Papers by Ali Jannesari
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation (2026.acl-long)
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Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao
| Challenge: | Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA . |
| Approach: | They propose a dual-LLM Questioner–Solver pipeline that integrates external knowledge from compilers and runtime feedback to generate verified translations and multi-turn dialogues. |
| Outcome: | The proposed model outperforms proprietary models on key metrics like compilation success and accuracy. |
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation (2026.findings-eacl)
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| Challenge: | Representation Fine-Tuning (ReFT) adapts large pre-trained models by updating only a small subset of parameters. |
| Approach: | They propose a method that uses sparse intervention layers to steer hidden representations directly to capture rich semantic information. |
| Outcome: | The proposed approach outperforms PEFTs on commonsense reasoning, arithmetic reasoning, and GLUE benchmarks while maintaining a high parameter efficiency. |
AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs (2025.naacl-long)
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Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung D Phan, Le Chen, Mihai Capotă, Theodore L. Willke, Nesreen K. Ahmed, Ali Jannesari
| Challenge: | In-Context Learning (ICL) is a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. |
| Approach: | They propose a way to generate context using guidance from graph neural networks to generate efficient parallel codes. |
| Outcome: | The proposed method improves state-of-the-art LLMs by 19.9% and 6.48% on NAS and rodinia benchmarks. |
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models (2024.lrec-main)
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| Challenge: | Foundation Models (FMs) have demonstrated success in a wide range of applications, but their optimization often requires access to sensitive data. |
| Approach: | They propose a framework that combines FMs and Federated Learning to enable privacy-preserving and collaborative learning across multiple end-users. |
| Outcome: | The proposed framework combines benefits of FMs and Federated Learning (FL) it enables privacy-preserving and collaborative learning across multiple end-users. |