| --- |
| license: apache-2.0 |
| datasets: |
| - openai/summarize_from_feedback |
| - openai/webgpt_comparisons |
| - berkeley-nest/Nectar |
| - Dahoas/instruct-synthetic-prompt-responses |
| - Anthropic/hh-rlhf |
| - lmsys/chatbot_arena_conversations |
| - openbmb/UltraFeedback |
| - argilla/ultrafeedback-binarized-preferences-cleaned |
| metrics: |
| - accuracy |
| tags: |
| - reward_model |
| - reward-model |
| - RLHF |
| - evaluation |
| - llm |
| - instruction |
| - reranking |
| language: |
| - en |
| --- |
| # Better Implementation of [*PairRM*](https://huggingface.co/llm-blender/PairRM) |
|
|
| ## Introduction |
|
|
| This version of PairRM have some fixes on training process, which improve model's performance by **15%**. |
|
|
| ### Minor Fixes |
|
|
| - Longer Context Length (2048 -> 3370) |
|
|
| Thanks to deberta's tokenzer, original PairRM model had enough Context Length. |
|
|
| But, the longer the better :> |
|
|
| --- |
|
|
| ### Major Fixes |
|
|
| - Change Prompt Format |
|
|
| Why use something like |
| ``` |
| <Response i + 1> {response} |
| ``` |
|
|
| So, I changed to a format based on Vicuna 1.1. |
|
|
| --- |
|
|
| - Change Truncate side |
|
|
| The original process was using right side truncate even on Input. This can cause serious problem when Input exceeds model's context length. |
|
|
| --- |
|
|
| - Dataset Filter |
|
|
| There was decent amount of empty assistant response on original dataset. So, I dropped them. |
|
|
| --- |
|
|
| ## Example Code |
|
|
| **The code below is modified from** (**PairRM-hf Repo**)[https://huggingface.co/llm-blender/PairRM-hf] |
|
|
| ```python |
| import os |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" |
| from llm_blender.pair_ranker.pairrm import DebertaV2PairRM |
| from transformers import AutoTokenizer |
| from typing import List |
| pairrm = DebertaV2PairRM.from_pretrained("maywell/Better-PairRM", device_map="cuda:0").eval() |
| tokenizer = AutoTokenizer.from_pretrained("maywell/Better-PairRM") |
| source_prefix = "<|source|>" |
| cand1_prefix = "<|candidate1|>" |
| cand2_prefix = "<|candidate2|>" |
| inputs = ["hello!", "I love you!"] |
| candidates_A = ["hi!", "I hate you!"] |
| candidates_B = ["f**k off!", "I love you, too!"] |
| def tokenize_pair(sources:List[str], candidate1s:List[str], candidate2s:List[str], source_max_length=2030, candidate_max_length=670): |
| ids = [] |
| assert len(sources) == len(candidate1s) == len(candidate2s) |
| max_length = source_max_length + 2 * candidate_max_length |
| for i in range(len(sources)): |
| source_ids = tokenizer.encode(source_prefix + sources[i], max_length=source_max_length, truncation=True) |
| candidate_max_length = (max_length - len(source_ids)) // 2 |
| candidate1_ids = tokenizer.encode(cand1_prefix + candidate1s[i], max_length=candidate_max_length, truncation=True) |
| candidate2_ids = tokenizer.encode(cand2_prefix + candidate2s[i], max_length=candidate_max_length, truncation=True) |
| ids.append(source_ids + candidate1_ids + candidate2_ids) |
| encodings = tokenizer.pad({"input_ids": ids}, return_tensors="pt", padding="max_length", max_length=max_length) |
| return encodings |
| |
| encodings = tokenize_pair(inputs, candidates_A, candidates_B) |
| encodings = {k:v.to(pairrm.device) for k,v in encodings.items()} |
| outputs = pairrm(**encodings) |
| logits = outputs.logits.tolist() |
| comparison_results = outputs.logits > 0 |
| print(logits) |
| print(comparison_results) |
| ``` |
|
|
| You can also easily compare two conversations like the followings: |
| ```python |
| import jinja2 |
| from transformers import AutoTokenizer |
| |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-large") |
| |
| def truncate_texts(text, max_length, truncate_side): |
| tokenizer.truncation_side = truncate_side |
| tokens = tokenizer.encode(text, add_special_tokens=False, max_length=max_length) |
| truncated_text = tokenizer.decode(tokens, skip_special_tokens=True) |
| return truncated_text |
| |
| MY_JINJA_TEMPLATE = """{% for message in messages -%} |
| {% if message['role'] == 'user' -%} |
| USER: {{ message['content']|trim -}} |
| {% if not loop.last -%} |
| |
| |
| {% endif %} |
| {% elif message['role'] == 'assistant' -%} |
| ASSISTANT: {{ message['content']|trim -}} |
| {% if not loop.last -%} |
| |
| |
| {% endif %} |
| {% elif message['role'] == 'user_context' -%} |
| USER: {{ message['content']|trim -}} |
| {% if not loop.last -%} |
| |
| |
| {% endif %} |
| {% elif message['role'] == 'system' -%} |
| SYSTEM MESSAGE: {{ message['content']|trim -}} |
| {% if not loop.last -%} |
| |
| |
| {% endif %} |
| {% endif %} |
| {% endfor -%} |
| {% if add_generation_prompt and messages[-1]['role'] != 'assistant' -%} |
| ASSISTANT: {% endif -%}""" |
| |
| my_jinja2_env = jinja2.Environment() |
| my_jinja2_template = my_jinja2_env.from_string(MY_JINJA_TEMPLATE) |
| |
| def tokenize_conv_pair(convAs: List[str], convBs: List[str]): |
| |
| # check conversations correctness |
| assert len(convAs) == len(convBs), "Number of conversations must be the same" |
| for c_a, c_b in zip(convAs, convBs): |
| assert len(c_a) == len(c_b), "Number of turns in each conversation must be the same" |
| assert all([c_a[i]['content'] == c_b[i]['content'] for i in range(0, len(c_a), 2)]), "USER turns must be the same" |
| |
| inputs = [ |
| truncate_texts(my_jinja2_template.render(messages=x[:-1], add_generation_prompt=True), 2030, "left") for x in convAs |
| ] |
| cand1_texts = [ |
| truncate_texts(x[-1]['content'], 670, "right") for x in convAs |
| ] |
| cand2_texts = [ |
| truncate_texts(x[-1]['content'], 670, "right") for x in convBs |
| ] |
| encodings = tokenize_pair(inputs, cand1_texts, cand2_texts) |
| return encodings |
| ``` |
|
|
| ## Statistics |
|
|
| ### Context length |
| | PairRanker type | Source max length | Candidate max length | Total max length | |
| |:-----------------:|:-----------------:|----------------------|------------------| |
| | [pair-ranker](https://huggingface.co/llm-blender/pair-ranker) | 128 | 128 | 384 | |
| | [PairRM](https://huggingface.co/llm-blender/pair-reward-model/) | 1224 | 412 | 2048 | |
| | [Better-PairRM](https://huggingface.co/maywell/Better-PairRM/) (This model) | 2030 | 670 | 3370 | |
|
|
| ### Performance |
|
|
| #### Reward-Bench by AllenAI |
|
|
| | Metric | llm-blender/PairRM-hf | maywell/Better-PairRM | |
| |----------------------------|------------------------|------------------------| |
| | model | llm-blender/PairRM-hf | maywell/Better-PairRM | |
| | model_type | Custom Classifier | Custom Classifier | |
| | alpacaeval-length | 0.758 | **0.863** | |
| | alpacaeval-hard | 0.979 | **1.000** | |
| | alpacaeval-easy | 0.970 | **0.990** | |
| | donotanswer | 0.360 | **0.522** | |
| | hep-cpp | 0.628 | **0.646** | |
| | hep-go | 0.689 | **0.713** | |
| | hep-java | 0.628 | **0.713** | |
| | hep-js | 0.604 | **0.707** | |
| | hep-python | 0.646 | **0.713** | |
| | hep-rust | 0.652 | **0.726** | |
| | llmbar-adver-GPTInst | **0.304** | 0.141 | |
| | llmbar-adver-GPTOut | **0.596** | 0.447 | |
| | llmbar-adver-manual | **0.500** | 0.261 | |
| | llmbar-adver-neighbor | **0.433** | 0.276 | |
| | llmbar-natural | **0.800** | 0.720 | |
| | math-prm | **0.333** | 0.295 | |
| | mt-bench-hard | 0.649 | **0.703** | |
| | mt-bench-med | 0.900 | **1.000** | |
| | mt-bench-easy | **0.964** | 0.929 | |
| | refusals-dangerous | 0.080 | **0.730** | |
| | refusals-offensive | 0.010 | **0.940** | |
| | xstest-should-refuse | 0.370 | **0.968** | |
| | xstest-should-respond | **0.952** | 0.876 | |
| | average | 0.600 | **0.690** | |
| |
| > *Note - llmbar test score is bit weird across all models on [Reward-Bench](https://huggingface.co/spaces/allenai/reward-bench)* |
| |
| ## Thanks to |
| |
| - [Sionic AI](https://sionic.ai/) for providing the A100 cluster. |
| |
| ## Contact |
| |
| - [Discord Server Link](https://discord.gg/MrBt3PXdXc) |
| |
| ## Original Paper |
| ``` |
| @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" |
| } |
| ``` |
