5 papers
Scaling LLM Knowledge Boundaries via Distribution-Optimized Synthesis
Songze Li, Yarong Lan, Zhongpu Bo +16
Knowledge injection via synthetic data is crucial for enhancing Large Language Models (LLMs). However, current synthesis methods simply stop at preset token counts or fixed data ra…
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
Jun Xu, Xinkai Du, Yu Ao +17
Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retriev…
KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
Dalong Zhang, Jun Xu, Jun Zhou +16
In this paper, we introduce KAG-Thinker, which upgrade KAG to a multi-turn interactive thinking and deep reasoning framework powered by a dedicated parameter-light large language m…
K-ON: Stacking Knowledge On the Head Layer of Large Language Model
Lingbing Guo, Yichi Zhang, Zhongpu Bo +5
Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next tok…
OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
Zhiqiang Liu, Chengtao Gan, Junjie Wang +5
Existing domain-specific Large Language Models (LLMs) are typically developed by fine-tuning general-purposed LLMs with large-scale domain-specific corpora. However, training on la…