413 citations · 581 across the 17 of their papers we have counts for
5 papers · 1 filter
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Xiang Yue, Xingwei Qu, Ge Zhang +5
We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on MathInstruct, o…
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
Minxin Du, Xiang Yue, Sherman S. M. Chow +3
Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-propagation, safeguarding training data from privacy leakage, particularly membership in…
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
Lingbo Mo, Shijie Chen, Ziru Chen +10
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking…
Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate
Boshi Wang, Xiang Yue, Huan Sun
Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive performance in complex reasoning tasks. However, it is difficult to know whether the models are reasoni…
Automatic Evaluation of Attribution by Large Language Models
Xiang Yue, Boshi Wang, Ziru Chen +3
A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. How…