48 citations · 74 across the 8 of their papers we have counts for
8 papers
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
Junpeng Liu, Yifan Song, Bill Yuchen Lin +4
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehe…
AttributionBench: How Hard is Automatic Attribution Evaluation?
Yifei Li, Xiang Yue, Zeyi Liao +1
Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence. However, evaluating the answer's attribution, i.e., wh…
Data Engineering for Scaling Language Models to 128K Context
Yao Fu, Rameswar Panda, Xinyao Niu +4
We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in part…
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…