9 citations · 14 across the 12 of their papers we have counts for
21 papers · 1 filter
Progressive Multimodal Reasoning via Active Retrieval
Guanting Dong, Chenghao Zhang, Mengjie Deng +3
Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scen…
Smaller Language Models Are Better Instruction Evolvers
Tingfeng Hui, Lulu Zhao, Guanting Dong +3
Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they…
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Guanting Dong, Yutao Zhu, Chenghao Zhang +3
Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs). However, the difficulty of aligning the…
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
Guanting Dong, Keming Lu, Chengpeng Li +4
One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhan…
DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning
Chengpeng Li, Guanting Dong, Mingfeng Xue +3
Large language models (LLMs) have made impressive progress in handling simple math problems, yet they still struggle with more challenging and complex mathematical tasks. In this p…
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task
Jinxu Zhao, Guanting Dong, Yueyan Qiu +4
In a realistic dialogue system, the input information from users is often subject to various types of input perturbations, which affects the slot-filling task. Although rule-based…