1 citations · 1 across the 7 of their papers we have counts for
7 papers
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
Jiasheng Zheng, Xin Zheng, Boxi Cao +8
Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and eval…
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Zhengzhao Ma, Xueru Wen, Boxi Cao +6
Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models…
Empowering Small Language Models with Factual Hallucination-Aware Reasoning for Financial Classification
Han Yuan, Yilin Wu, Li Zhang +1
Small language models (SLMs) are increasingly used for financial classification due to their fast inference and local deployability. However, compared with large language models, S…
Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang +2
Structured output from large language models (LLMs) has enhanced efficiency in processing generated information and is increasingly adopted in industrial applications. Prior studie…
Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis
Bo Hu, Han Yuan, Vlad Pandelea +3
The rapid advancement of large language models (LLMs) has sparked widespread adoption across diverse applications, making robust evaluation frameworks crucial for assessing their p…
Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis
Han Yuan, Li Zhang, Zheng Ma
Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous re…
Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
Jiasheng Zheng, Boxi Cao, Zhengzhao Ma +5
In recent years, researchers have proposed numerous benchmarks to evaluate the impressive coding capabilities of large language models (LLMs). However, current benchmarks primarily…