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sunnweiwei · Jul 10, 2023 · 259507b · 259507b
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-# AmbigPrompt
+# AmbigPrompt
+
+Code for paper [Answering Ambiguous Questions via Iterative Prompting](https://aclanthology.org/2023.acl-long.424/).
+
+![AmbigPrompt](assets/model.jpg)
+
+## Prepare Data
+Download the Wikipedia text splitted by 100 words from [DPR](https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz), put it under `data/wikipedia/psgs_w100.tsv`, and run the following command to build Wikipedia's redis cache.
+```bash
+python tool.py
+```
+
+Download NQ and AmbigNQ data from [shmsw25/AmbigQA](https://github.com/shmsw25/AmbigQA) and put it under `data/nq` and `data/ambig`
+
+## Train Retrieval Model
+
+Training a dense passage retrieval model using [luyug/dense](https://github.com/luyug/Dense)
+```bash
+python train_dense.py
+```
+
+Encode the passages and perform passage retrieval using [Faiss](https://github.com/facebookresearch/faiss).
+```bash
+python inference_dense.py
+```
+
+This step obtains QA data that includes the 100 retrieved passages, like [`data/ambig/dev.json`](https://github.com/sunnweiwei/AmbigPrompt/blob/main/data/ambig/dev.json).
+
+## Train QA Model
+Download the pre-trained checkpoint from [facebookresearch/FiD](https://github.com/facebookresearch/FiD).
+
+Train the prompting model and QA model on multi-answer QA data:
+```bash
+accelerate launch train.py --data_path data/ambig/train.json --save_path out/ambig/model --do_train true --do_eval false
+```
+
+Evaluate the model:
+```bash
+accelerate launch train.py --data_path data/ambig/dev.json --checkpoint out/ambig/model/9.py --do_train false --do_eval true
+```
+
+## Cite
+```
+@inproceedings{Sun2023IsCG,
+  title={Answering Ambiguous Questions via Iterative Prompting},
+  author={Weiwei Sun and Hengyi Cai and Hongshen Chen and Pengjie Ren and Zhumin Chen and Maarten de Rijke and Zhaochun Ren},
+  booktitle={ACL},
+  year={2023},
+}
+```
+
+