13 papers · 1 filter
Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling
Hongjian Zou, Yue Ge, Qi Ding +2
Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less predictable than that of text-on…
Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
Yujie Lin, Kunquan Li, Yixuan Liao +2
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their outputs often exhibit social biases,…
GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
Min Zeng, Jingfei Sun, Xueyou Luo +4
In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tun…
SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant?
Xudong Lu, Haohao Gao, Renshou Wu +4
Large Language Models (LLMs) have become integral to daily life, especially advancing as intelligent assistants through on-device deployment on smartphones. However, existing LLM e…
EdgeInfinite-Instruct: Bridging SFT-Based Optimization and NPU-Level Efficiency for Edge Devices
Jiyu Chen, Poh Seng Lim, Shuang Peng +12
Deploying Transformer-based large language models (LLMs) on resource-constrained edge devices for long-sequence tasks remains challenging due to the quadratic time complexity of se…
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Kashun Shum, Yuzhen Huang, Hongjian Zou +5
Language model pretraining involves training on extensive corpora, where data quality plays a pivotal role. In this work, we aim to directly estimate the contribution of data durin…