Papers by Haris Riaz
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation (2025.findings-acl)
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| Challenge: | Recent smaller language models rely on synthetic data generated using larger Language models. |
| Approach: | They propose a method for generating synthetic data that enhances diversity through meta-prompting . they use 25 million tokens of synthetic data generated by a language model orchestrated by multiple “expert” LLM agents to collaboratively generate data. |
| Outcome: | The proposed method outperforms the base LLM in Finance and Biomedicine with 25 million tokens of synthetic data. |
ELLEN: Extremely Lightly Supervised Learning for Efficient Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) tasks require an amount of annotations that are unrealistic for many real-world applications. |
| Approach: | They propose a semi-supervised named entity recognition method that blends language models with linguistic rules. |
| Outcome: | The proposed method outperforms most existing semi-supervised methods under the same supervision settings commonly used in the literature. |
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification (2024.findings-naacl)
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| Challenge: | a novel neuro-symbolic architecture for relation classification combines rule-based methods with deep learning techniques. |
| Approach: | They propose a neuro-symbolic architecture for relation classification that combines rule-based methods with deep learning techniques. |
| Outcome: | The proposed approach outperforms state-of-the-art models in three out of four settings . human interventions boost the performance on the relation org:parents by as much as 26% relative improvement . |