---
license: mit
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---
# Model Card for `first_mistral`
`first_mistral` is a language model trained to act as a listwise reranker, decoding from the first-token logits only to improve efficiency while maintaining effectiveness.
`first_mistral` is built on Mistral-7B-Instruct-v0.3, following [FIRST](https://arxiv.org/abs/2406.15657)'s strategy, trained using 40K GPT-4 labeled rerank instances from RankZephyr. 
More details can be found in the paper.

## Model description

- **Model type:** A 7B parameter listwise reranker fine-tuned from Mistral-7B-Instruct-v0.3
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Fine-tuned from model:** [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)

### Model Sources

- **Repository:** https://github.com/castorini/rank_llm
- **Paper:** https://arxiv.org/abs/2411.05508

## Evaluation

At the time of release, `first_mistral` outperforms the original FIRST implementation on most subsets of the BEIR benchmark.
More details that compare other LLM rerankers on more datasets can be found in the paper.

| Dataset        | FIRST (original) | first_mistral |
|----------------|-------------|--------------|
| climate-fever  | **0.2672**      | 0.2417   |
| dbpedia-entity | **0.5092**  | 0.5033       |
| fever          | 0.8164      | **0.8413**   |
| fiqa           | 0.4223      | **0.4778**   |
| hotpotqa       | 0.7424      | **0.7705**   |
| msmarco        | 0.4425      | **0.4512**   |
| nfcorpus       | 0.3725  | **0.3816**       |
| nq             | 0.6638  | **0.6985**       |
| scidocs        | 0.2047  | **0.2110**       |
| scifact        | 0.7459      | **0.7769**   |
| trec-covid     | **0.7913**  | 0.7666       |
| Average        | 0.5435      | **0.5564**   |

## Citation
If you find `first_mistral` useful for your work, please consider citing:

```
@ARTICLE{chen2024firstrepro,
  title   = title={An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking},
  author  = {Zijian Chen and Ronak Pradeep and Jimmy Lin},
  year    = {2024},
  journal = {arXiv:2411.05508}
}
```

If you would like to cite the FIRST methodology, please consider citing:

```
@ARTICLE{reddy2024first,
  title   = {FIRST: Faster Improved Listwise Reranking with Single Token Decoding},
  author  = {Reddy, Revanth Gangi and Doo, JaeHyeok and Xu, Yifei and Sultan, Md Arafat and Swain, Deevya and Sil, Avirup and Ji, Heng},
  year    = {2024}
  journal = {arXiv:2406.15657},
}
```