Jodie Burchell
Lies, Damned Lies and Large Language Models
#1about 2 minutes
Understanding the dual nature of large language models
LLMs can generate both creative, coherent text and factually incorrect "hallucinations," posing a significant challenge for real-world applications.
#2about 4 minutes
The architecture and evolution of LLMs
The combination of the scalable Transformer architecture and massive text datasets enables models like GPT to develop "parametric knowledge" as they grow in size.
#3about 3 minutes
How training data quality influences model behavior
The quality of web-scraped datasets like Common Crawl, even after filtering, directly contributes to model hallucinations by embedding misinformation.
#4about 2 minutes
Differentiating between faithfulness and factuality hallucinations
Hallucinations are categorized as either faithfulness errors, which contradict a given source text, or factuality errors, which stem from incorrect learned knowledge.
#5about 3 minutes
Using the TruthfulQA dataset to measure misinformation
The TruthfulQA dataset provides a benchmark for measuring an LLM's tendency to repeat common misconceptions and conspiracy theories across various categories.
#6about 6 minutes
A practical guide to benchmarking LLM hallucinations
A step-by-step demonstration shows how to use Python, LangChain, and Hugging Face Datasets to run the TruthfulQA benchmark on a model like GPT-3.5 Turbo.
#7about 4 minutes
Exploring strategies to reduce LLM hallucinations
Key techniques to mitigate hallucinations include careful prompt crafting, domain-specific fine-tuning, output evaluation, and retrieval-augmented generation (RAG).
#8about 4 minutes
A deep dive into retrieval-augmented generation
RAG reduces hallucinations by augmenting prompts with relevant, up-to-date information retrieved from a vector database of document embeddings.
#9about 2 minutes
Overcoming challenges with advanced RAG techniques
Naive RAG can fail due to poor retrieval or generation, but advanced methods like Rowan selectively apply retrieval to significantly improve factuality.
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Explaining how large language models work and why they hallucinate
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Understanding the risks of large language models
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Understanding the limitations of large language models
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