Papers by Joanna C. S. Santos
CodeGuard: Improving LLM Guardrails in CS Education (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are increasingly embedded in Computer Science classrooms to automate code generation, feedback, and assessment. |
| Approach: | They propose a guardrail framework for educational AI systems that can handle unsafe and irrelevant prompts. |
| Outcome: | The proposed framework reduces potentially harmful or policy-violating code completions by 30-65% without degrading performance on legitimate educational tasks. |
MojoBench: Language Modeling and Benchmarks for Mojo (2025.findings-naacl)
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| Challenge: | Mojo is a programming language that has been praised for its speed and performance over Python. |
| Approach: | They propose a framework for Mojo code generation that evaluates code Large Language Models (LLMs) they propose 'mojo-Coder' which is the first LLM pretrained and fine-tuned for MoJO code generation . |
| Outcome: | MojoBench is the first framework for mojo code generation . it achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet . |