Papers by Arian Askari
HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval (2025.acl-long)
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| Challenge: | a novel multimodal dense retrieval model for the travel domain addresses limitations of traditional search engines. |
| Approach: | They propose a multimodal dense retrieval model that enables natural language property search . they propose combining a small language model and a large language model for embedding hotel data . |
| Outcome: | The proposed model outperforms state-of-the-art models on four diverse test sets . it is generalizable across LLM architectures and scalability for processing large image galleries . |
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems (2024.findings-acl)
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| Challenge: | a number of studies have evaluated user satisfaction estimation in TOD systems . current benchmarks for user satisfaction estimates are highly skewed towards dialogues for which the user is satisfied. |
| Approach: | They leverage large language models to generate satisfaction-aware counterfactual dialogues to augment original dialogues of a test collection. |
| Outcome: | The proposed models show higher robustness to increase in dissatisfaction labels than fine-tuned models. |
Expand, Highlight, Generate: RL-driven Document Generation for Passage Reranking (2023.emnlp-main)
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| Challenge: | Existing studies use large language models to generate training data for ranking models. |
| Approach: | They propose a pipeline that generates synthetic documents from queries using large language models . they propose RL-based reinforcement learning to optimize the pipeline . |
| Outcome: | The proposed pipeline outperforms existing state-of-the-art methods in generating synthetic documents more effectively. |
SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs (2025.findings-naacl)
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Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne
| Challenge: | Existing methods for intent prediction rely on human feedback and are tailored to structured intents. |
| Approach: | They propose a method that generates dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |
| Outcome: | The proposed methods generate dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits. |
| Approach: | They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges. |
| Outcome: | The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment. |