vifactcheck` | 1,595 | 708 | 44.4% | 10.0 |
|
| | `vifactcheck-p090` | `vifactcheck` | 1,595 | 1,325 | 83.1% | 10.0 |
|
| | `vifactcheck-p095` | `vifactcheck` | 1,595 | 1,567 | 98.2% | 10.0 |
|
| | `vifactcheck-p099` | `vifactcheck` | 1,595 | 1,592 | 99.8% | 10.0 |
|
| | `vihealthqa-f2llm` | `vihealthqa` | 6,582 | 16 | 0.2% | 10.0 |
|
| | `vihealthqa-p090` | `vihealthqa` | 6,582 | 307 | 4.7% | 10.0 |
|
| | `vihealthqa-p095` | `vihealthqa` | 6,582 | 3,072 | 46.7% | 10.0 |
|
| | `vihealthqa-p099` | `vihealthqa` | 6,582 | 5,430 | 82.5% | 10.0 |
|
| | `vimedaqa-f2llm` | `vimedaqa` | 31,437 | 3,459 | 11.0% | 10.0 |
|
| | `vimedaqa-p090` | `vimedaqa` | 31,437 | 5,859 | 18.6% | 10.0 |
|
| | `vimedaqa-p095` | `vimedaqa` | 31,437 | 23,037 | 73.3% | 10.0 |
|
| | `vimedaqa-p099` | `vimedaqa` | 31,437 | 29,420 | 93.6% | 10.0 |
|
| | `vimqa-f2llm` | `vimqa` | 7,158 | 7,098 | 99.2% | 10.0 |
|
| | `vimqa-p090` | `vimqa` | 7,158 | 5,493 | 76.7% | 10.0 |
|
| | `vimqa-p095` | `vimqa` | 7,158 | 7,100 | 99.2% | 10.0 |
|
| | `vimqa-p099` | `vimqa` | 7,158 | 7,157 | 100.0% | 10.0 |
|
| | `vinli-f2llm` | `vinli` | 2,107 | 53 | 2.5% | 10.0 |
|
| | `vinli-p090` | `vinli` | 2,107 | 568 | 27.0% | 10.0 |
|
| | `vinli-p095` | `vinli` | 2,107 | 1,897 | 90.0% | 10.0 |
|
| | `vinli-p099` | `vinli` | 2,107 | 2,097 | 99.5% | 10.0 |
|
| | `vinli_simcse_v2-f2llm` | `vinli_simcse_v2` | 268,871 | 462 | 0.2% | 10.0 |
|
| | `vinli_simcse_v2-p090` | `vinli_simcse_v2` | 268,871 | 27,296 | 10.2% | 10.0 |
|
| | `vinli_simcse_v2-p095` | `vinli_simcse_v2` | 268,871 | 112,588 | 41.9% | 10.0 |
|
| | `vinli_simcse_v2-p099` | `vinli_simcse_v2` | 268,871 | 198,107 | 73.7% | 10.0 |
|
| | `virhe4qa-f2llm` | `virhe4qa` | 75 | 71 | 94.7% | 10.0 |
|
| | `virhe4qa-p090` | `virhe4qa` | 75 | 37 | 49.3% | 10.0 |
|
| | `virhe4qa-p095` | `virhe4qa` | 75 | 71 | 94.7% | 10.0 |
|
| | `virhe4qa-p099` | `virhe4qa` | 75 | 75 | 100.0% | 10.0 |
|
| | `viwikifc-f2llm` | `viwikifc` | 1,802 | 1,307 | 72.5% | 10.0 |
|
| | `viwikifc-p090` | `viwikifc` | 1,802 | 1,447 | 80.3% | 10.0 |
|
| | `viwikifc-p095` | `viwikifc` | 1,802 | 1,779 | 98.7% | 10.0 |
|
| | `viwikifc-p099` | `viwikifc` | 1,802 | 1,800 | 99.9% | 10.0 |
|
|
|
| Sources without corpus hard negatives (context-dependent questions: top candidates usually answer them too): `vire4mrc`, `vlogqa`. Use them with in-batch negatives from the base dataset.
