LLM Hallucination Correction Via Training-Time Correction, Generation-Time Correction & Augmentation Tools.

These methods are not mutually exclusive, and can be implemented in parallel for highly scaleable enterprise implementations.

4 min readOct 5, 2023

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The advent of LLMs, Foundation Models and Generative AI have given rise to a gold rush of sorts, with companies in a mad dash to develop the ultimate product to leverage the power of these models.

This gave rise to ambitious marketing (to say the least) and a susceptibility to identify one product or a single approach which will solve for all LLM implementation challenges.

The reality is that there is no elixir of sorts to remedy all implementation challenges; the solution most probably lies with a combination of technologies and principles.

This article covers three identified and accepted building blocks for LLM-based implementations; which can be used in concert or alone.

Training Time Correction

This approach is focused on a model level, where the model is fine-tuned with custom data.

Generation Time

Generation Time can also be referred to as inference time.

In generation time correction, a common theme is to make reasoning decisions on top of the base LLM in order to make them more reliable.

Another promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output.

Techniques leveraging automated feedback — either produced by the LLM itself or some external system, are of particular interest as they are a promising way to make LLM-based solutions more practical and deployable with minimal human feedback.

COVE also uses a related self-consistency approach, but without the multi-agent (multi-LLM) debate concept. Read more here.

Augmentation Tools

A third approach is to use external tools to help mitigate hallucinations, rather than relying solely on the abilities of the language model itself.

For example, retrieval-augmented generation can decrease hallucinations by using factual documents for grounding or chain-of-thought verification.

Other approaches include using tools for fact-checking or linking to external documents with attribution.

A majority of the methods for reducing hallucination can be divided into roughly three categories: training-time correction, generation-time correction and via augmentation (tool-use). ~ Source

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