Factiverse has secured €1 million in funding to expand its AI-powered fact-checking platform in the ongoing battle against fake news and AI hallucinations

While it is evident that machine learning algorithms are able to solve more challenging requirements, they are not yet perfect.

Learn to use AI-generated summaries, vector indexing, and large language models to create a smarter information retrieval system.

Explore detailed examples of deductive verification with a Natural Program-based approach, highlighting successful error detection and areas of improvement.

Explore a comprehensive list of prompts for verifying and generating reasoning chains

Discover a detailed process for extracting final answers from language models, including pattern recognition and regular expression techniques.

Understand why improvements in deductive verification accuracy don't always lead to better final answer correctness, with a focus on the GSM8K dataset.

Discover how fine-tuning Vicuna models boosts their deductive verification accuracy, and see why they still trail behind GPT-3.5 in performance.

Discover how the Natural Program framework revolutionizes AI reasoning by enhancing accuracy with innovative verification and voting strategies.

The limitations of the Natural Program deductive reasoning verification highlight AI’s struggles with contextual ambiguities.

This paper evaluates the effectiveness of the Natural Program-based deductive reasoning process, showcasing improvements in reasoning rigor and reliability.

Discover how Natural Program and deductive verification enhance AI reasoning accuracy and trust by validating every step with unanimity-plurality voting.

This paper introduces the concept of validating each reasoning step in LLMs for QA tasks, focusing on deductive reasoning to improve accuracy.

This project highlights advancements in AI reasoning by introducing Natural Programs, a method to verify step-by-step deductive reasoning processes in LLMs.

Natural Program introduces a step-by-step deductive reasoning framework for LLMs, reducing errors and hallucinations through rigorous self-verification.

I don’t care about the future of productivity: I want to laugh

Large language models were never meant to be sources of absolute truth. Yet, we continue to treat them as such.

Data scientists will struggle with AI hallucinations because they don't fit the standard definition of "errors."

RAG is fixing the hallucination problem in LLMs. As RAG systems are bleeding edge, they need a lot of improvement for production. So is LightRAG the answer?

What's more dangerous: asking questions or posting memes about AI "hallucinations" in Linkedin groups about artificial intelligence?