|
|
|
| ## Sources and licenses
|
|
|
| | Source | Origin | License | Label origin | Translated |
|
| |---|---|---|---|---|
|
| | `csconda` | [ura-hcmut/Vietnamese-Customer-Support-QA](https://huggingface.co/datasets/ura-hcmut/Vietnamese-Customer-Support-QA) | cc-by-nc-4.0 | human_paper | False |
|
| | `isedsc2024` | [MiuN2k3/isedsc.train.dataset](https://huggingface.co/datasets/MiuN2k3/isedsc.train.dataset) | unknown | competition | False |
|
| | `large_vi_legal_queries` | [phamson02/large-vi-legal-queries](https://huggingface.co/datasets/phamson02/large-vi-legal-queries) | cc-by-4.0 | synthetic | False |
|
| | `mmarco_vi` | [unicamp-dl/mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco), [GreenNode/msmarco-vn](https://huggingface.co/datasets/GreenNode/msmarco-vn) | apache-2.0 (repo); passages from GreenNode/msmarco-vn cc-by-sa-4.0; MS MARCO terms (non-commercial) | translated | True |
|
| | `multi_wiki_qa` | [alexandrainst/multi-wiki-qa](https://huggingface.co/datasets/alexandrainst/multi-wiki-qa) (config vi) | cc-by-nc-sa-4.0 | synthetic | False |
|
| | `table_markdown_retrieval` | [GreenNode/GreenNode-Table-Markdown-Retrieval-VN](https://huggingface.co/datasets/GreenNode/GreenNode-Table-Markdown-Retrieval-VN) | mit | synthetic | False |
|
| | `vianli` | [uitnlp/ViANLI](https://huggingface.co/datasets/uitnlp/ViANLI) | mit | human_paper | False |
|
| | `vicov19qa` | <https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_train.csv> | unknown (README: research purposes only) | human_paper | False |
|
| | `vietnamese_law_hn_mined` | [GreenNode/Vietnamese-Law-hn-mined-v2](https://huggingface.co/datasets/GreenNode/Vietnamese-Law-hn-mined-v2) | unknown | competition | False |
|
| | `vifactcheck` | [tranthaihoa/vifactcheck](https://huggingface.co/datasets/tranthaihoa/vifactcheck) | mit | human_paper | False |
|
| | `vihealthqa` | [tarudesu/ViHealthQA](https://huggingface.co/datasets/tarudesu/ViHealthQA) | unknown | human_paper | False |
|
| | `vimedaqa` | [tmnam20/ViMedAQA](https://huggingface.co/datasets/tmnam20/ViMedAQA) | unknown | human_paper | False |
|
| | `vimqa` | [nguyenlab/vimqa](https://huggingface.co/datasets/nguyenlab/vimqa) | cc-by-nc-sa-4.0 | human_paper | False |
|
| | `vinli` | [presencesw/vinli_4_label](https://huggingface.co/datasets/presencesw/vinli_4_label) | unknown | human_paper | False |
|
| | `vinli_simcse_v2` | [anti-ai/ViNLI-SimCSE-supervised_v2](https://huggingface.co/datasets/anti-ai/ViNLI-SimCSE-supervised_v2) | mit | translated | True |
|
| | `virhe4qa` | [TaiDoan123/UIT-R2GQA](https://huggingface.co/datasets/TaiDoan123/UIT-R2GQA) (config triplet) | unknown | human_paper | False |
|
| | `viwikifc` | [High-Will/ViWikiFC](https://huggingface.co/datasets/High-Will/ViWikiFC) | cc-by-4.0 | human_paper | False |
|
|
|
| ## Citations
|
|
|
| Please cite the method paper(s) first.
