ContextualAI/LMUnit-llama3.1-70b

LMUnit: Fine-grained Evaluation with Natural Language Unit Tests

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LMUnit is a state-of-the-art language model that is optimized for evaluating natural language unit tests. It takes three inputs: a prompt, a response, and a unit test. It then produces a continuous score between 1 and 5 where higher scores indicate that the response better satisfies the unit test criteria.

The LMUnit model achieves leading averaged performance across preference, direct scoring, and fine-grained unit test evaluation tasks, as measured by FLASK and BiGGen Bench, and performs on par with frontier models for coarse evaluation of long-form responses (per LFQA). The model also demonstrates exceptional alignment with human preferences, ranking in the top 5 of the RewardBench benchmark with 93.5% accuracy and in top #2 of RewardBench2 with 82.1% accuracy.

For more details, please check out the blogpost or the paper.

Model Details

LMUnit is highly performant and versatile because of key methodologies in its training approach:

• Multi-Objective Training: The model simultaneously learns from multiple evaluation signals, including pairwise comparisons between responses, direct quality ratings, and specialized criteria-based judgments.
• Synthetic Data Generation: We developed a sophisticated pipeline to generate training data that captures nuanced, fine-grained evaluation criteria and subtle quality distinctions between responses across a wide range of use cases and scenarios.
• Importance Weighting: We demonstrate that adjusting unit test weights to reflect the relative importance of different criteria achieves results that better align with human preferences.

Model Description

• Developed by: Contextual AI
• Language(s) (NLP): English
• Finetuned from model: Llama-3.1-70B-Instruct

Model Sources

• Repository: https://github.com/ContextualAI/LMUnit
• Paper: https://arxiv.org/abs/2412.13091

🚀 Model Quick Start

Installation

pip install lmunit

Basic Usage

from lmunit import LMUnit
from vllm import SamplingParams

# Initialize LMUnit
model = LMUnit(
    model_path="ContextualAI/LMUnit-llama3.1-70b",
    tp_size=4
)

# Define evaluation
query = "What is the capital of France?"
response = "Paris"
unit_test = "Does the response correctly identify the capital city?"

# Generate score
sampling_params = SamplingParams(temperature=0.0, max_tokens=10, logprobs=20)
prompt = f"Query: {query}\n\nResponse: {response}\n\nUnit Test: {unit_test}"
output = model.generate(prompt, sampling_params)
print(output)

Alternative: Using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model
tokenizer = AutoTokenizer.from_pretrained("ContextualAI/LMUnit-llama3.1-70b")
model = AutoModelForCausalLM.from_pretrained("ContextualAI/LMUnit-llama3.1-70b")

# Prepare prompt
query = "What is the capital of France?"
response = "Paris"
unit_test = "Does the response correctly identify the capital city?"
content = f"Query: {query}\n\nResponse: {response}\n\nUnit Test: {unit_test}"

messages = [{"role": "user", "content": content}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=40)
result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])
print(result)

For more examples, see our GitHub repository.

Evaluation - Results

Model	Flask	BiGGen-Bench	Human-Internal	InfoBench	RB	LFQA	RB2
LMUnit-LLaMA-3.1-70B	72.03	67.69	93.63	89.00	91.56	76.15	80.5
LMUnit-Qwen2.5-72B	73.85	69.56	94.44	88.67	91.13	73.85	82.1

Citation

If you find our work helpful, feel free to cite our paper:

@inproceedings{saadfalcon2025lmunit,
      title={{LMUnit}: Fine-grained Evaluation with Natural Language Unit Tests}, 
      author={Jon Saad-Falcon and Rajan Vivek and William Berrios and Nandita Shankar Naik and Matija Franklin and Bertie Vidgen and Amanpreet Singh and Douwe Kiela and Shikib Mehri},
      booktitle={Findings of the Association for Computational Linguistics: EMNLP 2025},
      year={2025},
      url={https://arxiv.org/abs/2412.13091}
}