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:
- reward_model
- reward-model
- RLHF
- evaluation
- llm
- instruction
- reranking
language:
- en
pipeline_tag: text-generation
Pairwise Reward Model for LLMs (PairRM) from LLM-Blender
- Github: https://github.com/yuchenlin/LLM-Blender
- Paper: https://arxiv.org/abs/2306.02561
- Space Demo: https://huggingface.co/spaces/llm-blender/LLM-Blender
Introduction
Pairwise Reward Model (PairRM) takes an instruction and a pair of output candidates as the input, and output a score for each candidate to measure their relative quality. Unlike the other RMs that encode and score each candidate respectively, PairRM takes a pair of candidates and compares them side-by-side to indentify the subtle differences between them.
PairRM can be used to (re-)rank a list of candidate outputs and thus can be used an LLM evaluator to efficiently assess the quality of LLMs in local environment.
PairRM can also be used to enhance the decoding by best-of-n sampling
(i.e., reranking N sampled outputs).
Apart from that, one can also use PairRM to
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
Usage
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
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 |
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 & Credits
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"
}