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When LLMs are used to judge qualities just like the correctness, accuracy, or relevance of a bit of textual content, consistency is paramount. If an LLM displays inconsistent judgements, then its evaluations turn into unreliable and untrustworthy.
If an LLM evaluates the reasoning high quality of arguments, however contradicts itself by ranking an invalid argument as extra logically sound than a wonderfully legitimate one, then it fails as an arbiter of cause. Its evaluations lose credibility because of the mannequin’s personal lack of logical consistency.
When such inconsistencies seem, there is no such thing as a secure foundation for comparability between the LLM’s assessments of various items of textual content. If the mannequin arbitrarily contradicts itself, then sentences can’t be reliably ranked in opposition to each other primarily based on the mannequin’s inconsistent scorings.
In essence, inconsistency destroys the grounds for comparability that evaluations goal to supply within the first place. If an LLM can not reveal constant software of evaluation standards, then utilizing it to judge textual content loses all effectiveness and utility.
So, consistency in judgement and analysis is obligatory for LLMs employed to attain or decide textual qualities and options. With no excessive stage of stability in its assessments, grounded in a constant understanding of ideas being evaluated, the idea for comparability falls aside when leveraging LLM output as a type of analysis or scoring.
Sampling a number of options reveals consistency between outputs strongly correlates with high quality. Nonetheless, present consistency methods depend on extracting and matching closed-form solutions, proscribing their applicability. This text explores strategies to reinforce self-consistency with out such constraints, whereas additionally grounding choices in real-world information.
The Want for Self-Consistency
Regardless of speedy progress, logical failures and falsehoods proceed hindering dependable reasoning in state-of-the-art fashions. For advanced multi-step evaluation or free-form technology, fashions usually contradict themselves or invent unsupported info.
This manifests in two key methods — inconsistent open-ended technology, and incoherent inferences. When performing…
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