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Developer's custom Japanese LLM struggles with positive sentiment due to data imbalance

By PulseAugur Editorial · [1 sources] ·

A developer built a Japanese conversational AI model named Lilas from scratch, which exhibited a peculiar tendency to respond to positive news with phrases like "That sounds tough." Initial analysis of raw training data suggested an overabundance of this phrase. However, a more nuanced weighting of the data, considering how often specific files were repeated during training, revealed that the phrase "I'm glad to hear that" was actually much rarer, appearing significantly less often than "That sounds tough." AI

IMPACT Highlights the critical importance of balanced training data for nuanced conversational AI.

RANK_REASON Developer blog post detailing the process of building and debugging a custom LLM.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer's custom Japanese LLM struggles with positive sentiment due to data imbalance

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4 / 100
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Developer blog post detailing the process of building and debugging a custom LLM.
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model release, other
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High
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Kate ·

    Why my from-scratch Japanese LLM says "That sounds tough" to good news

    <h2> TL;DR </h2> <p>Lilas is a ~40M-parameter Japanese chat model I built from scratch, tokenizer included. It kept answering cheerful remarks with "That sounds tough, please don't overdo it." A raw count of the training data pointed at one over-used phrase. Weighting the counts …

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