Andreas Erben

You are not my model anymore - understanding LLM model behavior

Your LLM is a shoggoth with a smiley face mask. Learn what happens when the mask slips and your application breaks.

You are not my model anymore - understanding LLM model behavior
#1about 2 minutes

Unexpected LLM behavior from hidden platform updates

A practical demonstration shows how a cloud provider's content filter update can unexpectedly block access to documents, causing application failures.

#2about 3 minutes

How LLMs generate text and learn behavior

Large language models use a transformer architecture to predict the next token based on probability, with instruction tuning and alignment shaping their final behavior.

#3about 2 minutes

The opaque and complex stack of modern LLM services

Major LLM providers operate in secrecy, and the full technology stack from model weights to the API is complex, leaving developers with limited visibility and control.

#4about 3 minutes

Managing risks from provider filters and short API lifecycles

Cloud provider content filters can change without notice, creating vulnerabilities, while the short lifecycle of model APIs requires constant adaptation.

#5about 4 minutes

Understanding LLMs as alien minds with fragile alignment

LLMs are conceptually like alien intelligences with a fragile, human-like alignment layer that can be bypassed by jailbreaks exploiting internal model circuits.

#6about 2 minutes

How model personalities and behaviors shift between versions

Different LLM versions exhibit distinct behaviors and may ignore system prompts, as shown by a comparison between GPT-4 and a newer reasoning model.

#7about 3 minutes

Using evaluations to systematically test model behavior

Systematically test model behavior using evaluations, which can be automated by generating prompt variations or using pre-built cloud and open-source frameworks.

#8about 4 minutes

Using prompt engineering to mitigate model drift

Mitigate model behavior drift by using advanced prompt engineering techniques like forcing reasoning, providing few-shot examples, and being highly explicit in instructions.

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Matching moments

Analyzing the risks and architecture of current AI models
22:05 MIN

Analyzing the risks and architecture of current AI models

Opening Keynote by Sir Tim Berners-Lee

Shifting from traditional code to AI-powered logic
09:55 MIN

Shifting from traditional code to AI-powered logic

WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA

Addressing the core challenges of large language models
00:27 MIN

Addressing the core challenges of large language models

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

The ethical risks of outdated and insecure AI models
13:54 MIN

The ethical risks of outdated and insecure AI models

AI & Ethics

Understanding the GenAI lifecycle and its operational challenges
01:11 MIN

Understanding the GenAI lifecycle and its operational challenges

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Addressing the key challenges of large language models
01:47 MIN

Addressing the key challenges of large language models

Large Language Models ❤️ Knowledge Graphs

The challenge of moving AI from demo to production
09:56 MIN

The challenge of moving AI from demo to production

What’s New with Google Gemini?

AI privacy concerns and prompt engineering
25:33 MIN

AI privacy concerns and prompt engineering

Coffee with Developers - Cassidy Williams -

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