4 citations · 9 across the 45 of their papers we have counts for
12 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…
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…
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…