The article discusses the OVON agentic framework for mitigating hallucinations in Large Language Models (LLMs). It explains the structured, collaborative pipeline involving front-end and reviewer agents, the use of 'Conversation Envelopes' and 'Whispers' for efficient data exchange, and novel KPIs for measuring success. The article also addresses future directions and the importance of trust in AI systems.
A discussion on the acceptance of AI hallucinations as a surmountable challenge rather than a fundamental flaw, highlighting improvements in model reliability and the benefits of AI wrappers and augmentation techniques.
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:
- Truthfulness is encoded in exact answer tokens: LLMs concentrate truthfulness information in specific tokens, which, if modified, would change the correctness of the answer.
- Probing classifiers can predict errors: Trained classifier models can predict features related to the truthfulness of generated outputs, significantly improving error detection.
- Skill-specific truthfulness: Probing classifiers generalize within tasks that require similar skills, but not across tasks with different skills.
- LLMs encode multiple mechanisms of truthfulness: Models represent truthfulness through various mechanisms, each corresponding to different notions of truth.
- Internal truthfulness signals align with external behavior: In some cases, the model's internal activations correctly identify the right answer, yet it generates an incorrect response, highlighting the limitations of current evaluation methods.
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.
