Papers by Amirhossein Abaskohi
StarFlow: Generating Structured Workflow Outputs From Sketch Images (2026.eacl-long)
Copied to clipboard
Patrice Bechard, Chao Wang, Amirhossein Abaskohi, Juan A. Rodriguez, Christopher Pal, David Vazquez, Spandana Gella, Sai Rajeswar, Perouz Taslakian
| Challenge: | Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. |
| Approach: | They propose a framework for generating structured workflow outputs from sketches using vision-language models to automate the process. |
| Outcome: | The proposed framework outperforms large vision-language models in the task of generating structured workflow outputs from sketches and diagrams. |
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
Copied to clipboard
Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
| Challenge: | a new study examines the efficacy of large language models (LLMs) for Persian . ChatGPT and LLMs have shown remarkable performance in English, but their efficiency for low-resource languages remains an open question. |
| Approach: | They present a benchmarking study of large language models (LLMs) for Persian . they focus on GPT-3.5-turbo, but also GPT-4 and OpenChat-3.5 . |
| Outcome: | The proposed model performs better in Persian than other low-resource languages . the study is the first comprehensive benchmarking of large language models . |
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications . despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets. |
| Approach: | They propose a framework for creating a high-quality dataset for multimodal multihop question answering . they use a 5-stage pipeline to acquire relevant multimodal documents from Wikipedia . |
| Outcome: | The proposed framework outperforms existing methods on multimodal multihop question answering datasets. |
ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement (2025.emnlp-main)
Copied to clipboard
Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini
| Challenge: | Chart question answering (CQA) is a key research challenge for large vision-language models . recent efforts focus on leveraging LVLMs directly on chart images . |
| Approach: | They propose a gaze-guided attention refinement that aligns image-text attention with human fixations to improve chart reasoning quality and interpretability. |
| Outcome: | The proposed approach improves answer accuracy and attention alignment yielding gains of up to 2.56 percentage points across multiple models. |
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have been limited when fine-tuned on small datasets. |
| Approach: | They propose to add contrastive learning to prompt-based fine-tuning to improve model performance. |
| Outcome: | The proposed approach outperforms other methods on multiple text classification benchmarks. |
CEMTM: Contextual Embedding-based Multimodal Topic Modeling (2025.emnlp-main)
Copied to clipboard
| Challenge: | CEMTM is a context-enhanced multimodal topic model that can infer coherent topic structures from documents . traditional multimodal topics failed to capture deeper cross-modal interactions . large vision language models (LLMs) and LVLMs have shown remarkable capacity to encode rich semantic knowledge from vast corpora. |
| Approach: | They propose a context-enhanced multimodal topic model that uses tokens to weight contributions to topic inference. |
| Outcome: | The proposed model outperforms unimodal and multimodal benchmarks on six multimodal domains and captures semantics in scientific articles. |