Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Xiong, Siheng"'
Autor:
Yu, Longxuan, Chen, Delin, Xiong, Siheng, Wu, Qingyang, Liu, Qingzhen, Li, Dawei, Chen, Zhikai, Liu, Xiaoze, Pan, Liangming
Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While large language models (LLMs) can generate rationales for their outputs, their ability to reliably perform ca
Externí odkaz:
http://arxiv.org/abs/2410.16676
Enhancing the reasoning capabilities of large language models (LLMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making. Humans excel at these tasks by leveraging deliberate planning with an internal world
Externí odkaz:
http://arxiv.org/abs/2410.03136
In this paper, we address the problem of lossy semantic communication to reduce uncertainty about the State of the World (SotW) for deductive tasks in point to point communication. A key challenge is transmitting the maximum semantic information with
Externí odkaz:
http://arxiv.org/abs/2410.01676
Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to perform a wide range of tasks such as multimodal data querying, tool usag
Externí odkaz:
http://arxiv.org/abs/2409.01495
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 evaluated in settings with a prior knowledge of task requirements. Howev
Externí odkaz:
http://arxiv.org/abs/2406.13764
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, due to the complexity that the notion of time introduces to the lear
Externí odkaz:
http://arxiv.org/abs/2402.12309
In this paper, we propose an advancement to Tarskian model-theoretic semantics, leading to a unified quantitative theory of semantic information and communication. We start with description of inductive logic and probabilities, which serve as notable
Externí odkaz:
http://arxiv.org/abs/2401.17556
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various methods to mitigate these limitations. Temporal reasoning (TR), in parti
Externí odkaz:
http://arxiv.org/abs/2401.06853
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 temporal relationships such as order and distance. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2312.15816
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for NL-FOL translation using LoRA on a
Externí odkaz:
http://arxiv.org/abs/2305.15541