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--- |
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license: mit |
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datasets: |
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- openai/summarize_from_feedback |
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- openai/webgpt_comparisons |
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- Dahoas/instruct-synthetic-prompt-responses |
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- Anthropic/hh-rlhf |
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- lmsys/chatbot_arena_conversations |
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- openbmb/UltraFeedback |
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metrics: |
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- accuracy |
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tags: |
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- pair-ranker |
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- pair_ranker |
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- reward_model |
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- reward-model |
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- pairrm |
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- pair-rm |
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- RLHF |
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language: |
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- en |
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--- |
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Inspired by [DeBERTa Reward Model Series](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2) |
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`llm-blender/PairRM` is pairranker version finetuned specifically as a reward model using deberta-v3-large. |
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- Github: [https://github.com/yuchenlin/LLM-Blender](https://github.com/yuchenlin/LLM-Blender) |
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- Paper: [https://arxiv.org/abs/2306.02561](https://arxiv.org/abs/2306.02561) |
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- Space Demo: [https://huggingface.co/spaces/llm-blender/LLM-Blender](https://huggingface.co/spaces/llm-blender/LLM-Blender) |
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## Usage Example |
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### Installation |
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Since PairRanker contains some custom layers and tokens. We recommend use PairRM with our llm-blender code API. |
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- First install `llm-blender` |
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```bash |
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pip install git+https://github.com/yuchenlin/LLM-Blender.git |
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``` |
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- Then load pairranker with the following code: |
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```python |
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import llm_blender |
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blender = llm_blender.Blender() |
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blender.loadranker("llm-blender/PairRM") # load PairRM |
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``` |
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### Use case 1: Compare responses (Quality Evaluator) |
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- Then you can rank candidate responses with the following function |
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```python |
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inputs = ["input1", "input2"] |
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candidates_texts = [["candidate1 for input1", "candidatefor input1"], ["candidate1 for input2", "candidate2 for input2"]] |
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ranks = blender.rank(inputs, candidates_texts, return_scores=False, batch_size=2) |
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# ranks is a list of ranks where ranks[i][j] represents the ranks of candidate-j for input-i |
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``` |
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- Directly compare two candidate responses |
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```python |
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candidates_A = [cands[0] for cands in candidates] |
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candidates_B = [cands[1] for cands in candidates] |
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comparison_results = blender.compare(inputs, candidates_A, candidates_B) |
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# comparison_results is a list of bool, where element[i] denotes whether candidates_A[i] is better than candidates_B[i] for inputs[i] |
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``` |
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- Directly compare two multi-turn conversations given that user's query in each turn are fiexed and responses are different. |
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```python |
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conv1 = [ |
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{ |
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"content": "hello", |
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"role": "USER" |
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}, |
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{ |
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"content": "<assistant response>", |
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"role": "ASSISTANT" |
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}, |
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... |
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] |
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conv2 = [ |
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{ |
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"content": "hello", |
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"role": "USER" |
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}, |
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{ |
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"content": "<assistant response>", |
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"role": "ASSISTANT" |
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}, |
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... |
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] |
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comparison_results = blender.compare_conversations([conv1], [conv2]) |
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# comparison_results is a list of bool, where each element denotes whether all the responses in conv1 together is better than that of conv2 |
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``` |
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### Use case 2: Best-of-n sampling (Decoding Enhancing) |
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**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 at[OpenAI WebGPT section 3.2](https://arxiv.org/pdf/2112.09332.pdf) and [OpenAI Blog](https://openai.com/research/measuring-goodharts-law)). |
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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. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta") |
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model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto") |
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inputs = [...] # your list of inputs |
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system_message = { |
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"role": "system", |
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"content": "You are a friendly chatbot who always responds in the style of a pirate", |
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} |
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messages = [ |
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[ |
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system_message, |
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{"role": "user", "content": _input}, |
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] |
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for _input in zip(inputs) |
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] |
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prompts = [tokenizer.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages] |
