14 citations · 29 across the 28 of their papers we have counts for
25 papers · 1 filter
A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models
Fanji Yang, Huiyao Chen, Xi Yu +3
Online reviews shape consumer decisions, platform governance, and corporate reputation.Fake reviews compromise this information channel by injecting deceptive evidence into rating…
KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill
Yunxin Li, Jinchao Li, Shibo Su +7
OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mech…
Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking
Xinping Zhao, Jiaxin Xu, Ziqi Dai +10
As retrieval systems scale, effective and efficient reranking becomes increasingly important. However, most existing encoder- and decoder-based rerankers jointly process every quer…
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
Xinxin Li, Huiyao Chen, Meishan Zhang +6
Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved…
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment
Bobo Li, Rui Wu, Zibo Ji +5
Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-a…
ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution
Shouzheng Huang, Meishan Zhang, Baotian Hu +1
Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evolving tool repositories, exi…
LMEB: Long-horizon Memory Embedding Benchmark
Xinping Zhao, Xinshuo Hu, Jiaxin Xu +9
Embedding models are increasingly used as retrievers in memory-augmented systems, but are typically evaluated on traditional passage retrieval benchmarks. It remains unclear whethe…
Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning
Zhuoen Chen, Dongfang Li, Meishan Zhang +2
Large Language Models (LLMs) face significant challenges in long-context processing, including quadratic computational costs, information forgetting, and the context fragmentation…
Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
Yunxin Li, Xinyu Chen, Shenyuan Jiang +9
We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li +6
In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant q…
On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Meishan Zhang, Xin Zhang, Xinping Zhao +3
Text embeddings have attracted growing interest due to their effectiveness across a wide range of natural language processing (NLP) tasks, including retrieval, classification, clus…
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation
Xinping Zhao, Shouzheng Huang, Yan Zhong +4
Retrieval-Augmented Generation (RAG) effectively improves the accuracy of Large Language Models (LLMs). However, retrieval noises significantly undermine the quality of LLMs' gener…
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models
Xinxin Li, Huiyao Chen, Chengjun Liu +4
Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). Although generative decoder-based large language models (LLMs) have achieved remarkable success…
KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
Xinping Zhao, Xinshuo Hu, Zifei Shan +14
Recent advancements in Large Language Models (LLMs)-based text embedding models primarily focus on data scaling or synthesis, yet limited exploration of training techniques and dat…
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing
Yifan Lu, Jing Li, Yigeng Zhou +7
Large language models (LLMs) exhibit impressive language capabilities but remain vulnerable to malicious prompts and jailbreaking attacks. Existing knowledge editing methods for LL…
Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing
Peiming Guo, Meishan Zhang, Jianling Li +2
Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate auto…
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Xin Zhang, Yanzhao Zhang, Wen Xie +7
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a comb…
An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
Peiming Guo, Sinuo Liu, Yanzhao Zhang +4
Photo-Sharing Multi-modal dialogue generation requires a dialogue agent not only to generate text responses but also to share photos at the proper moment. Using image text caption…
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Xin Zhang, Yanzhao Zhang, Dingkun Long +10
We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder…
LLM-Driven Multimodal Opinion Expression Identification
Bonian Jia, Huiyao Chen, Yueheng Sun +2
Opinion Expression Identification (OEI) is essential in NLP for applications ranging from voice assistants to depression diagnosis. This study extends OEI to encompass multimodal i…
Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
Huiyao Chen, Yu Zhao, Zulong Chen +4
Hierarchical text classification (HTC) is an important task with broad applications, while few-shot HTC has gained increasing interest recently. While in-context learning (ICL) wit…
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Longhui Zhang, Dingkun Long, Meishan Zhang +3
Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on th…
In-Context Learning for Few-Shot Nested Named Entity Recognition
Meishan Zhang, Bin Wang, Hao Fei +1
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested…
LLM-enhanced Self-training for Cross-domain Constituency Parsing
Jianling Li, Meishan Zhang, Peiming Guo +2
Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-tr…