Chain Of Natural Language Inference (CoNLI)

Hallucination is categorised into subcategories of Context-Free Hallucination, Ungrounded Hallucination &...

Small & Medium-Sized Enterprises Enter The Conversational AI Arena

Introduction

Considering digital innovation, small and medium-sized enterprises (SMEs) should be increasingly embracing...

Large Language Model Hallucination Mitigation Techniques

This recently released study is a comprehensive survey of 32+ mitigation techniques to address hallucination.

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Validating Low-Confidence LLM Generation

The basic approach has a hallucination detection phase and a mitigation phase.

Random Chain-Of-Thought For LLMs & Distilling Self-Evaluation Capability

Here I discuss the five emerging architectural principles for LLM implementations & how curation & enrichment of...

Prompt Pipelines

LLM-based applications can take the form of autonomous agents, prompt chaining or prompt pipelines. These approaches...

Active Prompting with Chain-of-Thought for Large Language Models

By using AI accelerated human annotation this framework removes uncertainty and introduces reliability via a...

Teaching LLMs To Say, “I don’t know”

Instead of stating that it does not know, LLMs hallucinate. Hallucination can best be described as highly plausible,...

Improving Text Embeddings with LLM Generated Synthetic Data

Value Discovered

Text embeddings are really playing a pivotal role in retrieving semantically similar text for RAG...

LLM Performance Over Time & LLM Task Contamination

A recent study revealed that LLMs perform significantly better on datasets released before their training data creation...