arXiv Computation and Language
Aug 28

Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs

The paper introduces Information-Guided Frontier Decoding (IGFD), a training‑free strategy for diffusion multimodal language models that prioritizes the commitment of reliable semantic tokens over fragile structural ones. IGFD ranks candidates by token confidence, neighborhood uncertainty, and structural commitment risk, and uses a dynamic frontier to limit selection to locally expandable regions. Across multiple multimodal benchmarks, IGFD consistently outperforms existing decoding methods while requiring no extra training or forward passes.

By Xingyou Fang, Jingxing Zhong, Xiaosong Yuan, Xiaofeng Zhang
arXiv Machine Learning
Sep 21

Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA

The paper presents the first large‑scale benchmark for uncertainty quantification (UQ) calibration in long‑form scientific question answering, evaluating four UQ methods on 685,000 responses from up to 20 large language models across seven datasets. It shows that instruction tuning leads to token‑level probability polarization, undermining token‑level uncertainty signals, while reasoning model families differ in how they handle this effect. Only semantic consistency—consistency of the final answer—provides well‑calibrated outputs, demonstrating that semantic calibration remains robust in multi‑step, dependency‑rich reasoning.

By Philip M\"uller, Nicholas Popovi\v{c}, Michael F\"arber, Peter Steinbach
arXiv AI
Jun 3

SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory

arXiv:2511. 16275v4 Announce Type: replace-cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when uncertain, thereby avoiding hallucinations, i.

By Xingtao Zhao, Hao Peng, Dingli Su, Xianghua Zeng, Chunyang Liu, Jinzhi Liao, Philip S. Yu