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LLM Hallucinations in RAG QA - Thomas Stadelmann, deepset.ai

  Рет қаралды 6,685

deepset

deepset

Жыл бұрын

Hallucinations are one of the biggest challenges in running LLM-based applications as they can significantly undermine the trustworthiness of your application. Retrieval-augmented QA not only enables us to run LLMs on any data but also to mitigate hallucinations. However even with retrieval augmentation we cannot fully avoid them.
In this webinar Thomas will show you current approaches on how to systematically detect hallucinations, paving the way for automating this critical issue.
#ai #llm #generativeai #developer #deepset.ai

Пікірлер: 4
@vsrohit
@vsrohit 11 ай бұрын
Can you please provide the links for the hallucination detection model?
@pythontok4192
@pythontok4192 9 ай бұрын
What does the hallucination detector model compare each sentence of the answer against? If you run it against the contexts , say if k=4 snippets retrieved by the rag, wouldn’t some of them not be relevant?
@davefar2964
@davefar2964 7 ай бұрын
Thanks a lot for this presentation, the research papers on hallucinations as well as your BERTscore solutions were quite interesting. Another class of approaches (that causes high cost but no big latency if done in parallel) to detect and avoid hallucinations (see kzfaq.info/get/bejne/otmKdrmeqKi2nJc.htmlsi=-dYinw2SiAGw44df&t=1428) is getting multiple samples at test time, e.g. doing self-consistency or ensembling, deciding for the final answer by mayority voting or ranking.
@billykotsos4642
@billykotsos4642 Жыл бұрын
So even RAG can't be trusted 100% huh...
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