19 citations · 84 across the 26 of their papers we have counts for
16 papers · 1 filter
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Yiqiao Jin, Yiyang Wang, Lucheng Fu +7
Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains c…
MASCOT: Multi-Agent Socio-Collaborative Companion Systems
Yiyang Wang, Yiqiao Jin, Alex Cabral +1
Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing systems frequently suffer from persona col…
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
Junyu Luo, Bohan Wu, Xiao Luo +8
Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training…
SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding
Yiqiao Jin, Rachneet Kaur, Zhen Zeng +2
Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While multimoda…
Beyond Magic Words: Sharpness-Aware Prompt Evolving for Robust Large Language Models with TARE
Guancheng Wan, Lucheng Fu, Haoxin Liu +10
The performance of Large Language Models (LLMs) hinges on carefully engineered prompts. However, prevailing prompt optimization methods, ranging from heuristic edits and reinforcem…
Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings
Kartik Sharma, Yiqiao Jin, Rakshit Trivedi +1
Large language models (LLMs) acquire knowledge across diverse domains such as science, history, and geography encountered during generative pre-training. However, due to their stoc…
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Yiqiao Jin, Kartik Sharma, Vineeth Rakesh +4
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in re…
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations
Mohit Chandra, Siddharth Sriraman, Harneet Singh Khanuja +2
Limited access to mental healthcare, extended wait times, and increasing capabilities of Large Language Models (LLMs) has led individuals to turn to LLMs for fulfilling their menta…
Prototypical Reward Network for Data-Efficient RLHF
Jinghan Zhang, Xiting Wang, Yiqiao Jin +3
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF…
AgentReview: Exploring Peer Review Dynamics with LLM Agents
Yiqiao Jin, Qinlin Zhao, Yiyang Wang +4
Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of exist…
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Chengyuan Deng, Yiqun Duan, Xin Jin +15
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns…
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
Yiqiao Jin, Minje Choi, Gaurav Verma +2
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emot…
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Yiqiao Jin, Mohit Chandra, Gaurav Verma +3
Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like heal…
PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners
Yijia Xiao, Yiqiao Jin, Yushi Bai +10
The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless…
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
Yiqiao Jin, Xiting Wang, Yaru Hao +2
In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for…
Towards Fine-Grained Reasoning for Fake News Detection
Yiqiao Jin, Xiting Wang, Ruichao Yang +4
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move tow…