Papers by Amin Abolghasemi
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. |
Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models (2025.findings-acl)
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| Challenge: | Prior work has focused on improving and evaluating the attribution quality of large language models (LLMs) but this may come at the expense of inducing biases in the attributed answers. |
| Approach: | They propose to evaluate attribution sensitivity and bias with respect to authorship information in large language models (LLMs) in retrieval-augmented generation pipelines. |
| Outcome: | The proposed framework can significantly improve the attribution quality of large language models (LLMs) in retrieval-augmented generation pipelines by adding authorship information to source documents. |
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. |