5 accepted papers
Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processing. Despite their success, a critical gap remains in enabling LLMs to perform knowledge graph completion (KGC). Empirica…
Optimizing data utilization remains a central challenge in applying Reinforcement Learning (RL) to Large Language Models (LLMs), directly impacting sample efficiency, training stability, and final model performance.Current approaches often rely on massive static datasets, leading to computational in
Despite the remarkable progress made by large language models in mathematical reasoning, interactive theorem proving in formal logic still remains a prominent challenge. Previous methods resort to neural models for proofstep generation and search. However, they suffer from exploring possible proofst…
Optimization problems seek to find the best solution to an objective under a set of constraints, and have been widely investigated in real-world applications. Modeling and solving optimization problems in a specific domain typically require a combination of domain knowledge, mathematical skills, and…
With rapid evolution of e-commerce, it is essential but challenging to quickly provide a recommending service for users. The recommender system can be divided into two stages: retrieval and ranking. However, most recent academic research has focused on the second stage for datasets with limited size…