HelpSteer2

Preview：

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Introduce：

HelpSteer2 is an open source dataset released by NVIDIA to support training that aligns models to make them more helpful, fact-correct, and coherent, while being adjustable in terms of complexity and redundancy of responses. The dataset, created in collaboration with Scale AI, achieved an 88.8% performance on RewardBench when used with the Llama 3 70B base model, one of the best reward models as of June 12, 2024.

https://pic.chinaz.com/ai/2024/06/24061810392299476445.jpg
Stakeholders:
The HelpSteer2 dataset is primarily aimed at developers and researchers who need to train and optimize dialogue systems, reward models, and language models. It is especially suitable for professionals who want to improve the performance of their models on specific tasks, such as customer service automation, virtual assistants, or any scenario that requires natural language understanding and generation.
Usage Scenario Examples:

It is used to train SteerLM regression reward model to improve the performance of dialogue system on specific tasks.
As part of the research project, analyze and compare the response quality of different models when handling multiple rounds of dialogue.
In the field of education, help students understand how to improve the response of language models through machine learning techniques.

The features of the tool:

It contained 21,362 samples, each consisting of a prompt, a response, and five human-labeled attribute scores.
Attribute scores include helpfulness, correctness, coherence, complexity, and redundancy.
Samples that support multiple rounds of conversations can be used for DPO or Preference RM training based on preference pairs.
Responses are generated by 10 different internal large language models, providing diverse but reasonable responses.
Labeling using Scale AI ensures the quality and consistency of the data set.
The dataset is licensed under CC-BY-4.0 and is free to use and distribute.

Steps for Use:

Step 1: Visit Hugging Face’s website and search for the HelliSteer2 dataset.
Step 2: Download the dataset and load the dataset using the appropriate tool or library.
Step 3: Select specific samples or attributes from the dataset for analysis based on project requirements.
Step 4: Use data sets to train or optimize your language model and monitor how the model performs on various attributes.
Step 5: Adjust the model parameters and improve the training process of the model as needed.
Step 6: Evaluate model performance to ensure it meets expectations in terms of helpfulness, correctness, and other key attributes.
Step 7: Deploy the trained model into a real application, such as a chatbot or virtual assistant.

Tool’s Tabs: Open source datasets, reward models