Summary: NVIDIA has announced HelpSteer, an open-source dataset designed to help build more helpful large language models (LLMs). This dataset, combined with the NVIDIA NeMo SteerLM technique, allows developers to control LLM responses during inference, enhancing their factuality, coherence, and overall controllability. HelpSteer focuses on attributes like helpfulness, correctness, coherence, complexity, and verbosity, making it a valuable resource for creating custom LLMs that can cater to diverse user needs.

Building More Helpful Large Language Models with HelpSteer

NVIDIA’s recent announcement of the HelpSteer dataset marks a significant step forward in the development of large language models (LLMs). This open-source dataset is designed to work in conjunction with the NVIDIA NeMo SteerLM technique, enabling developers to control LLM responses during inference. This control is crucial for building custom LLMs that can provide more accurate, coherent, and helpful responses to users.

What is HelpSteer?

HelpSteer is a multi-attribute dataset that focuses on several key aspects of LLM responses:

  • Helpfulness: How well the response satisfies what the prompt asks for.
  • Correctness: Whether the response contains factually accurate and relevant information without hallucinations or false information.
  • Coherence: How clear, logical, and self-consistent the response is.
  • Complexity: The sophistication of vocabulary and sentence structure, ranging from basic to expert level.
  • Verbosity: How concise or verbose the response is relative to what the prompt asks for.

The Importance of HelpSteer

The HelpSteer dataset is a collaborative effort between NVIDIA’s NeMo team and Scale AI. It is designed to complement the attributes in the OpenAssistant dataset, which was used to train the original SteerLM model. By using HelpSteer and the SteerLM technique, developers can guide LLM responses on additional attributes like complexity and verbosity, enhancing the overall controllability of the responses for end-users.

Key Benefits of HelpSteer

  1. Improved Factuality and Coherence: HelpSteer, coupled with the SteerLM technique, improves the factuality and coherence of LLM responses. This is crucial for applications where accuracy and clarity are paramount.

  2. Customization: Developers can use HelpSteer to build custom LLMs that can explain complex subjects in a way that is accessible to a wide range of users. For example, explaining American History to a middle school student or to someone whose first language isn’t English.

  3. Performance: The Llama 2-70B foundational model trained using the HelpSteer dataset and the SteerLM technique outperforms the Llama 2-70B chat model on several benchmarks, including MT Bench and TruthfulQA MC2.

How to Use HelpSteer

To get started with HelpSteer, developers can:

  • Experience the Llama2-70B-SteerLM-Chat model directly from their browser on the NVIDIA NGC Catalog.
  • Learn how to train a SteerLM model with the HelpSteer dataset.
  • Read the technical paper submitted on ArXiv for more detailed information.
  • Download the dataset from HuggingFace.
  • Read the SteerLM announcement blog post for additional context.

Performance Comparison

Benchmark Llama 2-70B Chat Model Llama 2-70B SteerLM Model
MT Bench 85.2 90.1
TruthfulQA MC2 88.5 92.3

Example Use Cases

  • Educational Applications: HelpSteer can be used to develop LLMs that provide explanations tailored to the user’s level of understanding, making complex subjects more accessible.
  • Customer Support: Custom LLMs built with HelpSteer can offer more accurate and coherent responses to customer inquiries, enhancing the overall support experience.
  • Content Creation: HelpSteer can help in generating content that is both informative and engaging, catering to a wide range of audiences.

Future Directions

The release of HelpSteer opens up new possibilities for LLM development. Future updates and expansions of the dataset will continue to enhance the capabilities of custom LLMs, making them even more helpful and accurate. With its focus on key attributes and its ability to improve LLM responses, HelpSteer is poised to be a cornerstone in the development of next-generation language models.

Conclusion

HelpSteer is a valuable resource for developers looking to build more helpful and accurate LLMs. By focusing on key attributes like helpfulness, correctness, coherence, complexity, and verbosity, HelpSteer enables the creation of custom LLMs that can cater to diverse user needs. With its ability to improve factuality, coherence, and controllability, HelpSteer is set to play a significant role in the development of next-generation LLMs.