Cobus Greyling

UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue

This study explores how the RAG process can be decomposed, adding elements like multi-document retrieval, memory and...

Chain-of-Symbol Prompting (CoS) For Large Language Models

Cobus Greyling

LLMs need to understand a virtual spatial environment described through natural language while planning & achieving...

Concise Chain-of-Thought (CCoT) Prompting

Cobus Greyling

Traditional CoT comes at a cost of increased output token usage, CCoT prompting is a prompt-engineering technique which...

Understanding LLM User Experience & Expectation

Cobus Greyling

This study surfaces valuable insights into the frequency of LLM use together with user intents, expectations and...

Meta Taxonomy Of LLM Correction & Refinement

Cobus Greyling

A number of LLM techniques and classes of implementation are emerging, a fairly recent study created a meta taxonomy of...

Considering Large Language Model Reasoning Step Length

Cobus Greyling

When using Chain of Thought, what is the optimal number of steps to use?

Chain Of Natural Language Inference (CoNLI)

Cobus Greyling

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

Validating Low-Confidence LLM Generation

Cobus Greyling

This is an approach that actively detects and mitigates hallucinations during the generation process.

Large Language Model Hallucination Mitigation Techniques

Cobus Greyling

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

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

Cobus Greyling

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

Kore.ai Technical Blog

UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue

Chain-of-Symbol Prompting (CoS) For Large Language Models

LLMs need to understand a virtual spatial environment described through natural language while planning & achieving...

Concise Chain-of-Thought (CCoT) Prompting

Traditional CoT comes at a cost of increased output token usage, CCoT prompting is a prompt-engineering technique which...

Understanding LLM User Experience & Expectation

This study surfaces valuable insights into the frequency of LLM use together with user intents, expectations and...

Meta Taxonomy Of LLM Correction & Refinement

A number of LLM techniques and classes of implementation are emerging, a fairly recent study created a meta taxonomy of...

Considering Large Language Model Reasoning Step Length

When using Chain of Thought, what is the optimal number of steps to use?

Chain Of Natural Language Inference (CoNLI)

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

Validating Low-Confidence LLM Generation

Large Language Model Hallucination Mitigation Techniques

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

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