7 citations · 19 across the 21 of their papers we have counts for
21 papers
SmellBench: Towards Fine-Grained Evaluation of Code Agents on Refactoring Tasks
Fake Lin, Binbin Hu, Xi Zhu +6
Code Agents have achieved remarkable advances in recent years, exhibiting strong capabilities across a wide range of software engineering tasks. However, their misuse often produce…
Token-level Collaborative Alignment for LLM-based Generative Recommendation
Fake Lin, Binbin Hu, Zhi Zheng +5
Large Language Models (LLMs) have demonstrated strong potential for generative recommendation by leveraging rich semantic knowledge. However, existing LLM-based recommender systems…
MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
Jiapeng Wang, Changxin Tian, Kunlong Chen +5
Optimizing data mixtures is essential for unlocking the full potential of large language models (LLMs), yet identifying the optimal composition remains computationally prohibitive…
Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Sirui Chen, Changxin Tian, Binbin Hu +4
Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and…
Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph
Jingwei Wang, Zai Zhang, Hao Qian +7
Teaching large language models (LLMs) to use tools is crucial for improving their problem-solving abilities and expanding their applications. However, effectively using tools is ch…
Relation-Aware Graph Foundation Model
Jianxiang Yu, Jiapeng Zhu, Hao Qian +4
In recent years, large language models (LLMs) have demonstrated remarkable capability to generalize across diverse natural language processing tasks, inspiring the development of g…
POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications
Chunjing Gan, Dan Yang, Binbin Hu +5
Large language models (LLMs) have become a disruptive force in the industry, introducing unprecedented capabilities in natural language processing, logical reasoning and so on. How…
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
Xinyu Tang, Xiaolei Wang, Zhihao Lv +5
Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capabil…
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
Lei Liang, Mengshu Sun, Zhengke Gui +16
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, inc…
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
Yakun Wang, Daixin Wang, Hongrui Liu +4
Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive p…
Personalized Binomial DAGs Learning with Network Structured Covariates
Boxin Zhao, Weishi Wang, Dingyuan Zhu +5
The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Junjie Wang, Mingyang Chen, Binbin Hu +10
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enha…
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Chunjing Gan, Dan Yang, Binbin Hu +9
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucinati…
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Chunjing Gan, Binbin Hu, Bo Huang +5
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are…
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
Yichi Zhang, Binbin Hu, Zhuo Chen +6
Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train ne…
Can Small Language Models be Good Reasoners for Sequential Recommendation?
Yuling Wang, Changxin Tian, Binbin Hu +6
Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are s…
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
Hao Qian, Hongting Zhou, Qian Zhao +7
The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from…
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
Chunjing Gan, Bo Huang, Binbin Hu +6
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery,…
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
Yakun Wang, Binbin Hu, Shuo Yang +6
The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a…
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
Chunjing Gan, Dan Yang, Binbin Hu +6
Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user pr…
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Lei Liu, Xiaoyan Yang, Yue Shen +4
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and re…