Researchers have developed a lightweight method for quantifying uncertainty in neural network predictions using two key approximations: a first-order Taylor expansion and an isotropy assumption. The a…
Researchers introduced a multi-factor scoring system to evaluate large language model responses, assessing accuracy, conciseness, factual consistency, readability, and coherence. Tests on the Truthful…
A researcher found that a clean implementation of abliteration on Qwen3.5-27B costs only about 1.4 TruthfulQA points, far less than the 5.5+ points lost by HuiHui AI's crude method, confirming that mo…
Researchers found that a linear probe applied to mid-layer hidden states of quantized large language models can detect hallucinations with up to 1.000 AUROC, significantly outperforming sampling-based…
Researchers have developed SERC, a semantic error correction framework inspired by LDPC codes that treats LLM text generation as a noisy communication channel to detect and fix hallucinations. The tra…
Researchers have developed MechELK, a three-stage framework that uses mechanistic interpretability to extract hidden factual and reasoning knowledge from large language models. The framework, which co…