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outputs = blender.best_of_n_generate(model, tokenizer, prompts, n=10) |
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print("### Prompt:") |
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print(prompts[0]) |
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print("### best-of-n generations:") |
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print(outputs[0]) |
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``` |
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### Use case 3: RLHF |
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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. |
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We believe PairRM will power the alignment of LLM in an efficient and effective way. |
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With a `blender.compare()` function, you can easily apply PairRM to poopular RLHF toolkits like [trl](https://huggingface.co/docs/trl/index). |
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**🔥 Check more details on our example jupyter notebook usage: [`blender_usage.ipynb`](https://github.com/yuchenlin/LLM-Blender/blob/main/blender_usage.ipynb)** |
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Learn more in our LLM-Blender Github [README.md](https://github.com/yuchenlin/LLM-Blender#rank-and-fusion) |
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## Statistics |
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### Context length |
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| PairRanker type | Source max length | Candidate max length | Total max length | |
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|:-----------------:|:-----------------:|----------------------|------------------| |
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| [pair-ranker](https://huggingface.co/llm-blender/pair-ranker) | 128 | 128 | 384 | |
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| [PairRM](https://huggingface.co/llm-blender/pair-reward-model/) (This model) | 1224 | 412 | 2048 | |
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### Performance |
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PairRM has been trained on various high-quality and large-scale dataset with human preference annotations and exhibits great correlation with human preferences |
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with an extremly small model size (0.4B), approching the performance of GPT-4. |
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We test the pairwise comparison on |
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- [Auto-J pairwise testdata](https://github.com/GAIR-NLP/auto-j#pairwise-response-comparison) |
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- [HHH-alignment](https://huggingface.co/datasets/HuggingFaceH4/hhh_alignment) |
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- [MT-bench-human-judgements](https://huggingface.co/datasets/lmsys/mt_bench_human_judgments) |
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#### Auto-J Pairwise test data performance |
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| Model | Summ | Exam | Code | Rewriting | Crea W | Func W | Comm | NLP | Overall | |
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|:---------------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-----:|:--------:|:---------:| |
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| Closed -source Models | |
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| ChatGPT | 33.3 | 40.3 | 36.6 | 31.6 | 48.2 | 40.4 | 47.6 | 45.8 | 42.7 | |
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| Claude -2 | 30.6 | 36.1 | 41.7 | 34.2 | 48.1 | 42.5 | 40.6 | 48.5 | 42.4 | |
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| GPT -4 | 59.7 | 51.4 | 69.2 | 58.3 | 66.7 | 60.4 | 58.3 | 65.2 | 61.9 | |
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| Open -source Models | |
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| SteamSHP | 33.3 | 29.2 | 26.7 | 33.3 | 40.7 | 31.3 | 51.4 | 51.9 | 40.6 | |
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| PandaLM | 29.2 | 33.3 | 31.7 | 23.3 | 43.5 | 32.9 | 44.8 | 48.9 | 38.9 | |
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| LLaMA -2-Chat -13B | 20.8 | 27.8 | 19.2 | 20 | 31.5 | 27.5 | 35.8 | 31.8 | 29 | |
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| Vicuna -13B-v1.5 | 30.6 | 23.6 | 35 | 28.3 | 36.1 | 37.5 | 45.5 | 39.8 | 37.3 | |
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| WizardLM -13B-v1.2 | 22.2 | 20.8 | 32.5 | 19.2 | 28.7 | 25.4 | 29.2 | 33 | 27.8 | |
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| LLAMA -2-chat -70B | 34.7 | 33.3 | 36.7 | 35.8 | 51.4 | 54.2 | 47.2 | 47.7 | 45.9 | |
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| AUTO -J (13b) | 45.8 | 38.9 | 59.2 | 47.5 | 54.6 | 57.1 | **58** | 57.6 | 54.8 | |
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| **PairRM (0.4b)** | **56.94** | **52.78** | **58.33** | **55.83** | **61.57** | **59.17** | 57.64 | **62.5** | **59.05** | |
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#### HHH-Alignment and MT-bench human judgements |
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| Evaluator LM | HHH ALIGNMENT | | | | | MT BENCH HUMAN JUDG . | |
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|:-------------------------:|:-------------:|:---------:|:---------:|:--------:|:-----------:|:---------------------:| |
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| | Help . | Harm . | Hon . | Other | Total Avg . | Human Preference | |
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| RANDOM | 50 | 50 | 50 | 50 | 50 | 34.26 | |
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| STANFORDNLP REWARD MODEL | 69.49 | 60.34 | 52.46 | 51.16 | 58.82 | 44.79 | |
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| ALMOST REWARD MODEL | 74.58 | 67.24 | 78.69 | 86.05 | 76.02 | 49.9 | |
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| LLAMA2 -CHAT 7B | 66.1 | 81.03 | 70.49 | 74.42 | 72.85 | 51.78 | |
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| LLAMA2 -CHAT 13B | 74.58 | 87.93 | 55.74 | 79.07 | 73.76 | 52.34 | |
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| LLAMA2 -CHAT 70B | 66.1 | **89.66** | 67.21 | 74.42 | 74.21 | 53.67 | |
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| LLAMA2 -CHAT 13B+COARSE . | 68.74 | 68.97 | 65.57 | 67.44 | 67.42 | 46.89 | |
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| GPT -3.5-TURBO -0613 | 76.27 | 87.93 | 67.21 | 86.05 | 78.73 | 57.12 | |
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| PROMETHEUS 7B | 69.49 | 84.48 | 78.69 | 90.7 | 80.09 | 55.14 | |
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| PROMETHEUS 13B | 81.36 | 82.76 | 75.41 | 76.74 | 79.19 | 57.72 | |
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| **PairRM (0.4b)** | **84.75** | 84.48 | **80.33** | **90.7** | **84.62** | **59** | |
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| GPT -4-0613 | 91.53 | 93.1 | 85.25 | 83.72 | 88.69 | 63.87 | |
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**While PairRM is a extremely small model (0.4B) based on deberta, the pairwise comparison aggrement performance approches GPT-4's performance!** |
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Two reasons to attribute: |
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- Our PairRM specically designed model arch for pairwise comparison through bidirectional attention (See LLM-blender paper for more details) |
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- The high-quality and large-scale human preference annotation data it was train on (see training dataset list on this hugging face page) |
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## Citation |
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If you are using PairRM in your research, please cite LLM-blender. |
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```bibtex |
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@inproceedings{llm-blender-2023, |
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title = "LLM-Blender: Ensembling Large Language Models with Pairwise Comparison and Generative Fusion", |
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author = "Jiang, Dongfu and Ren, Xiang and Lin, Bill Yuchen", |
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booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023)", |
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year = "2023" |
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} |
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``` |
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