Model Card for Deita Complexity Scorer

Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs).

Deita Complexity Scorer is a tool for automatically annotating the Instruction Complexity of SFT data.

Model description

Model type: Model fine tuned to automatically annotate the Instruction Complexity
Language(s) (NLP): Primarily English
Finetuned from model: Llama-1-13b-hf

Model Sources

Repository: https://github.com/hkust-nlp/deita
Model Family: Other models and the dataset are found in the Deita collection.

Performance

Model	Align	Data Size	MT-Bench	AlpacaEval(%)	OpenLLM (Avg.)
Proprietary Models
GPT-4-Turbo	?	--	9.32	97.70	--
GPT-4	SFT + PPO	--	8.99	95.03	--
Claude-2	SFT + PPO	--	8.06	91.36	--
GPT-3.5-turbo	SFT + PPO	--	7.94	89.37	--
Open-sourced Models based on LLaMA-1-13B
LIMA	SFT	1K SFT	4.29	41.98	59.82
WizardLM-13B	SFT	70K SFT	6.35	75.31	58.96
Vicuna-13B-v1.3	SFT	125K SFT	6.39	82.11	60.01
Random	SFT	10K SFT	6.03	71.52	60.14
DEITA-LLaMA1-13B-v1.0-sft	SFT	10K SFT	6.60	78.01	64.27
Open-sourced Models based on LLaMA-2-13B
Tulu-2-13B	SFT	326K SFT	6.70	78.90	--
Tulu-2-13B+DPO	SFT + DPO	326K SFT + 60K DPO	7.00	89.50	--
LLaMA2-13B-Chat	SFT + PPO	--	6.65	81.09	--
WizardLM-13B-v1.2	SFT	>70K SFT	7.09	89.17	--
Vicuna-13B-v1.5	SFT	125K SFT	6.57	78.80	61.63
Random	SFT	10K SFT	5.78	65.19	61.32
DEITA-LLaMA2-13B-v1.0-sft	SFT	10K SFT	6.79	81.09	62.71
Open-sourced Models based on Mistral-7B
Mistral-7B-Instruct-v0.1	--	--	6.84	69.65	60.45
Zephyr-7B-sft	SFT	200K SFT	5.32	75.12	60.93
$\text{Zephyr-7B-}\beta$	SFT + DPO	200K SFT + 60K DPO	7.34	90.60	66.36
OpenChat-3.5	C-RLFT	>> 70K C-RLFT	7.81	88.51	--
Starling-7B	C-RLFT + APA	>>70K C-RLFT + 183K APA	8.09	91.99	--
Random	SFT	10K SFT	5.89	56.90	61.72
DEITA-7B-v1.0-sft (6K)	SFT	6K SFT	7.22	80.78	64.94
DEITA-7B-v1.0-sft (10K)	SFT	10K SFT	7.32	81.67	64.00
DEITA-7B-v1.0	SFT + DPO	6K SFT + 10K DPO	7.55	90.06	69.86

Usage

Please use the following format to score the complexity of the Instruction:

from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
model_name = "hkust-nlp/deita-complexity-scorer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


def infer_complexity(model, tokenizer, input_text):
    complexity_template = ("You are a helpful assistant. Please identify the complexity score of the following user query. \n##Query: {instruction}  \n##Complexity: ")
    user_input = complexity_template.format(instruction=input_text)
    input_ids = tokenizer.encode(user_input, return_tensors="pt")
    max_length = 512
    outputs = model.generate(input_ids, max_length=512, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
    logprobs_list = outputs.scores[0][0]
    score_logits = []
    id2score = {
        29896: "1",
        29906: "2",
        29941: "3",
        29946: "4",
        29945: "5",
        29953: "6"
    }
    score_template = np.array([1,2,3,4,5,6])
    for k in id2score:
        score_logits.append(logprobs_list[k])
    score_logits = np.array(score_logits)
    score_npy = softmax(score_logits, axis=0)
    score_npy = score_npy * score_template

    score_npy = np.sum(score_npy, axis=0)
    return score_npy

# example input
input_text = "write a performance review for a junior data scientist"
complexity_score = infer_complexity(model, tokenizer, input_text)

print(complexity_score)

Citation

If you find the content of this project helpful, please cite our paper as follows:

@misc{liu2023what,
      title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning}, 
      author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He},
      year={2023},
      eprint={2312.15685},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

hkust-nlp
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deita-complexity-scorer