8 papers
ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions
Peixian Zhou, Yuxu Chen, Chaorui Zhang +3
Large language models perform increasingly well on standardized logical reasoning benchmarks, but whether this ability remains robust beyond English is unclear. We introduce ChLogi…
On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
Xueyan Niu, Bo Bai, Wei Han +1
Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have different objectives: SFT minimiz…
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL
Zerui Yang, Weichuan Wang, Yanwei Xu +4
Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs th…
DB3 Team's Solution For Meta KDD Cup' 25
Yikuan Xia, Jiazun Chen, Yirui Zhan +6
This paper presents the db3 team's winning solution for the Meta CRAG-MM Challenge 2025 at KDD Cup'25. Addressing the challenge's unique multi-modal, multi-turn question answering…
NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Weizhi Fei, Hao Shi, Jing Xu +7
Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing s…
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources
Yikuan Xia, Jiazun Chen, Yirui Zhan +6
Large language models (LLMs) excel in question-answering (QA) tasks, and retrieval-augmented generation (RAG) enhances their precision by incorporating external evidence from diver…