oaimli/ProxyCoT-HotpotQA
This is the HotpotQA data that we used in our ProxyCoT project (https://aclanthology.org/2026.acl-long.1917/), and it is based on long-context reasoning (32K-128K tokens). HotpotQA here is a new version originally from https://aclanthology.org/2026.acl-long.1917/ with extended contexts. For more details on the context extension, refer to the ProxyCoT paper. To use our dataset, please follow the code below. train_samples = load_dataset("oaimli/proxycot-hotpotqa", split="train") dev_samples =… See the full description on the dataset page: https://huggingface.co/datasets/oaimli/ProxyCoT-HotpotQA.
This is the HotpotQA data that we used in our ProxyCoT project (https://aclanthology.org/2026.acl-long.1917/), and it is based on long-context reasoning (32K-128K tokens).
HotpotQA here is a new version originally from https://aclanthology.org/2026.acl-long.1917/ with extended contexts. For more details on the context extension, refer to the ProxyCoT paper.
To use our dataset, please follow the code below.
train_samples = load_dataset("oaimli/proxycot-hotpotqa", split="train")
dev_samples = load_dataset("oaimli/proxycot-hotpotqa", split="val")
test_samples = load_dataset("oaimli/proxycot-hotpotqa", split="test")
for sample in train_samples:
question = sample["question"]
answer = sample["answer"]
metadata = sample["metadata"]
articles = sample["articles"]
instruction_proxy = sample["instruction_proxy"]
instruction_full = sample["instruction_full"]
instruction_proxy = instruction_proxy.replace("<question>", question)
instruction_proxy = instruction_proxy.replace("<articles>", "\n\n\n".join(metadata))
conversation_proxy = [{"role": "user", "content": instruction_proxy}]
instruction_full = instruction_full.replace("<question>", question)
instruction_full = instruction_full.replace("<articles>", "\n\n\n".join(articles))
conversation_full = [{"role": "user", "content": instruction_full}]