|
|
|
| ```bibtex
|
| @misc{moreira2025nvretrieverimprovingtextembedding,
|
| title={NV-Retriever: Improving text embedding models with effective hard-negative mining},
|
| author={Gabriel de Souza P. Moreira and Radek Osmulski and Mengyao Xu and Ronay Ak and Benedikt Schifferer and Even Oldridge},
|
| year={2025},
|
| eprint={2407.15831},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.IR},
|
| url={https://arxiv.org/abs/2407.15831},
|
| }
|
|
|
| @misc{zhang2025f2llmtechnicalreportmatching,
|
| title={F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data},
|
| author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
|
| year={2025},
|
| eprint={2510.02294},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2510.02294},
|
| }
|
|
|
| @misc{wang2024multilinguale5textembeddings,
|
| title={Multilingual E5 Text Embeddings: A Technical Report},
|
| author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
|
| year={2024},
|
| eprint={2402.05672},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2402.05672},
|
| }
|
|
|
| @misc{chen2025m3embeddingmultilingualitymultifunctionalitymultigranularity,
|
| title={M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
|
| author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
|
| year={2025},
|
| eprint={2402.03216},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2402.03216},
|
| }
|
|
|
| @inproceedings{csconda,
|
| title = {A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support},
|
| author = {Nguyen, Long S. T. and Hua, Truong P. and Nguyen, Thanh M. and Pham, Toan Q. and Ngo, Nam K. and Nguyen, An X. and Pham, Nghi D. M. and Nguyen, Nghia H. and Quan, Tho T.},
|
| editor = {Nguyen, Ngoc Thanh and Dinh Duc Anh, Vu and Kozierkiewicz, Adrianna and Nguyen Van, Sinh and Nunez, Manuel and Treur, Jan and Vossen, Gottfried},
|
| booktitle = {Advances in Computational Collective Intelligence},
|
| year = {2026},
|
| publisher = {Springer Nature Switzerland},
|
| address = {Cham},
|
| pages = {481--496},
|
| isbn = {978-3-032-10202-7}
|
| }
|
|
|
| @inproceedings{vire,
|
| title = "Which Works Best for {V}ietnamese? {A} Practical Study of Information Retrieval Methods across Domains",
|
| author = "Nguyen, Long S. T. and
|
| Quan, Tho T.",
|
| editor = "Demberg, Vera and
|
| Inui, Kentaro and
|
| Marquez, Llu{\'i}s",
|
| booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
|
| month = mar,
|
| year = "2026",
|
| address = "Rabat, Morocco",
|
| publisher = "Association for Computational Linguistics",
|
| url = "https://aclanthology.org/2026.findings-eacl.110/",
|
| doi = "10.18653/v1/2026.findings-eacl.110",
|
| pages = "2098--2119",
|
| ISBN = "979-8-89176-386-9"
|
| }
|
|
|
| @InProceedings{large-vi-legal-queries,
|
| author="Son, Pham Tien and Hieu, Nguyen Doan and An, Nguyen Dai and Sang, Dinh Viet",
|
| editor="Buntine, Wray and Fjeld, Morten and Tran, Truyen and Tran, Minh-Triet and Huynh Thi Thanh, Binh and Miyoshi, Takumi",
|
| title="Improving Vietnamese Legal Document Retrieval Using Synthetic Data",
|
| booktitle="Information and Communication Technology",
|
| year="2025",
|
| publisher="Springer Nature Singapore",
|
| address="Singapore",
|
| pages="378--393",
|
| isbn="978-981-96-4291-5"
|
| }
|
|
|
| @misc{bonifacio2022mmarcomultilingualversionms,
|
| title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
|
| author={Luiz Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and Roberto Lotufo and Rodrigo Nogueira},
|
| year={2022},
|
| eprint={2108.13897},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2108.13897},
|
| }
|
|
|
| @misc{bajaj2018msmarcohumangenerated,
|
| title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
|
| author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song and Alina Stoica and Saurabh Tiwary and Tong Wang},
|
| year={2018},
|
| eprint={1611.09268},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/1611.09268},
|
| }
|
|
|
| @misc{smart2026multiwikiqareadingcomprehensionbenchmark,
|
| title={MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages},
|
