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--- |
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tags: |
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- information retrieval |
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- reranking |
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license: apache-2.0 |
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--- |
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# Model Card for NQ Question Encoder in Re2G |
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# Model Details |
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> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. |
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<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_cards/Re2G_Arch2.png" width="100%"> |
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## Training, Evaluation and Inference |
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The code for training, evaluation and inference is in our github in the [re2g branch](https://github.com/IBM/kgi-slot-filling/tree/re2g). |
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## Usage |
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The best way to use the model is by adapting the [dpr_apply.py](https://github.com/IBM/kgi-slot-filling/blob/re2g/dpr/dpr_apply.py) |
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## Citation |
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``` |
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@inproceedings{glass-etal-2022-re2g, |
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title = "{R}e2{G}: Retrieve, Rerank, Generate", |
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author = "Glass, Michael and |
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Rossiello, Gaetano and |
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Chowdhury, Md Faisal Mahbub and |
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Naik, Ankita and |
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Cai, Pengshan and |
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Gliozzo, Alfio", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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address = "Seattle, United States", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-main.194", |
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doi = "10.18653/v1/2022.naacl-main.194", |
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pages = "2701--2715", |
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abstract = "As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9{\%} to 34{\%} over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source.", |
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} |
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``` |
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## Model Description |
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The model creators note in the [associated paper](https://aclanthology.org/2022.naacl-main.194.pdf): |
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> As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source. |
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- **Developed by:** IBM |
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- **Shared by [Optional]:** IBM |
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- **Model type:** Query/Passage Reranker |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Parent Model:** [dpr-question_encoder-multiset-base](https://huggingface.co/facebook/dpr-question_encoder-multiset-base) |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/IBM/kgi-slot-filling) |
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- [Associated Paper](https://aclanthology.org/2022.naacl-main.194.pdf) |
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# Uses |
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## Direct Use |
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This model can be used for the task of encoding a question to a vector to be used as a query into an Approximate Nearest Neighbors index. It must be used in combination with a context encoder that encodes passages to a vector and indexes them. |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@inproceedings{glass-etal-2022-re2g, |
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title = "{R}e2{G}: Retrieve, Rerank, Generate", |
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author = "Glass, Michael and |
|
Rossiello, Gaetano and |
|
Chowdhury, Md Faisal Mahbub and |
|
Naik, Ankita and |
|
Cai, Pengshan and |
|
Gliozzo, Alfio", |
|
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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address = "Seattle, United States", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-main.194", |
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doi = "10.18653/v1/2022.naacl-main.194", |
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pages = "2701--2715", |
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abstract = "As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9{\%} to 34{\%} over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source.", |
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} |
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``` |
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