Confronting Bias and Staleness in AI's Quest for Truth

The biggest difficulty in developing large language models (LLMs) is dealing with data quality and bias. Ensuring unbiased, fair, and representative training data is an ongoing challenge.


  • The main issue brought up is data quality and bias. Biased training data leads to biased model outputs, which is a major problem for sensitive applications.
  • Striving for unbiased and representative data is complex and continuous work. However, as AI collects more real-time data, this issue may improve.
  • Bias manifests in many forms - coding, data collection methods, regulatory factors, media singularities vs pluralities, etc. Humans also have inherent biases.
  • Controlling, overseeing and cooperating with AI systems given biases is very difficult currently. Understanding perceptions of difficulty and danger with AI is important.
  • Keeping knowledge "fresh" by pruning non-essential elements and constantly updating is another challenge as knowledge generation quickens.
  • Possible solutions include adding time dimensions to knowledge vectors and reranking by timestamp.
  • Overall, data quality, bias, and knowledge freshness seem to be the biggest difficulties cited in developing LLMs. Control, oversight and human cooperation are also complex.


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