most citedTEILP: Time Prediction over Knowledge Graphs via Logical Reasoning

2 citations · 2 across the 3 of their papers we have counts for

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cs.CL2024

Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model

Siheng Xiong, Ali Payani, Yuan Yang +1

Enhancing the reasoning capabilities of language models (LMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of…

cs.CL2024

The Compressor-Retriever Architecture for Language Model OS

Yuan Yang, Siheng Xiong, Ehsan Shareghi +1

Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to per…

cs.CL2024

Can LLMs Reason in the Wild with Programs?

Yuan Yang, Siheng Xiong, Ali Payani +2

Large Language Models (LLMs) have shown superior capability to solve reasoning problems with programs. While being a promising direction, most of such frameworks are trained and ev…

cs.CL2024

TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs

Siheng Xiong, Yuan Yang, Faramarz Fekri +1

Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general. However…

cs.CL20242 cited

TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning

Siheng Xiong, Yuan Yang, Ali Payani +2

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential t…