法律辩护需要独特的权威语气、节奏停顿以强调重点以及情感智能。本研究调查了Gemini 2.5 Flash TTS和Gemini 2.5 Pro TTS模型在生成跨五种印地语系语言（泰米尔语、泰卢固语、孟加拉语、印地语和古吉拉特语）的合成法庭演讲中的表现。我们提出了一种提示框架，利用Gemini 2.5对五种语言的原生支持及其上下文感知节奏来生成不同的辩护人角色。大型语言模型（LLMs）的发展使文本到语音（TTS）技术的焦点从基本可懂性转向上下文感知、富有表现力的合成。在法律领域，合成语音必须传达权威性和特定的专业角色，这一任务在印度语言多样化的背景下变得显著复杂。模型表现出“单调权威”，在程序性信息传递上表现出色，但在需要动态语音调节和情感庄严的说服性辩护中表现不佳。孟加拉语和古吉拉特语的性能下降进一步突显了语音学前沿，需要未来改进。本研究强调了多语言TTS在程序性法律任务上的准备性，同时指出了在复制人类法律话语的说服艺术方面仍需解决的挑战。代码可在-https://github.com/naturenurtureelite/Synthesizing-the-Virtual-Advocate/tree/main获取。
英文摘要：
Legal advocacy requires a unique combination of authoritative tone, rhythmic pausing for emphasis, and emotional intelligence. This study investigates the performance of the Gemini 2.5 Flash TTS and Gemini 2.5 Pro TTS models in generating synthetic courtroom speeches across five Indic languages: Tamil, Telugu, Bengali, Hindi, and Gujarati. We propose a prompting framework that utilizes Gemini 2.5s native support for 5 languages and its context-aware pacing to produce distinct advocate personas. The evolution of Large Language Models (LLMs) has shifted the focus of TexttoSpeech (TTS) technology from basic intelligibility to context-aware, expressive synthesis. In the legal domain, synthetic speech must convey authority and a specific professional persona a task that becomes significantly more complex in the linguistically diverse landscape of India. The models exhibit a "monotone authority," excelling at procedural information delivery but struggling with the dynamic vocal modulation and emotive gravitas required for persuasive advocacy. Performance dips in Bengali and Gujarati further highlight phonological frontiers for future refinement. This research underscores the readiness of multilingual TTS for procedural legal tasks while identifying the remaining challenges in replicating the persuasive artistry of human legal discourse. The code is available at-https://github.com/naturenurtureelite/Synthesizing-the-Virtual-Advocate/tree/main
Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas
College of Computing and Informatics, Sungkyunkwan University（成均馆大学计算与信息学院）；Qatar Computing Research Institute, Hamad Bin Khalifa University（哈马德·本·哈利法大学卡塔尔计算研究所）
The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）
Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University（工业工程与运筹学系及数据科学研究所，哥伦比亚大学）；Decision, Risk, and Operations Division, Columbia Business School（决策、风险与运营部门，哥伦比亚商学院）
Illinois Tech - Chicago Kent College of Law（伊利诺伊理工大学芝加哥肯特法学院）；Bucerius Law School（布塞留斯法学院）；CodeX - The Stanford Center for Legal Informatics（CodeX——斯坦福法律信息学中心）
Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their firs…
Evaluating and Inducing Personality in Pre-trained Language Models
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Yuanpei College, Peking University（北京大学元培学院）；National Key Laboratory of General Artificial Intelligence, BIGAI（北京通用人工智能研究院通用人工智能全国重点实验室）；Beijing Jiaotong University（北京交通大学）