| author={Dan Saattrup Smart},
|
| year={2026},
|
| eprint={2509.04111},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2509.04111},
|
| }
|
|
|
| @inproceedings{gn-trvn,
|
| abstract = {Information retrieval often comes in plain text, lacking semi-structured text such as HTML and markdown, retrieving data that contains rich format such as table became non-trivial. In this paper, we tackle this challenge by introducing a new dataset, GreenNode Table Retrieval VN (GN-TRVN), which is collected from a massive corpus, a wide range of topics, and a longer context compared to ViQuAD2.0. To evaluate the effectiveness of our proposed dataset, we introduce two versions, M3-GN-VN and M3-GN-VN-Mixed, by fine-tuning the M3-Embedding model on this dataset. Experimental results show that our models consistently outperform the baselines, including the base model, across most evaluation criteria on various datasets such as VieQuADRetrieval, ZacLegalTextRetrieval, and GN-TRVN. In general, we release a more comprehensive dataset and two model versions that improve response performance for Vietnamese Markdown Table Retrieval.},
|
| address = {Singapore},
|
| author = {Pham, Bao Loc
|
| and Hoang, Quoc Viet
|
| and Luu, Quy Tung
|
| and Vo, Trong Thu},
|
| booktitle = {Proceedings of the Fifth International Conference on Intelligent Systems and Networks},
|
| isbn = {978-981-95-1746-6},
|
| pages = {153--163},
|
| publisher = {Springer Nature Singapore},
|
| title = {GN-TRVN: A Benchmark for Vietnamese Table Markdown Retrieval Task},
|
| year = {2026},
|
| }
|
|
|
| @article{vianli,
|
| title = {A New Benchmark Dataset and Mixture-of-Experts Language Models for Adversarial Natural Language Inference in Vietnamese},
|
| journal = {Expert Systems with Applications},
|
| pages = {130109},
|
| year = {2025},
|
| issn = {0957-4174},
|
| doi = {https://doi.org/10.1016/j.eswa.2025.130109},
|
| url = {https://www.sciencedirect.com/science/article/pii/S095741742503725X},
|
| author = {Tin Van Huynh, Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
|
| }
|
|
|
| @inproceedings{thai-etal-2022-uit,
|
| title = "{UIT}-{V}i{C}o{V}19{QA}: A Dataset for {COVID}-19 Community-based Question Answering on {V}ietnamese Language",
|
| author = "Thai, Triet and
|
| Thao-Ha, Ngan Chu and
|
| Vo, Anh and
|
| Luu, Son",
|
| editor = "Dita, Shirley and
|
| Trillanes, Arlene and
|
| Lucas, Rochelle Irene",
|
| booktitle = "Proceedings of the 36th Pacific {A}sia Conference on Language, Information and Computation",
|
| month = oct,
|
| year = "2022",
|
| address = "Manila, Philippines",
|
| publisher = "Association for Computational Linguistics",
|
| url = "https://aclanthology.org/2022.paclic-1.88/",
|
| pages = "801--810"
|
| }
|
|
|
| @inproceedings{tran2025vifactcheck,
|
| title = {ViFactCheck: A New Benchmark Dataset and Methods for Multi-Domain News Fact-Checking in Vietnamese},
|
| author = {Tran, Thai Hoa and Tran, Quang Duy and Tran, Khanh Quoc and Nguyen, Kiet Van},
|
| booktitle = {Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25)},
|
| pages = {308--316},
|
| year = {2025},
|
| publisher = {AAAI Press}
|
| }
|
|
|
| @inproceedings{tran-etal-2024-vimedaqa,
|
| title = "{V}i{M}ed{AQA}: A {V}ietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model",
|
| author = "Tran, Minh-Nam and
|
| Nguyen, Phu-Vinh and
|
| Nguyen, Long and
|
| Dinh, Dien",
|
| editor = "Fu, Xiyan and
|
| Fleisig, Eve",
|
| booktitle = "Proceedings of the 62nd Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
|
| month = aug,
|
| year = "2024",
|
| address = "Bangkok, Thailand",
|
| publisher = "Association for Computational Linguistics",
|
| url = "https://aclanthology.org/2024.acl-srw.31/",
|
| doi = "10.18653/v1/2024.acl-srw.31",
|
| pages = "252--260",
|
| ISBN = "979-8-89176-097-4"
|
| }
|
|
|
| @inproceedings{le-etal-2022-vimqa,
|
| title = "{VIMQA}: A {V}ietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering",
|
| author = "Le, Nguyen-Khang and
|
| Nguyen, Dieu-Hien and
|
| Le, Tung and
|
| Nguyen, Minh",
|
