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Hwanjun Song

ACL 2026×4ICML 2026×2对齐与偏好强化学习与后训练效率与系统推理多模态
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1 一作 · 6 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

6
作者位次 #2/3ICML 2026Poster· Deep Learning / Graph Neural Networks

Quantile-Free Uncertainty Quantification in Graph Neural Networks

Soyoung Park · Hwanjun Song · Sungsu Lim

作者位次 #6/7ACL 2026Poster· Natural Language Processing

Q u DAR : Query-Wise Dual-Perspective Adaptive Retrieval

Joeun Kim · Seunghyouk Yoon · Xuan-Bach Le · Youngeun Nam · Doyoung Kim · Hwanjun Song · Jae-Gil Lee

摘要

Retrieval-augmented generation(RAG) systems depend on retrieval modules to supply grounding evidence for large language models. While hybrid approaches combining sparse and dense retrievers improve performance, most rely on fixed weights that ignore query-specific and corpus-specific variation. S...

检索与知识数据与预训练OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines

Hwanjun Song

摘要

Much of the alignment tuning literature is organized around optimization objectives, while the construction of alignment data is often treated implicitly. In this survey, we adopt a data centric perspective and reframe alignment tuning as a pipeline design problem. We decompose alignment data con...

对齐与偏好智能体与工具强化学习与后训练OpenReview 项目页
作者位次 #4/5ACL 2026Findings· Findings of ACL

Distilling LLM Reasoning into Dense Encoders: Bridging the Accuracy-Efficiency Gap in Recommendation

Donghee Han · Daeyoung Roh · A Young Kim · Hwanjun Song · Mun Yong Yi

摘要

Large Language Models (LLMs) have shown remarkable potential in recommendation systems but suffer from prohibitive inference latency. Existing distillation approaches typically target Small Language Models (SLMs) or Conventional Recommendation Models (CRMs), face a critical trade-off between comp...

效率与系统推理对齐与偏好OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

Distilling Long- C o T Reasoning through Collaborative Step-wise Multi-Teacher Decoding

Taewon Yun · Jisu Shin · Jeonghwan Choi · Seunghwan Bang · Hwanjun Song

摘要

Distilling large reasoning models (LRMs) has become essential for making their Long-CoT reasoning capabilities practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches, which select complete reasoning traces post-hoc, overlook the collaborative po...

推理效率与系统强化学习与后训练OpenReview 项目页

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