Main point: seeking information on this potential strategy for improving neural network interpretability, alignment, and reliability.
Segmenting training data and injecting unique "barcoding" tokens into each data subset. These unique tokens should occasionally show up in generated outputs at a frequency correlated with the importance of each training subset to the output, providing insights into model decision making.

I've been unable to find out who (if anyone) is working on this strategy that came to mind, based loosely on genetic sequencing and bioinformatics techniques.

I would be very grateful to the community for pointing me in the right direction, or providing discussion and feedback on the practicality and potential pitfalls of this technique.

Hi, my name is Sean. I'm and engineer (biomolecular, informatics, and mechanical) fascinated by neuroscience, minds, and more recently with language and diffusion models. New here, but not new to research or data analysis.

Here is a summary of the proposed technique. Feel free to critique or comment on it as well.

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Enhancing Interpretability in Large Language Models through Barcode Injection Tracing

Sean Hacking

April 12, 2024

Abstract:

An approach for enhancing the interpretability of large language models (LLMs) by injecting unique identifier strings, or "barcodes," into specific subsets of training data. The proposed technique aims to trace the influence of particular data subsets on model outputs, providing insights into the model's decision-making process and improving transparency. There are many unanswered questions to be explored regarding efficacy of this approach, challenges in implementation, and strategies for addressing these challenges.

Introduction:

Large language models (LLMs) have achieved remarkable performance across a wide range of natural language processing tasks. However, the opacity of these models raises concerns about their interpretability, accountability, and trustworthiness. Guaranteeing model alignment appears nearly impossible without improved interpretability tools. Developing techniques to trace the influence of training data on model outputs is crucial for enhancing transparency and understanding the reasoning behind generated text.

Proposed Technique:

The proposed barcode injection tracing technique involves the following steps:

1. Classifying training data int...

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