Junhao Dong
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The paper introduces BiAxisAudit, a novel framework that evaluates LLM bias by analyzing bias scores across multiple prompt formats and within the internal inconsistency of model responses, revealing that simple aggregate scores are unreliable.
The paper introduces an adaptive probe-based steering method that significantly improves the robustness and effectiveness of LLM jailbreaking without requiring extra prompts or manual tuning.
This paper investigates whether model compression techniques (like quantization and pruning) preserve a Large Language Model's ability to quantify its own uncertainty, finding that accuracy-only evaluation is insufficient for assessing deployment readiness.
Papers
Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction
Yujia Tong, Yuxi Wang, Yunyang Wan, Tian Zhang +2 more
This paper investigates whether model compression techniques (like quantization and pruning) preserve a Large Language Model's ability to quantify its own uncertainty, finding that accuracy-only evalu…