Model Card for Llama-Prometheus

Llama-Prometheus is a English evaluation model introduced as part of the CIA Suite to assess multilingual Large Language Models (LLMs).

Llama-Prometheus is fine-tuned on the Feedback-Collection dataset using the same setup as Prometheus 2, but using the Llama-3.1-8B-Instruct as the base model. All FFT models and LoRA weights part of CIA Suite are available here.

Model Details

Model Description

• Model type: Evaluator Language model
• Language(s) (NLP): English
• Related Models: Hercule Models
• Resources for more information:
  • Research paper
  • GitHub Repo

Prompt Format

We’ve developed wrapper functions and classes to make it easy to work with Hercule. Check them out on our github repository – we highly recommend using them!

If you only need to use the model for your specific use case, please follow the prompt format provided below.

Reference Guided Direct Assessment

The model expects four input components: an evaluation instruction, a response to evaluate, a scoring rubric, and a reference answer. Use the prompt format provided below, ensuring that you include the instruction, response, reference answer, evaluation criteria, and a detailed score rubric for each score from 1 to 5.

After running inference, the output will include feedback and a score, separated by the phrase [RESULT].

###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\"
4. Please do not generate any other opening, closing, and explanations.

###The instruction to evaluate:
{instruction}

###Response to evaluate:
{response}

###Reference Answer (Score 5):
{reference_answer}

###Score Rubrics:
[{criteria}]
Score 1: {score1_rubric}
Score 2: {score2_rubric}
Score 3: {score3_rubric}
Score 4: {score4_rubric}
Score 5: {score5_rubric}

###Feedback: 

We use the same evaluation prompt as used in Prometheus 2.

Links for Reference

• Repository: https://github.com/AI4Bharat/CIA
• Paper: https://arxiv.org/abs/2410.13394
• Point of Contact: sumanthd@cse.iitm.ac.in, safikhan@ai4bharat.org

Citation

If you find the following model helpful, please consider citing our paper!

BibTeX:

@article{doddapaneni2024crosslingual,
  title   = {Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs},
  author  = {Sumanth Doddapaneni and Mohammed Safi Ur Rahman Khan and Dilip Venkatesh and Raj Dabre and Anoop Kunchukuttan and Mitesh M. Khapra},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2410.13394}
}