license: mit
Model Card for FIRST
FIRST is a language models trained specifically for listwise reranking tasks, leveraging the output logits of the first generated identifier to directly produce a ranked ordering of candidates. Built on the Zephyr-7B-β model, FIRST undergoes single-stage fine-tuning on a converted alphabetic version of the RankZephyr dataset, which includes RankGPT-4 reorderings of OpenAI's Ada2 orderings for 5k queries.
Model Description
- Model type: A 7B parameter GPT-like model based on Zephyr-7B-β model and further fine-tuned on task-specific listwise reranking data
- Language(s) (NLP): Primarily English
- License: MIT
- Finetuned from model: HuggingFaceH4/zephyr-7b-beta
Model Sources
- Repository: https://github.com/gangiswag/llm-reranker
- Paper: https://arxiv.org/abs/2406.15657
Evaluations
At the time of release, FIRST demonstrates superior performance across a variety of reranking datasets. The table below provides a detailed performance comparison against other LLM rerankers on the BEIR benchmark.
| Reranker | Training Data | Avg. | Climate FEVER | DBPedia | FEVER | FiQA | Hotpot QA | MS Marco | NFCorpus | NQ | Sci-docs | Sci-fact | Trec-COVID |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Rank Vicuna | GPT 3.5 | 50.7 | 28.2 | 50.0 | 81.0 | 35.9 | 73.5 | 36.7 | 33.1 | 58.6 | 18.4 | 70.5 | 71.3 |
| Rank Zephyr | GPT 3.5 + 3.5 | 53.7 | 25.6 | 50.0 | 80.1 | 42.2 | 71.6 | 42.7 | 37.7 | 65.6 | 20.5 | 76.7 | 78.4 |
| FIRST | GPT-4 | 54.3 | 26.7 | 50.9 | 81.7 | 42.2 | 74.2 | 44.4 | 37.4 | 66.4 | 20.4 | 74.6 | 78.8 |
More details can be found in the paper.
Bias, Risks, and Limitations
We forward here an excerpt from the Zephyr-7B-β model card:
Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model ([mistralai/Mistral-7B-v0.1]), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.
FIRST is trained specifically on monolingual English data, effectiveness on multilingual sets is not guaranteed.
Citation
If you find FIRST useful for your work, please consider citing our paper:
@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},
journal={arXiv preprint arXiv:2406.15657},
year={2024}
}