Papers by Shafin Rahman
Bidirectional Reasoning Supervision for Multilingual Financial Decision Making (2025.emnlp-industry)
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Muhammad Rafsan Kabir, Jawad Ibn Ahad, Robin Krambroeckers, Silvia Ahmed, M M Lutfe Elahi, Nabeel Mohammed, Shafin Rahman
| Challenge: | Large Language Models have been used for sentiment analysis, machine translation, and question answering, but their effectiveness in the multilingual financial domain remains unknown. |
| Approach: | They propose a fine-tuning approach that integrates positive and negative rationales alongside classification labels. |
| Outcome: | The proposed approach outperforms existing methods across English, Hindi, Bengali, and Telugu, and is suitable for industry applications. |
Thesis Proposal: Detecting Empathy Using Multimodal Language Model (2024.eacl-srw)
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| Challenge: | Existing studies on empathy detection in video and audio have relied on scripted or semi-scripted interactions that fail to capture the complexities and nuances of real-life interactions. |
| Approach: | They propose to develop a multimodal language model that detects empathy in audiovisual data by using neural architecture search and optimisation techniques. |
| Outcome: | The proposed model will be able to detect empathy in audiovisual data and use neural architecture search to deliver it. |
CAPSTONE: Composable Attribute‐Prompted Scene Translation for Zero‐Shot Vision–Language Reasoning (2025.emnlp-industry)
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| Challenge: | CAPSTONE transforms visual inputs into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Approach: | They propose a plug-and-play framework that transforms off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Outcome: | The proposed framework outperforms fully trained VLMs on the POPE dataset while the 4B model achieves competitive results. |
AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models (2025.emnlp-industry)
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Nafew Azim, Abrar Ur Alam, Hasan Bin Omar, Abdullah Mohammad Muntasir Adnan Jami, Jawad Ibn Ahad, Muhammad Rafsan Kabir, Md. Ismail Hossain, Fuad Rahman, Mohammad Ruhul Amin, Shafin Rahman, Nabeel Mohammed
| Challenge: | Large Language Models excel at complex reasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. |
| Approach: | They propose a framework that fully automates large language models' pipeline construction using reinforcement learning. |
| Outcome: | Experimental results show that autoDSPy outperforms DSPy benchmarks in accuracy gains and time. |
LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper Article (2024.findings-eacl)
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| Challenge: | Empathy is a key component of human-to-human interactions, and is often overlooked due to the inherent noise in crowdsourced annotations. |
| Approach: | They propose a large language model-guided empathy prediction system that rectifies annotation errors based on defined annotation selection threshold and makes annotations reliable for conventional empathy prediction models. |
| Outcome: | The proposed system rectifies annotation errors based on defined selection threshold and makes the annotations reliable for conventional empathy prediction models, e.g., BERT-based pre-trained language models. |