---
tags:
- eklav
- passagereranking
- answer-only
---

# Eklav-Reranker-AnswerOnly-Data

Training data for the **Eklav** paper.

- **Task:** passage reranking (BRIGHT / NevIR benchmarks)
- **Method:** Answer-only (no reasoning of any kind -- the no-CoT floor)
- **Examples:** 381,934 train / 3,857 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 a bare `true`/`false` judgment (gpt turn) -- no hint, no chain-of-thought, no `<think>` block anywhere. Loss is computed over the entire (one-token) gpt turn.

Built from the same source query/passage/judgment triples as `Eklav-Reranker-Data` (Eklav method) and `Eklav-Reranker-CotGen-Data` (std-SFT method), with the teacher reasoning removed entirely rather than hinted at or reproduced -- the train/val split (381,934 / 3,857, seed 42) matches those two repos exactly so all three methods are directly comparable example-for-example.

Files:
- `train.json` -- training split
- `val.json` -- held-out validation split

See the Eklav paper for full dataset construction methodology, and the corresponding `Eklav-*-PassageReranking-AnswerOnly` model repos for checkpoints trained on this data.