Papers by Vladimir V. Ivanov
Efficient Hallucination Detection in Automatic Code Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models produce source code that appears correct and well-formed, but includes hallucinated elements that cause downstream test failures. |
| Approach: | They develop a transformer-based detector that uses LLM internal representations to identify hallucinations. |
| Outcome: | The proposed detector outperforms existing methods and unsupervised methods in the code generation domain. |
Evaluating Tokenizer Adaptation Methods for Large Language Models on Low-Resource Programming Languages (2025.acl-srw)
Copied to clipboard
| Challenge: | Large language models (LLMs) trained on high-resource programming languages perform sub-optimally for low-resourced programming languages (LRPLs). |
| Approach: | They evaluate the impact of tokenizer adaptation methods on improving code generation for LRPLs. |
| Outcome: | The proposed methods outperform the original models and fine-tuned models in LRPLs, but performance declines in non-target languages like Python after tokenizer adaptation. |