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Aref Jafari

5 accepted papers

2025

Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models

EMNLP 2025

Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility to adjust model behavior based on varying resource budgets, existing methods are frequently limited by hardware ineffici

2024

Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification

NAACL 2024findings

In recent years, there has been a growing interest in utilizing external knowledge to reduce hallucinations in large language models (LLMs) and provide them with updated information. Despite this improvement, a major challenge lies in the lack of explicit citations, which hampers the ability to veri…

2022

Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization

EMNLP 2022finding

Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model’s (a student) generalization by transferring the knowledge from a larger model (a teacher). Although KD methods achieve state-of-the-art performance in numerous settings, the…

Cited by 5SourcePDFScholar
2022

Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher

COLING 2022main

With the ever growing scale of neural models, knowledge distillation (KD) attracts more attention as a prominent tool for neural model compression. However, there are counter intuitive observations in the literature showing some challenging limitations of KD. A case in point is that the best perform…

Cited by 14SourcePDFScholar
2021

How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding

EMNLP 2021finding

Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one. Even though KD has shown promise on a wide range of Natural Language Processing (NLP) applications, little is understood about how one KD algorithm compares to…

Cited by 9SourcePDFScholar