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Nikolaos Pappas

ACL 2026×2ICML 2026可解释性安全多模态强化学习与后训练推理
新星分资深
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0 一作 · 3 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

3
作者位次 #4/4ICML 2026Poster· Deep Learning / Large Language Models

Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation

Zheng Qi · Chao Shang · Evangelia Spiliopoulou · Nikolaos Pappas

安全多模态OpenReview
作者位次 #6/6ACL 2026Findings· Findings of ACL

Balancing Classification and Calibration Performance in Decision-Making LLM s via Calibration Aware Reinforcement Learning

Duygu Nur Yaldiz · Evangelia Spiliopoulou · Zheng Qi · Siddharth Varia · Srikanth Doss · Nikolaos Pappas

摘要

Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this wo...

强化学习与后训练推理可解释性OpenReview 项目页
作者位次 #3/8ACL 2026Findings· Findings of ACL

MEAV : Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrum

Sadat Shahriar · Zheng Qi · Nikolaos Pappas · Srikanth Doss · Kishaloy Halder · Monica Sunkara · Manuel Mager · Yassine Benajiba

摘要

Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and infer...

对齐与偏好社会与人本可解释性OpenReview 项目页

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