We, at Assemble Teams are building a new LLM that addresses the challenges of bias, accuracy, explainability, security, and safety.
We believe that LLMs have the potential to be powerful tools for a variety of tasks, but we also recognize that they come with some challenges. Our goal is to build an LLM that is both powerful and safe.
Here are some of the challenges that we are addressing:
- Bias: We are using a dataset that is carefully curated to minimize bias. We are also using techniques to debias the output of our model.
- Accuracy: We are using a state-of-the-art training algorithm and a large dataset to train our model. We are also using techniques to improve the accuracy of our model.
- Explainability: We are developing techniques to explain how our model generates its output. This will make it easier to trust the output of our model and to debug it when it generates incorrect or misleading information.
- Security: We are using security techniques to make our model more resistant to attack. We are also working to develop security best practices for using LLMs.
- Safety: We are developing techniques to make our model more safe to use. We are also working to develop safety best practices for using LLMs.
We are inviting developers and followers to engage in building cost effective LLMs.
We believe that building cost effective LLMs is important for making these tools accessible to a wider range of people. We are open to collaborating with developers and followers to build cost effective LLMs.