55 citations · 72 across the 38 of their papers we have counts for
10 papers · 1 filter
BenTo: Benchmark Task Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun +1
Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates ho…
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
Ming Li, Yanhong Li, Tianyi Zhou
What makes a difference in the post-training of LLMs? We investigate the training patterns of different layers in large language models (LLMs) through the lens of the gradient. We…
PFID: Privacy First Inference Delegation Framework for LLMs
Haoyan Yang, Zhitao Li, Yong Zhang +4
This paper introduces a novel privacy-preservation framework named PFID for LLMs that addresses critical privacy concerns by localizing user data through model sharding and singula…
RuleR: Improving LLM Controllability by Rule-based Data Recycling
Ming Li, Han Chen, Chenguang Wang +3
Large language models (LLMs) still lack delicate controllability over their responses, which is critical to enhancing their performance and the user experience. However, curating s…
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang +5
Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primar…
A Survey on Knowledge Distillation of Large Language Models
Xiaohan Xu, Ming Li, Chongyang Tao +6
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such…
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
Ming Li, Jiuhai Chen, Lichang Chen +1
Making LLMs speak for different, especially minority groups of people, and generate statements supporting their diverse or even controversial perspectives is critical to creating a…
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +3
Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the tra…
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Ming Li, Yong Zhang, Shwai He +5
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both…
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning
Yong Zhang, Hanzhang Li, Zhitao Li +4
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. However, their performance can be hampered by inhere…