33 citations · 93 across the 16 of their papers we have counts for
17 papers
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning
Liang Chen, Hongcheng Gao, Tianyu Liu +5
Vision-Language Models (VLMs) excel in many direct multimodal tasks but struggle to translate this prowess into effective decision-making within interactive, visually rich environm…
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
Liang Chen, Haozhe Zhao, Tianyu Liu +4
In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat and Video-LLaVA.…
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
Liang Chen, Yichi Zhang, Shuhuai Ren +7
We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchma…
Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond
Liang Chen, Yichi Zhang, Shuhuai Ren +6
In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) h…
Guiding AMR Parsing with Reverse Graph Linearization
Bofei Gao, Liang Chen, Peiyi Wang +2
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic gr…
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Haozhe Zhao, Zefan Cai, Shuzheng Si +7
Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utiliz…
MIT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
Lei Li, Yuwei Yin, Shicheng Li +9
Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress i…
On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
Liang Chen, Shuming Ma, Dongdong Zhang +2
While multilingual neural machine translation has achieved great success, it suffers from the off-target issue, where the translation is in the wrong language. This problem is more…
Large Language Models are not Fair Evaluators
Peiyi Wang, Lei Li, Liang Chen +7
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of respo…
On the Pareto Front of Multilingual Neural Machine Translation
Liang Chen, Shuming Ma, Dongdong Zhang +2
In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual…
A Two-Stage Method for Chinese AMR Parsing
Liang Chen, Bofei Gao, Baobao Chang
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation. We firstly propose a two-stage method to conduct Chinese AMR Parsing with alignment generat…
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Liang Chen, Peiyi Wang, Runxin Xu +3
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enh…
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation
Liang Chen, Runxin Xu, Baobao Chang
Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models. However, we argue that simply applying both techniques can be conflictin…
Hierarchical Curriculum Learning for AMR Parsing
Peiyi Wang, Liang Chen, Tianyu Liu +4
Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence…
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Yulong Chen, Yang Liu, Liang Chen +1
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarizati…
Crossed-Time Delay Neural Network for Speaker Recognition
Liang Chen, Yanchun Liang, Xiaohu Shi +2
Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems. In this paper we introduce a novel structure Crossed-Time Delay Neural Ne…