Papers by George Ma
Understanding Silent Data Corruption in LLM Training (2025.acl-long)
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| Challenge: | Large language models (LLMs) are a challenging task because of their large size and complexity. |
| Approach: | They propose to isolate and analyze the impact of SDCs on LLM training by using a cloud computing platform to access unhealthy nodes swept out of production by automated fleet management. |
| Outcome: | The proposed model training compares healthy production nodes with unhealthy nodes exhibiting SDCs at three levels: at each submodule computation, at a single optimizer step, and at . training period. |
SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion (2026.acl-long)
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George Ma, Anurag Koul, Qi Chen, Yawen Wu, Sachit Kuhar, Yu Yu, Aritra Sengupta, Varun Kumar, Murali Krishna Ramanathan
| Challenge: | Large Language Models (LLMs) excel at code-related tasks but struggle in real software repositories. |
| Approach: | They propose a large-scale agent that injects repository context at inference time to improve both latency and code-generation quality by proactively exploring repository files during indexing and constructing speculative context. |
| Outcome: | Experiments show that SpecAgent achieves 9–11% relative performance gains compared to baselines while significantly reducing inference latency. |
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events (2026.acl-long)
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| Challenge: | Existing MLTC benchmarks are saturated and may be affected by training data contamination. |
| Approach: | They propose a machine learning benchmark based on medical device adverse event reports . they establish baselines across 20 encoder- and decoder-only models . |
| Outcome: | The proposed benchmarks show that small fine-tuned models achieve the strongest head-to-tail accuracy while maintaining competitive UQ. |