Papers by Aitzaz Ahmad
ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) requires high-quality labeled datasets. |
| Approach: | They propose a method that uses pre-trained language models to generate entity-controlled text to augment small labeled datasets for downstream NER tasks. |
| Outcome: | The proposed method produces full text samples with desired entities appearing in a controllable way while retaining sentence coherence closest to the real world data. |
Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
| Approach: | They propose a framework that leverages contrastive prompts to distinguish between high- and low-performing cases. |
| Outcome: | The proposed framework can generalize across open and proprietary models and NLU benchmarks. |
Efficient Continual Pre-training for Building Domain Specific Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are typically trained entirely on domain corpus to excel at handling domain-specific tasks. |
| Approach: | They propose a continual pre-training strategy to build domain-specific LLMs over existing open-domain LLM. |
| Outcome: | The proposed model outperforms existing LLMs with 10% of corpus size and cost without any degradation on open-domain tasks. |