AdarshSingh7647/Eklav-Reranker-CotGen-Data
HETU-PassageReranking-CotGen-Data Training data for the HETU (Hints Enable True Understanding) paper. Task: passage reranking (BRIGHT / NevIR benchmarks) Method: CotGen Examples: 381,934 train / held-out val Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory. Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt (human turn) and the model's full chain-of-thought plus true/false judgment (gpt turn)… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-CotGen-Data.
HETU-PassageReranking-CotGen-Data
Training data for the HETU (Hints Enable True Understanding) paper.
- Task: passage reranking (BRIGHT / NevIR benchmarks)
- Method: CotGen
- Examples: 381,934 train / held-out val
- Format: ShareGPT (
system+conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt (human turn) and the model's full chain-of-thought plus true/false judgment (gpt turn), with loss computed over the entire gpt turn.
Files:
train.json-- training splitval.json-- held-out validation split
See the HETU paper for full dataset construction methodology, and the corresponding HETU-*-PassageReranking-CotGen model repos for checkpoints trained on this data.