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Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on knowledge boundaries of LLMs has …
Chenghao Xiao, Hou Pong Chan, Hao Zhang, Mahani Aljunied, Lidong Bing, Noura Al Moubayed, Yu Rong
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Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
FineReason: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving
Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent …
Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong
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FineReason: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving
Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger
Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging …
Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Liu
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Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods …
Xiaopeng Li, Xiangyang Li, Hao Zhang, Zhaocheng Du, Pengyue Jia, Yichao Wang, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
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SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
Hesitation and Tolerance in Recommender Systems
User interactions in recommender systems are inherently complex, often involving behaviors that go beyond simple acceptance or …
Kuan Zou, Aixin Sun, Xuemeng Jiang, Yitong Ji, Hao Zhang, Jing Wang, Ruijie Guo
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Hesitation and Tolerance in Recommender Systems
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering
Multimodal Large Language Models (MLLMs) have demonstrated great zero-shot performance on visual question answering (VQA). However, …
Weixi Weng, Jieming Zhu, Hao Zhang, Xiaojun Meng, Rui Zhang, Chun Yuan
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Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users’ …
Bo Chen, Xinyi Dai, Huifeng Guo, Wei Guo, Weiwen Liu, Yong Liu, Jiarui Qin, Ruiming Tang, Yichao Wang, Chuhan Wu, Yaxiong Wu, Hao Zhang
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All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
CoIR: A Comprehensive Benchmark for Code Information Retrieval Models
Despite the substantial success of Information Retrieval (IR) in various NLP tasks, most IR systems predominantly handle queries and …
Xiangyang Li, Kuicai Dong, Yi Quan Lee, Wei Xia, Yichun Yin, Hao Zhang, Yong Liu, Yasheng Wang, Ruiming Tang
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CoIR: A Comprehensive Benchmark for Code Information Retrieval Models
How Can Recommender Systems Benefit from Large Language Models: A Survey
With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for …
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang
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How Can Recommender Systems Benefit from Large Language Models: A Survey
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) …
Huanshuo Liu, Hao Zhang, Zhijiang Guo, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu
Preprint Cite Code 📌ArXiv'24
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control