Papers by Mehdi Jafari
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is a model compression algorithm that helps transfer knowledge in a large neural network into a smaller one. |
| Approach: | They propose a framework to assess adversarial robustness of multiple KD algorithms. |
| Outcome: | The proposed algorithm achieves state-of-the-art on the GLUE benchmark and out-of domain generalization and adversarial robustness compared to competitive methods. |
Annealing Knowledge Distillation (2021.eacl-main)
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| Challenge: | Knowledge distillation (KD) is a powerful model compression technique for deep neural networks. |
| Approach: | They propose a method to feed the rich information provided by teacher’s soft-targets incrementally and more efficiently by annealing the teacher output incrementally. |
| Outcome: | The proposed method can be used on image classification and NLP language inference tasks with BERT-based models on the GLUE benchmark. |
SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) demonstrate strong capabilities in translating natural language into code, but applying them to this domain remains challenging. |
| Approach: | They propose a dual-anchored evolutionary framework that combines a static blueprint and a bi-level optimization to decouple structural refinement from parameter calibration. |
| Outcome: | The proposed framework identifies two failure modes in long-horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. |
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization (2022.findings-emnlp)
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| Challenge: | Existing methods for knowledge distillation (KD) do not mitigate the noise in the teacher’s output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. |
| Approach: | They propose a method that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. |
| Outcome: | The proposed method achieves state-of-the-art performance on NLU and computer vision tasks. |
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher (2022.coling-1)
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| Challenge: | Knowledge distillation (KD) is a powerful tool for deep learning applications. |
| Approach: | They propose a method which defines a smoother training path for the student by following the training footprints of the teacher rather than solely relying on distilling from a single mature fully-trained teacher. |
| Outcome: | The proposed technique is quite effective in mitigating the capacity-gap problem and the checkpoint search problem. |
Beyond Words: Integrating Theory of Mind into Conversational Agents for Human-Like Belief, Desire, and Intention Alignment (2025.findings-acl)
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| Challenge: | Empirical evaluations of LLaMA-3 models demonstrate that ToM-informed alignment improves response quality, achieving win rates of 63% and 67%, respectively. |
| Approach: | They investigate whether open-source LLaMA models can represent and retain ToM-related constructs and whether they can be used to generate more aligned responses. |
| Outcome: | The proposed models can represent and retain ToM-related constructs and improve response quality. |
Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification (2024.findings-naacl)
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Marzieh Tahaei, Aref Jafari, Ahmad Rashid, David Alfonso-Hermelo, Khalil Bibi, Yimeng Wu, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
| Challenge: | Existing models with explicit citations lack the ability to verify information generated by these models. |
| Approach: | They construct a citation training dataset and fine-tune two models to address the challenge of explicit citations efficiently. |
| Outcome: | The proposed models surpass ChatGPT and exhibit exceptional out-of-domain generalization in both human and automatic evaluation. |
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models (2025.emnlp-main)
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Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh, Parsa Farinneya, Hossein Rajabzadeh, Aref Jafari, Boxing Chen, Marzieh S. Tahaei
| Challenge: | Existing methods for dynamic inference are limited by hardware inefficiencies or performance degradation. |
| Approach: | They propose a framework for depth-based dynamic inference that freezes the pre-trained model and inserts additional transformer layers at selected exit points. |
| Outcome: | The proposed framework outperforms state-of-the-art methods such as Flextron and Layerskip on multiple models at various scales, as well as other leading compression techniques across a variety of benchmarks. |
Do we need Label Regularization to Fine-tune Pre-trained Language Models? (2023.eacl-main)
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Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh, Tianda Li, Alan Do-Omri, Peng Lu, Pascal Poupart, Ali Ghodsi
| Challenge: | Knowledge Distillation (KD) is a label regularization technique that can be replaced with lighter teacher-free variants such as the label-smoothing technique. |
| Approach: | They propose to use knowledge distillation to train student models by deploying the teacher network during training. |
| Outcome: | The proposed method can be replaced with lighter teacher-free variants on PLMs with more than 600 distinct trials and ran each configuration five times. |