2 citations · 3 across the 13 of their papers we have counts for
14 papers
Self-Distillation with Verbal Regularization
Jiazheng Li, Yawei Li, Junru Lu +7
On-policy self-distillation (OPSD) trains a large language model using supervision from a teacher instantiated from the same model as the student. The teacher is conditioned on pri…
Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge
Zhuoshi Pan, Junru Lu, Yan Qian +3
Factual question answering (QA) typically assumes a single canonical answer, obscuring whether large language models (LLMs) retain divergent accounts of long-tail facts. To address…
ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL
Zhuoshi Pan, Qizhi Pei, Junru Lu +4
Long-horizon agentic tasks require large language models (LLMs) to iteratively retrieve, integrate, and maintain dispersed information across multi-turn interactions, but preservin…
Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision
Zhixiang Wei, Yi Li, Zhehan Kan +38
Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, lea…
Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
Junru Lu, Jiarui Qin, Lingfeng Qiao +35
We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that r…
SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space
Zhenyi Shen, Junru Lu, Lin Gui +4
Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse attention to full-attention-trained m…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Junru Lu, Jiazheng Li, Guodong Shen +5
Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-p…
Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time
Jiazheng Li, Yuxiang Zhou, Junru Lu +4
Although preference optimization methods have improved reasoning performance in Large Language Models (LLMs), they often lack transparency regarding why one reasoning outcome is pr…
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
Wenhao Liu, Siyu An, Junru Lu +8
Role-Playing Agents (RPAs) have shown remarkable performance in various applications, yet they often struggle to recognize and appropriately respond to hard queries that conflict w…
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
Shujuan Zhao, Lingfeng Qiao, Kangyang Luo +3
Large language models (LLMs) have become powerful tools for advancing natural language processing applications in the financial industry. However, existing financial LLMs often fac…
FinVerse: An Autonomous Agent System for Versatile Financial Analysis
Siyu An, Qin Li, Junru Lu +2
With the significant advancements in cognitive intelligence driven by LLMs, autonomous agent systems have attracted extensive attention. Despite this growing interest, the developm…
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
Junru Lu, Jiazheng Li, Siyu An +4
Direct Preference Optimization (DPO) has emerged as a prominent algorithm for the direct and robust alignment of Large Language Models (LLMs) with human preferences, offering a mor…
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
Junru Lu, Siyu An, Min Zhang +3
When the quality of naive prompts is carefully optimized by human experts, the task performance of large language models (LLMs) can be significantly improved. However, expert-based…