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Zhengliang Shi

ACL 2026×2多语言与语言学多模态强化学习与后训练社会与人本评测与基准
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一作贡献30
核心/参与5
方向广度10
合作网络12
1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #4/17ACL 2026Poster· Natural Language Processing

B aton V oice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLM s

Yue Wang · Ruotian Ma · Xingyu Chen · Zhengliang Shi · Morunliu Yang · Wanshun Chen · Huang Liu · Jiadi Yao · Xin He · Qu Yang · Qingxuan Jiang · Fanghua Ye · Juntao Li · Zhaopeng Tu · Xiaolong Li · Liefeng Bo · Min Zhang

摘要

The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilitie...

多语言与语言学多模态强化学习与后训练OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator

Zhengliang Shi · Ruotian Ma · Jen-Tse Huang · Xinbei Ma · Xingyu Chen · Mengru Wang · Qu Yang · Yue Wang · Fanghua Ye · Ziyang Chen · Shanyi Wang · CIXING LI · Wenxuan Wang · Zhaopeng Tu · Xiaolong Li · Zhaochun Ren · Liefeng Bo

摘要

Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that guide these models when distributing scarce societal resources remain largely unexamined. To address this, we introduce the Social Welfare Function...

社会与人本评测与基准智能体与工具OpenReview 项目页

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