107 citations · 122 across the 18 of their papers we have counts for
18 papers
Scaling Agentic Verifier for Competitive Coding
Zeyao Ma, Jing Zhang, Xiaokang Zhang +9
Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-base…
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
Sijia Luo, Xiaokang Zhang, Yuxuan Hu +6
Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing K…
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Haoyang Li, Shang Wu, Xiaokang Zhang +9
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements…
Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL
Hanbing Liu, Haoyang Li, Xiaokang Zhang +5
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it…
Dynamic Scaling of Unit Tests for Code Reward Modeling
Zeyao Ma, Xiaokang Zhang, Jing Zhang +3
Current large language models (LLMs) often struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. Prior research tackles this…
CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis
Bohan Zhang, Xiaokang Zhang, Jing Zhang +3
Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these method…
SAM Decoding: Speculative Decoding via Suffix Automaton
Yuxuan Hu, Ke Wang, Xiaokang Zhang +4
Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have y…
P Law: Scaling Law for Post-Training After Model Pruning
Xiaodong Chen, Yuxuan Hu, Xiaokang Zhang +4
Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs). To recover model performance after pruning, post-training is c…
SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
Zeyao Ma, Bohan Zhang, Jing Zhang +6
We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LL…
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking
Xiaokang Zhang, Zijun Yao, Jing Zhang +4
Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations. Current factuality probes, trained using humananno…
Reverse That Number! Decoding Order Matters in Arithmetic Learning
Daniel Zhang-Li, Nianyi Lin, Jifan Yu +6
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite ac…
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
Xiaokang Zhang, Sijia Luo, Bohan Zhang +12
We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embe…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
Jing Zhang, Xiaokang Zhang, Daniel Zhang-Li +10
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet kno…
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang, Xiaokang Zhang, Jifan Yu +4
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large on…
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Yu Feng, Jing Zhang, Xiaokang Zhang +3
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constra…
Graph Contrastive Learning for Anomaly Detection
Bo Chen, Jing Zhang, Xiaokang Zhang +6
Graph-based anomaly detection has been widely used for detecting malicious activities in real-world applications. Existing attempts to address this problem have thus far focused on…
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
Bo Chen, Jing Zhang, Xiaokang Zhang +6
Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, a…