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20242026
most citedImprove Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model

Zhe Huang, Peng Wang, Yan Zheng +2

Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering appr…

cs.CL2025

KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG

Yongjian Li, HaoCheng Chu, Yukun Yan +7

Retrieval-Augmented Generation (RAG) equips large language models with external knowledge and is central to knowledge-intensive tasks. As RAG systems enter real-world use, generato…

cs.CL2025★ 2 cited

Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models

Jialiang Wu, Yi Shen, Sijia Liu +4

Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess…

cs.CL2024

UniMem: Towards a Unified View of Long-Context Large Language Models

Junjie Fang, Likai Tang, Hongzhe Bi +12

Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-…

eess.SY2024

Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents

Jiyi Wang, Likai Tang, Huimiao Chen +2

Replay is a powerful strategy to promote learning in artificial intelligence and the brain. However, the conditions to generate it and its functional advantages have not been fully…