| editor = "Calzolari, Nicoletta and
|
| B{\'e}chet, Fr{\'e}d{\'e}ric and
|
| Blache, Philippe and
|
| Choukri, Khalid and
|
| Cieri, Christopher and
|
| Declerck, Thierry and
|
| Goggi, Sara and
|
| Isahara, Hitoshi and
|
| Maegaard, Bente and
|
| Mariani, Joseph and
|
| Mazo, H{\'e}l{\`e}ne and
|
| Odijk, Jan and
|
| Piperidis, Stelios",
|
| booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
|
| month = jun,
|
| year = "2022",
|
| address = "Marseille, France",
|
| publisher = "European Language Resources Association",
|
| url = "https://aclanthology.org/2022.lrec-1.700/",
|
| pages = "6521--6529"
|
| }
|
|
|
| @inproceedings{huynh-etal-2022-vinli,
|
| title = "{V}i{NLI}: A {V}ietnamese Corpus for Studies on Open-Domain Natural Language Inference",
|
| author = "Huynh, Tin Van and
|
| Nguyen, Kiet Van and
|
| Nguyen, Ngan Luu-Thuy",
|
| editor = "Calzolari, Nicoletta and
|
| Huang, Chu-Ren and
|
| Kim, Hansaem and
|
| Pustejovsky, James and
|
| Wanner, Leo and
|
| Choi, Key-Sun and
|
| Ryu, Pum-Mo and
|
| Chen, Hsin-Hsi and
|
| Donatelli, Lucia and
|
| Ji, Heng and
|
| Kurohashi, Sadao and
|
| Paggio, Patrizia and
|
| Xue, Nianwen and
|
| Kim, Seokhwan and
|
| Hahm, Younggyun and
|
| He, Zhong and
|
| Lee, Tony Kyungil and
|
| Santus, Enrico and
|
| Bond, Francis and
|
| Na, Seung-Hoon",
|
| booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
|
| month = oct,
|
| year = "2022",
|
| address = "Gyeongju, Republic of Korea",
|
| publisher = "International Committee on Computational Linguistics",
|
| url = "https://aclanthology.org/2022.coling-1.339/",
|
| pages = "3858--3872"
|
| }
|
|
|
| @inproceedings{williams-etal-2018-broad,
|
| title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
|
| author = "Williams, Adina and
|
| Nangia, Nikita and
|
| Bowman, Samuel R.",
|
| editor = "Walker, Marilyn and
|
| Ji, Heng and
|
| Stent, Amanda",
|
| booktitle = "Proceedings of the 2018 Conference of the {N}orth {A}merican Chapter of the {A}ssociation for {C}omputational {L}inguistics: Human Language Technologies, Volume 1 (Long Papers)",
|
| month = jun,
|
| year = "2018",
|
| address = "New Orleans, Louisiana",
|
| publisher = "Association for Computational Linguistics",
|
| url = "https://aclanthology.org/N18-1101/",
|
| doi = "10.18653/v1/N18-1101",
|
| pages = "1112--1122"
|
| }
|
|
|
| @misc{do2024r2gqaretrieverreadergeneratorquestionanswering,
|
| title={R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education},
|
| author={Phuc-Tinh Pham Do and Duy-Ngoc Dinh Cao and Khanh Quoc Tran and Kiet Van Nguyen},
|
| year={2024},
|
| eprint={2409.02840},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL},
|
| url={https://arxiv.org/abs/2409.02840},
|
| }
|
|
|
| @inproceedings{le-etal-2026-viwikifc,
|
| title = "{V}i{W}iki{FC}: Fact-Checking for {V}ietnamese {W}ikipedia-Based Textual Knowledge Source",
|
| author = "Le, Hung Tuan and
|
| To, Long Truong and
|
| Nguyen, Manh Trong and
|
| Nguyen, Kiet Van",
|
| editor = "Piperidis, Stelios and
|
| Bel, N{\'u}ria and
|
| van den Heuvel, Henk and
|
| Ide, Nancy and
|
| Krek, Simon and
|
| Toral, Antonio",
|
| booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
|
| month = may,
|
| year = "2026",
|
| address = "Palma de Mallorca, Spain",
|
| publisher = "ELRA Language Resource Association",
|
| url = "https://aclanthology.org/2026.lrec-1.869/",
|
| doi = "10.63317/5ew98tfds6jc",
|
| pages = "11125--11140"
|
| }
|
| ```
|
|
|
| No verified citation was found for: `isedsc2024`, `vietnamese_law_hn_mined`.
|
|
|
| ## Limitations and licenses
|
|
|
| Each source keeps its own license (column above); this collection is `license: other`. Sources that are non-commercial or have no stated license: `csconda`, `isedsc2024`, `multi_wiki_qa`, `vicov19qa`, `vietnamese_law_hn_mined`, `vihealthqa`, `vimedaqa`, `vimqa`, `vinli`, `virhe4qa`. Check the original license before any use beyond research. Q&A sources crawled from the web had person names masked with a Vietnamese NER model and phone/ID/account numbers masked by regex (`<name>`, `<phone>`, …).
|
|
|
