0 bookmark(s) - Sort by: Date ↓ / Title /
A new study reveals that large language models (LLMs) possess a deeper understanding of truthfulness than previously thought, and can identify their own mistakes through internal representations.
The study, by researchers at Technion, Google Research, and Apple, reveals that Large Language Models (LLMs) possess a deeper understanding of truthfulness than previously thought. The study analyzed the internal workings of LLMs, finding that they can identify their own mistakes, including factual inaccuracies, biases, and common-sense reasoning failures.
Key Findings:
The article discusses the intrinsic representation of errors, or hallucinations, in large language models (LLMs). It highlights that LLMs' internal states encode truthfulness information, which can be leveraged for error detection. The study reveals that error detectors may not generalize across datasets, implying that truthfulness encoding is multifaceted. Additionally, the research shows that internal representations can predict the types of errors the model is likely to make, and that there can be discrepancies between LLMs' internal encoding and external behavior.
The article explores the challenges associated with generative artificial intelligence systems producing inaccurate or 'hallucinated' information. It proposes a strategic roadmap to mitigate these issues by enhancing data quality, improving model training techniques, and implementing robust validation checks. The goal is to ensure that AI-generated content is reliable and trustworthy.
This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.
First / Previous / Next / Last
/ Page 1 of 0