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Kore.ai Technical Blog

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Large Language Model Hallucination Mitigation Techniques

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

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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...

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What Is Multi-Task Language Understanding or MMLU?

With one of the Google LLM launches, Gemini Ultra was shown as having exceeded human expert level using the MMLU...

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Large Language Model Programs

A study named Large Language Model Programs explores how LLMs can be implemented in applications.

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Data Delivery To Large Language Models

It has been said that every AI strategy should start with a data strategy. The Data Strategy should consist of four...

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Generative AI Trends to Watch in 2025 & Beyond

We are six weeks into 2025, and already a number of developments have taken the world by storm. In article I build on...

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As-Needed Decomposition & Planning Using Large Language Models — ADaPT

There has been a shift with regard to LLM implementations to decompose tasks and solve each step in an interactive...

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Self-Consistency For Chain-Of-Thought Prompting

The paper on Self-Consistency Prompting was published 7 March 2023, as an improvement on Chain-Of-Thought Prompting. At...

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The Anatomy Of Chain-Of-Thought Prompting (CoT)

The research on Chain-Of-Thought Prompting (CoT) was published on 10 Jan 2023, and since then, there has been a...

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Contrastive Chain-Of-Thought Prompting

Contrastive Chain-of-Thought Prompting (CCoT) uses both positive & negative demonstrations to improve LLM reasoning.

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