Search Results for author: Ziyan Jiang

Found 19 papers, 7 papers with code

#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention

1 code implementation EMNLP 2021 Yuji Zhang, Yubo Zhang, Chunpu Xu, Jing Li, Ziyan Jiang, Baolin Peng

It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).

ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging

1 code implementation27 Mar 2025 Haoming Xu, Shuxun Wang, Yanqiu Zhao, Yi Zhong, Ziyan Jiang, Ningyuan Zhao, Shumin Deng, Huajun Chen, Ningyu Zhang

This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models.

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

1 code implementation14 Oct 2024 Jiacheng Chen, Tianhao Liang, Sherman Siu, Zhengqing Wang, Kai Wang, YuBo Wang, Yuansheng Ni, Wang Zhu, Ziyan Jiang, Bohan Lyu, Dongfu Jiang, Xuan He, YuAn Liu, Hexiang Hu, Xiang Yue, Wenhu Chen

We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.

VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks

no code implementations7 Oct 2024 Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, Wenhu Chen

Our results show that VLM2Vec achieves an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets in MMEB.

Information Retrieval Language Modeling +7

Semi-Supervised Reward Modeling via Iterative Self-Training

1 code implementation10 Sep 2024 Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao

Reward models (RM) capture the values and preferences of humans and play a central role in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large language models (LLMs).

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs

1 code implementation9 Sep 2024 Jizhan Fang, Tianhe Lu, Yunzhi Yao, Ziyan Jiang, Xin Xu, Ningyu Zhang, Huajun Chen

To address this gap, we introduce CKnowEdit, the first-ever Chinese knowledge editing dataset designed to correct linguistic, factual, and logical errors in LLMs.

Benchmarking knowledge editing

LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs

no code implementations21 Jun 2024 Ziyan Jiang, Xueguang Ma, Wenhu Chen

In order to alleviate the imbalance, we propose a new framework LongRAG, consisting of a `long retriever' and a `long reader'.

4k Chunking +3

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

2 code implementations3 Jun 2024 YuBo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, Wenhu Chen

In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains.

MMLU Multi-task Language Understanding

RecMind: Large Language Model Powered Agent For Recommendation

no code implementations28 Aug 2023 Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Yingzhen Yang

While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints.

Explanation Generation Language Modeling +4

Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application

no code implementations9 Jun 2023 Elan Markowitz, Ziyan Jiang, Fan Yang, Xing Fan, Tony Chen, Greg Ver Steeg, Aram Galstyan

We propose to unify these approaches: using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would not be possible with either information source alone.

Knowledge Graphs Recommendation Systems

Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding

no code implementations23 May 2023 Zheng Chen, Ziyan Jiang, Fan Yang, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Aram Galstyan

This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +9

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