Papers by Alperen Yildiz
Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown promise in software vulnerability detection, especially on function-level benchmarks like Devign and BigVul. |
| Approach: | They propose a JIT vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits. |
| Outcome: | The proposed JIT vulnerability detection benchmark enables comprehensive evaluation of detection capabilities. |
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs (2025.emnlp-demos)
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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang, Alperen Öziş, Hayrettin Eren Yildiz, Mitash Ashish Shah, Zhiqi Huang, Anoop Kumar, Alfy Samuel, Daben Liu, Sai Praneeth Karimireddy, Salman Avestimehr
| Challenge: | Generative Large Language Models (LLMs) produce untruthful outputs, referred to as hallucinations, which are often referred as false positives. |
| Approach: | They propose an open-source Python library with over 30 truthfulness prediction methods. |
| Outcome: | The proposed methods span diverse trade-offs in computational cost, access level, grounding document requirements, and supervision type (self-supervised or supervised). |
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are outdated or incorrect over time due to unintended ripple effects that propagate even to the hidden space. |
| Approach: | They propose a lightweight representation-level technique to identify where ripple effects may occur by detecting entanglement between facts using forward activations from a single intermediate layer. |
| Outcome: | The proposed method achieves 62.2% improvement in Spearman correlation with ripple effects while being 2.74 faster and using 2.85 less peak GPU memory. |