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PairRM

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metadata
license: mit
datasets:
  - openai/summarize_from_feedback
  - openai/webgpt_comparisons
  - Dahoas/instruct-synthetic-prompt-responses
  - Anthropic/hh-rlhf
  - lmsys/chatbot_arena_conversations
  - openbmb/UltraFeedback
metrics:
  - accuracy
tags:
  - pair-ranker
  - pair_ranker
  - reward_model
  - reward-model
  - pairrm
  - pair-rm
  - RLHF
language:
  - en

Inspired by DeBERTa Reward Model Series llm-blender/PairRM is pairranker version finetuned specifically as a reward model using deberta-v3-large.

Statistics

Context length

PairRanker type Source max length Candidate max length Total max length
pair-ranker 128 128 384
PairRM (This model) 1224 412 2048

Usage Example

Installation

Since PairRanker contains some custom layers and tokens. We recommend use PairRM with our llm-blender code API.

  • First install llm-blender
pip install git+https://github.com/yuchenlin/LLM-Blender.git
  • Then load pairranker with the following code:
import llm_blender
blender = llm_blender.Blender()
blender.loadranker("llm-blender/PairRM") # load PairRM

Use case 1: Compare responses (Quality Evaluator)

  • Then you can rank candidate responses with the following function
inputs = ["input1", "input2"]
candidates_texts = [["candidate1 for input1", "candidatefor input1"], ["candidate1 for input2", "candidate2 for input2"]]
ranks = blender.rank(inputs, candidates_texts, return_scores=False, batch_size=2)
# ranks is a list of ranks where ranks[i][j] represents the ranks of candidate-j for input-i
  • Directly compare two candidate responses
candidates_A = [cands[0] for cands in candidates]
candidates_B = [cands[1] for cands in candidates]
comparison_results = blender.compare(inputs, candidates_A, candidates_B)
# comparison_results is a list of bool, where element[i] denotes whether candidates_A[i] is better than candidates_B[i] for inputs[i]
  • Directly compare two multi-turn conversations given that user's query in each turn are fiexed and responses are different.
conv1 = [
    {
        "content": "hello",
        "role": "USER"
    },
    {
        "content": "<assistant response>",
        "role": "ASSISTANT"
    },
    ...
]
conv2 = [
    {
        "content": "hello",
        "role": "USER"
    },
    {
        "content": "<assistant response>",
        "role": "ASSISTANT"
    },
    ...
]
comparison_results = blender.compare_conversations([conv1], [conv2])
# comparison_results is a list of bool, where each element denotes whether all the responses in conv1 together is better than that of conv2

Use case 2: Best-of-n sampling (Decoding Enhancing)

Best-of-n Sampling, aka, rejection sampling, is a strategy to enhance the response quality by selecting the one that was ranked highest by the reward model (Learn more atOpenAI WebGPT section 3.2 and OpenAI Blog).

Best-of-n sampling is a easy way to imporve your llm power with just a few lines of code. An example of applying on zephyr is as follows.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")

inputs = [...] # your list of inputs
system_message = {
    "role": "system",
    "content": "You are a friendly chatbot who always responds in the style of a pirate",
}
messages = [
    [   
        system_message,
        {"role": "user", "content": _input},
    ]
    for _input in zip(inputs)
]
prompts = [tokenizer.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages]
outputs = blender.best_of_n_generate(model, tokenizer, prompts, n=10)
print("### Prompt:")
print(prompts[0])
print("### best-of-n generations:")
print(outputs[0])

Use case 3: RLHF

PairRM has been trained on various high-quality and large-scale dataset with human preference annotations and exhibits great correlation with human preferences with an extremly small model size (0.4B), approching the performance of GPT-4. We believe PairRM will power the alignment of LLM in an efficient and effective way. With a blender.compare() function, you can easily apply PairRM to poopular RLHF toolkits like trl.

🔥 Check more details on our example jupyter notebook usage: blender_usage.ipynb

Learn more in our LLM-Blender Github README.md

Performance

PairRM has been trained on various high-quality and large-scale dataset with human preference annotations and exhibits great correlation with human preferences with an extremly small model size (0.4B), approching the performance of GPT-4.

We test the pairwise comparison on

Auto-J Pairwise test data performance

Model Summ Exam Code Rewriting Crea W Func W Comm NLP Overall
Closed -source Models
ChatGPT 33.3 40.3 36.6 31.6 48.2 40.4 47.6 45.8 42.7
Claude -2 30.6 36.1 41.7 34.2 48.1 42.5 40.6 48.5 42.4
GPT -4 59.7 51.4 69.2 58.3 66.7 60.4 58.3 65.2 61.9
Open -source Models
SteamSHP 33.3 29.2 26.7 33.3 40.7 31.3 51.4 51.9 40.6
PandaLM 29.2 33.3 31.7 23.3 43.5 32.9 44.8 48.9 38.9
LLaMA -2-Chat -13B 20.8 27.8 19.2 20 31.5 27.5 35.8 31.8 29
Vicuna -13B-v1.5 30.6 23.6 35 28.3 36.1 37.5 45.5 39.8 37.3
WizardLM -13B-v1.2 22.2 20.8 32.5 19.2 28.7 25.4 29.2 33 27.8
LLAMA -2-chat -70B 34.7 33.3 36.7 35.8 51.4 54.2 47.2 47.7 45.9
AUTO -J (13b) 45.8 38.9 59.2 47.5 54.6 57.1 58 57.6 54.8
PairRM (0.4b) 56.94 52.78 58.33 55.83 61.57 59.17 57.64 62.5 59.05

HHH-Alignment and MT-bench human judgements

Evaluator LM HHH ALIGNMENT MT BENCH HUMAN JUDG .
Help . Harm . Hon . Other Total Avg . Human Preference
RANDOM 50 50 50 50 50 34.26
STANFORDNLP REWARD MODEL 69.49 60.34 52.46 51.16 58.82 44.79
ALMOST REWARD MODEL 74.58 67.24 78.69 86.05 76.02 49.9
LLAMA2 -CHAT 7B 66.1 81.03 70.49 74.42 72.85 51.78
LLAMA2 -CHAT 13B 74.58 87.93 55.74 79.07 73.76 52.34
LLAMA2 -CHAT 70B 66.1 89.66 67.21 74.42 74.21 53.67
LLAMA2 -CHAT 13B+COARSE . 68.74 68.97 65.57 67.44 67.42 46.89
GPT -3.5-TURBO -0613 76.27 87.93 67.21 86.05 78.73 57.12
PROMETHEUS 7B 69.49 84.48 78.69 90.7 80.09 55.14
PROMETHEUS 13B 81.36 82.76 75.41 76.74 79.19 57.72
PairRM (0.4b) 84.75 84.48 80.33 90.7 84.62 59
GPT -4-0613 91.53 93.1 85.25 83.72 88.69 63.87

While PairRM is a extremely small model (0.4B) based on deberta, the pairwise comparison aggrement performance approches GPT-4's performance!

Two reasons to attribute:

  • Our PairRM specically designed model arch for pairwise comparison through bidirectional attention (See LLM-blender paper for more details)
  • The high-quality and large-scale human preference annotation data it was train on (see training dataset list on this hugging face page)

Citation

If you are using PairRM in your research, please cite LLM-blender.

@inproceedings{llm-blender-2023,
    title = "LLM-Blender: Ensembling Large Language Models with Pairwise Comparison and Generative Fusion",
    author = "Jiang, Dongfu and Ren, Xiang and Lin, Bill Yuchen",
    booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023)",
    year = "2023"
}