3 accepted papers
Large language models (LLMs) augmented with retrieval have shown impressive performance in open-domain question answering, yet struggle significantly with temporal knowledge graph question answering (TKGQA). The core issue lies in structural misalignment: treating structured, temporally sensitive gr
Detoxification in large language models (LLMs) remains a significant research challenge. Existing decoding detoxification methods are all based on external constraints, which require additional resource overhead and lose generation fluency. This work innovatively proposes Detoxification with Self-Co
Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries using knowledge bases. Although large language models (LLMs) and LLM-based agents such as ReAct have shown potential for TKGQA, they often lack sufficient temporal constraints in the retrieval process. To tackle thi…