Papers by Aliakbar Nafar
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models (2025.naacl-long)
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| Challenge: | Existing studies on in-context learning mechanisms are not consistent . current research identifies two main approaches to explain the ICL mechanism . |
| Approach: | They propose a framework for evaluating in-context learning mechanisms by focusing on regression tasks. |
| Outcome: | The proposed framework can solve regression problems and then measure the extent to which the LLM retrieves its internal knowledge versus learning from in-context examples. |
Teaching Probabilistic Logical Reasoning to Transformers (2024.findings-eacl)
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| Challenge: | Existing approaches to reasoning using transformers are limiting, resulting in inconsistent results in arithmetic and QA benchmarks. |
| Approach: | They propose a novel approach that utilizes probabilistic logical rules as constraints in the fine-tuning phase without relying on them in the inference stage. |
| Outcome: | The proposed approach improves the transformer-based language model’s intrinsic reasoning and makes their probabilistic logical reasoning process more explicit and explainable. |
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning (2021.emnlp-demo)
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| Challenge: | Current deep learning architectures are data-hungry with issues mainly in generalizability and explainability. |
| Approach: | They propose a library for the integration of domain knowledge in deep learning architectures . structure of data is expressed symbolically via graph declarations and constraints can be added to deep models . |
| Outcome: | The proposed framework simplifies programming for integration of domain knowledge in deep learning architectures while separating the knowledge representation from learning algorithms. |