Exposing the Unsaid: Visualizing Hidden LLM Bias through Stochastic Path Aggregation

arXiv:2606.19344v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit representational and syntactic biases that are difficult to evaluate due to the stochastic nature of text generation. Standard auditing methods rely on a single output inspection or static automated metrics. These approaches obscure the underlying probability distributions and fail to capture biases hidden in lower-probability generation branches. This paper introduces TreeTracer, a visual analytics tool designed to evaluate LLM bias through aggregated comparison. Using a systematic perturbation analysis pipel

Source: arXiv cs.CL — read the full report at the original publisher.

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