Language model “hallucinations” might always be an issue to some degree, but Meta’s recent investment in Scale AI shows the importance of techniques such as "human-in-the-loop" data labeling.
“Edge cases” often are the issue, as human language is inherently ambiguous. A single word can have multiple meanings depending on context, tone, and cultural nuances. Machines struggle with this without explicit human guidance. And that’s where humans help.
When undertaking tasks involving sentiment analysis, summarization or dialogue generation, subjectivity is involved. There isn't always one "correct" answer, and human guidance is helpful there.
It often is noted that language models do not possess common sense or real-world knowledge in the way humans do, so HITL helps prevent models from generating nonsensical or logically flawed responses.
And while AI models are generally good at learning from patterns, they often struggle with "edge cases" involving unusual, rare, or complex scenarios that aren't well-represented in the training data.
Human annotators can identify, interpret, and correctly label these edge cases.
Likewise, human-in-the-loop processes allow for the identification and mitigation of biases in the source data.
Also, HITL helps models LLM generate responses that are more aligned with human preferences and ethical guidelines: safe, useful and contextually appropriate for human users.
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