1 citations · 2 across the 5 of their papers we have counts for
6 papers
InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
Ke Li, Dong An, Xiaoling Zang +6
Low-bit activation quantization remains a major bottleneck in efficient large language model (LLM) deployment. The difficulty is not only that activations contain outliers, but tha…
Lemon Agent Technical Report
Haipeng Jiang, Kailong Ren, Zimo Yin +17
Recent advanced LLM-powered agent systems have exhibited their remarkable capabilities in tackling complex, long-horizon tasks. Nevertheless, they still suffer from inherent limita…
NavBench: Probing Multimodal Large Language Models for Embodied Navigation
Yanyuan Qiao, Haodong Hong, Wenqi Lyu +5
Multimodal Large Language Models (MLLMs) have demonstrated strong generalization in vision-language tasks, yet their ability to understand and act within embodied environments rema…
Action Tokenizer Matters in In-Context Imitation Learning
An Dinh Vuong, Minh Nhat Vu, Dong An +1
In-context imitation learning (ICIL) is a new paradigm that enables robots to generalize from demonstrations to unseen tasks without retraining. A well-structured action representa…
Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models
Malak Mansour, Ahmed Aly, Bahey Tharwat +3
Large Language Models (LLMs) such as GPT-4, trained on huge amount of datasets spanning multiple domains, exhibit significant reasoning, understanding, and planning capabilities ac…
Large Language Models Understand Layout
Weiming Li, Manni Duan, Dong An +1
Large language models (LLMs) demonstrate extraordinary abilities in a wide range of natural language processing (NLP) tasks. In this paper, we show that, beyond text understanding…