Papers by Mohammad Mahdi Abdollah Pour
DiffuDetox: A Mixed Diffusion Model for Text Detoxification (2023.findings-acl)
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Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
| Challenge: | Existing text generation models that reduce toxicity of toxic text are inadequate for text detoxification tasks. |
| Approach: | They propose a conditional and unconditional diffusion model for text detoxification . conditional model takes toxic text as condition and reduces its toxicity . experimental results show the model achieves human-level fluency . |
| Outcome: | The proposed model reduces toxic text and produces diverse sentences . it can be used to train other models and ensure fluency . |
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing Knowledge Graph Question Answering (KGQA) methods focus on answering factual questions, leaving questions involving commonsense reasoning unaddressed. |
| Approach: | They propose a commonsense KGQA methodology that axiomatically surfaces commonsensical knowledge of Large Language Models and grounding every factual reasoning step on KG triples. |
| Outcome: | The proposed method outperforms existing methods and reduces instances of hallucination and reasoning errors. |
Gaussian Process Optimization for Adaptable Multi-Objective Text Generation using Linearly-Weighted Language Models (2024.findings-naacl)
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| Challenge: | Multi-objective text generation requires a method to optimize for dynamic requirements of the overall objective. |
| Approach: | They propose a linear combination of objective-specific language models to efficiently adapt the decoding process and optimize for the desired overall objective without retraining one or more language models. |
| Outcome: | The proposed method outperforms other weighting schemes and standard baselines in a few iterations of decoding. |
Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)
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| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
| Approach: | They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data. |
| Outcome: | The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model). |