555win cung cấp cho bạn một cách thuận tiện, an toàn và đáng tin cậy [xổ số vũng]
Mar 28, 2025 · We examine how classical symbolic techniques (e.g., theorem proving, model checking, knowledge representation) can be combined with neural networks and LLMs to achieve robust reasoning systems that leverage the strengths of both paradigms.
We propose a neuro-symbolic approach that uses in-context learning with LLMs to decompose complex questions into simpler ones and symbolic learning methods to learn rules for recomposing partial answers.
Jan 17, 2024 · Symbolic reasoning instead has LLMs interact with structured data indirectly – by generating code logic like SQL, SPARQL, Python that in turn interfaces with the tables or graphs. For example, a Python code snippet that loads a table dataset and calculates new metrics using Pandas operations.
In this work, we present TReMu (T emporal Re asoning for LLM-Agents in Mu lti-Session Di- alogues), a novel framework designed to enhance temporal reasoning in multi-session dialogues.
In this project, we developed a neuro-symbolic framework called Prototype-then-Refine (ProRef) that improves the logical reasoning ability of LLMs by enabling more robust generation of correct logic programs that are fed to symbolic solvers.
Nov 27, 2024 · However, LLMs often struggle with spatial reasoning which is one essential part of reasoning and inference and requires understanding complex relationships between objects in space. This paper proposes a novel neural-symbolic framework that enhances LLMs’ spatial reasoning abilities.
Feb 5, 2025 · The recently released LLM, DeepSeek-R1 [1], excels in complex tasks such as mathematics and coding, showcasing advanced reasoning capabilities. It effectively simulates human-like analytical thinking, enhancing multi-step reasoning in …
nsured to be causal and reliable due to the inherent de-fects of LLMs. Tracking such deficiencies, we present a neuro-symbolic integration method, in which a neural LLM is used to represent the knowledge of the problem while an LLM-free symbo.
This paper provides a comprehensive survey of neural-symbolic reasoning with a focus on the integration of logical automated reasoning engines and LLM-based neural reasoning.
By incorporating adaptive fewshot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs.
Bài viết được đề xuất: