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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
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CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
Evaluating the External and Parametric Knowledge Fusion of Large Language Models
Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by …
Hao Zhang, Yuyang Zhang, Xiaoguang Li, Wenxuan Shi, Haonan Xu, Huanshuo Liu, Yasheng Wang, Lifeng Shang, Qun Liu, Yong Liu, Ruiming Tang
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Evaluating the External and Parametric Knowledge Fusion of Large Language Models