5 papers
GenProve: Learning to Generate Text with Fine-Grained Provenance
Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao +5
Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a c…
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
Iordanis Fostiropoulos, Muhammad Rafay Azhar, Abdalaziz Sawwan +8
We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike trad…
Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models
Yulin Huang, Yaxuan Luan, Jinxu Guo +2
This study addresses the issues of privacy protection and efficiency in instruction fine-tuning of large-scale language models by proposing a parameter-efficient method that integr…
Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Models
Xiangchen Song, Yulin Huang, Jinxu Guo +2
This study investigates a hybrid method for text classification that integrates deep feature extraction from large language models, multi-scale fusion through feature pyramids, and…
Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering
Xiangchen Song, Yuchen Liu, Yaxuan Luan +2
This study presents a controllable abstract summary generation method for large language models based on prompt engineering. To address the issues of summary quality and controllab…