LLM Extraction Raises Questions About Deterministic Score Authenticity Amid Provenance Laundering Concerns
| Source: Dev.to | Original article
Deterministic scores may not be truly deterministic when LLMs extract inputs. LLM judgment can bypass deterministic gates.
A recent investigation has shed light on the issue of "provenance laundering" in large language models (LLMs), where a scoring function's determinism is compromised by the LLM's judgment. This means that even if a scoring function is designed to be deterministic, the LLM's input can introduce non-determinism, making the overall process less reliable.
This matters because determinism is crucial in many applications, such as decision-making systems, where consistency and reproducibility are essential. If an LLM's output varies for the same input, it can have significant consequences for the accuracy and fairness of the system. The problem of non-determinism in LLMs has been noted in various studies, which have highlighted the need for a more systematic investigation into this issue.
As researchers and practitioners seek to address this problem, we can expect to see more guidance on how to make LLM-based systems more deterministic in practice. This may involve developing new techniques for mitigating non-determinism or designing systems that can account for the potential variability of LLM outputs. By acknowledging and addressing the issue of provenance laundering, developers can work towards creating more reliable and trustworthy AI systems.
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