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LLM hallucinations 101 + other resources

neptune.ai

The experiment tracker for foundation model training.

发布日期: 2024年9月30日

As we close out September, we’ve got some new content worth a look. We’re covering topics like LLM hallucinations, reinforcement learning with human feedback for LLMs, setting up guardrails for LLM safety, and more insights to keep you informed.

Enjoy!

MLOps & LLMOps

> LLMOps: What It Is, Why It Matters, and How to Implement It -? To kick things off, Stephen Oladele ’s guide breaks down LLMOps, explaining how it builds on MLOps, its critical levels, components, practical applications, and where it’s headed next.

> How Veo Eliminated Work Loss With a New Experiment Tracker - Then, we have a story on how the ML/AI team at Veo Technologies moved from MLflow to Neptune, gaining more structured management and security for their projects.

Guides & tutorials

> Reinforcement Learning With Human Feedback For LLMs - Moving on, Micha? Oleszak discusses the value of RLHF in the context of LLMs, offering a closer look at the process, best practices, and useful tools to power this approach.

> LLM Guardrails: Secure and Controllable Deployment - Following that, Natalia Kuzminykh unpacks the critical vulnerabilities within large language models, offering insights into effective guardrail strategies and some real-world examples to help secure LLM-based applications.

The flow of training data poisoning. First, an attacker injects poisoned samples into the training dataset. Subsequently, the model is trained on this corrupted data, learning harmful patterns. During inference, the poisoned model exhibits compromised behavior, leading to, e.g., a drop in accuracy or misclassifications.

> LLM Hallucinations 101: Why Do They Appear? Can We Avoid Them? - Next, Aitor Mira Abad breaks down the double-edged nature of LLM hallucinations, providing a well-structured guide to understanding their origins, mechanisms, and strategies to address them.

Overview of a RAG application. The prompt is used to retrieve relevant documents from a document store, which are added to the input sent to the LLM. This provides knowledge to the LLM it has not learned during training. — Overview of a?RAG application. The prompt is used to retrieve relevant documents from a document store, which are added to the input sent to the LLM. This provides knowledge to the LLM it has not learned during training.

> LLMs for Structured Data - Lastly, Ricardo Cardoso Pereira presents three practical applications of structured data: RAG-based data filtering, generating code for operations on structured datasets, and creating synthetic data points.

The Data Exchange Podcast

Our CPO, Aurimas Griciūnas , recently joined The Data Exchange Podcast to discuss the challenges and innovations in training and scaling LLMs.

They talked about going from MLOps to LLMOps, the scale and complexity of LLM clusters and training, frontier models and training cycles, LLMOps enterprise lessons, experimentation in agentic systems, and more.

You can find the full episode here.

Thanks for sticking with us! If you think someone else could benefit from these updates, please send it their way.