Výsledky vyhledávání - "Cheng, Monica"

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Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective

Autor: Zeng, Shenglai, Zhang, Jiankun, Li, Bingheng, Lin, Yuping, Zheng, Tianqi, Everaert, Dante, Lu, Hanqing, Liu, Hui, Xing, Yue, Cheng, Monica Xiao, Tang, Jiliang

Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, ofte

Externí odkaz: http://arxiv.org/abs/2411.14572

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RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning

Autor: Wang, Haoyu, Liu, Tianci, Li, Ruirui, Cheng, Monica, Zhao, Tuo, Gao, Jing

Pre-trained language models, trained on large-scale corpora, demonstrate strong generalizability across various NLP tasks. Fine-tuning these models for specific tasks typically involves updating all parameters, which is resource-intensive. Parameter-

Externí odkaz: http://arxiv.org/abs/2406.10777

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BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering

Autor: Wang, Haoyu, Li, Ruirui, Jiang, Haoming, Tian, Jinjin, Wang, Zhengyang, Luo, Chen, Tang, Xianfeng, Cheng, Monica, Zhao, Tuo, Gao, Jing

Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowle

Externí odkaz: http://arxiv.org/abs/2402.11129

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Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions

Autor: Xu, Yinglun, Suresh, Tarun, Gumaste, Rohan, Zhu, David, Li, Ruirui, Wang, Zhengyang, Jiang, Haoming, Tang, Xianfeng, Yin, Qingyu, Cheng, Monica Xiao, Zeng, Qi, Zhang, Chao, Singh, Gagandeep

Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one applies a reinforcement learning step after a reward modeling step has be

Externí odkaz: http://arxiv.org/abs/2401.00330

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Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based

Externí odkaz: http://arxiv.org/abs/2307.09688

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A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy

Autor: Dai, Enyan, Cui, Limeng, Wang, Zhengyang, Tang, Xianfeng, Wang, Yinghan, Cheng, Monica, Yin, Bing, Wang, Suhang

Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can fool the GNN model to make desired predictions of the attacker. In additi

Externí odkaz: http://arxiv.org/abs/2306.08604

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