14 citations · 31 across the 62 of their papers we have counts for
19 papers · 1 filter
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
Yingjie Zhu, Xuefeng Bai, Kehai Chen +3
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across diverse tasks. Despite great success, recent studies show that LVLMs encounter substantial limi…
LLM-based Discriminative Reasoning for Knowledge Graph Question Answering
Mufan Xu, Kehai Chen, Xuefeng Bai +3
Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs ofte…
LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning
Hongbin Zhang, Kehai Chen, Xuefeng Bai +2
Large language models (LLMs) have exhibited impressive multilingual reasoning capabilities, driven by extensive multilingual pre-training corpora and instruction fine-tuning data.…
Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen +4
Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechan…
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering
Yu Zhang, Kehai Chen, Xuefeng Bai +3
Knowledge graph question answering (KGQA) involves answering natural language questions by leveraging structured information stored in a knowledge graph. Typically, KGQA initially…
LLM-based Translation Inference with Iterative Bilingual Understanding
Andong Chen, Kehai Chen, Yang Xiang +5
The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance. However, incorrect understanding of the sent…
Dynamic Planning for LLM-based Graphical User Interface Automation
Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen +4
The advent of large language models (LLMs) has spurred considerable interest in advancing autonomous LLMs-based agents, particularly in intriguing applications within smartphone gr…
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models
Zelin Li, Kehai Chen, Lemao Liu +4
With the great advancements in large language models (LLMs), adversarial attacks against LLMs have recently attracted increasing attention. We found that pre-existing adversarial a…
MemLong: Memory-Augmented Retrieval for Long Text Modeling
Weijie Liu, Zecheng Tang, Juntao Li +2
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs…
LLM with Relation Classifier for Document-Level Relation Extraction
Xingzuo Li, Kehai Chen, Yunfei Long +1
Large language models (LLMs) have created a new paradigm for natural language processing. Despite their advancement, LLM-based methods still lag behind traditional approaches in do…
Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving
Andong Chen, Lianzhang Lou, Kehai Chen +5
Different from the traditional translation tasks, classical Chinese poetry translation requires both adequacy and fluency in translating culturally and historically significant con…
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms
Andong Chen, Lianzhang Lou, Kehai Chen +5
Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine translation. The key idea is guiding LLMs to generate translation…
A Survey on Human Preference Learning for Large Language Models
Ruili Jiang, Kehai Chen, Xuefeng Bai +6
The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancin…
On the Hallucination in Simultaneous Machine Translation
Meizhi Zhong, Kehai Chen, Zhengshan Xue +3
It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information. While many efforts have been mad…
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model
Hongbin Zhang, Kehai Chen, Xuefeng Bai +2
Large language models (LLMs) have showcased impressive multilingual machine translation ability. However, unlike encoder-decoder style models, decoder-only LLMs lack an explicit al…
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
Meizhi Zhong, Chen Zhang, Yikun Lei +5
Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a…
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
Yixin Ji, Yang Xiang, Juntao Li +6
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating…
Unsupervised Sign Language Translation and Generation
Zhengsheng Guo, Zhiwei He, Wenxiang Jiao +6
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns fro…
When Large Language Models Meet Vector Databases: A Survey
Zhi Jing, Yongye Su, Yikun Han +5
This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferatio…