Papers by Hamed Firooz
A Survey on Multimodal Disinformation Detection (2022.coling-1)
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Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
| Challenge: | Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. |
| Approach: | They propose to tackle online multimodal offensive content using different modalities and combinations thereof. |
| Outcome: | The proposed approach combines factuality and harmfulness in a framework that can be used for multiple modalities and combinations of modality. |
MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)
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Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu
| Challenge: | Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout. |
| Approach: | They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction. |
| Outcome: | The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks. |
Detecting Propaganda Techniques in Memes (2021.acl-long)
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Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino
| Challenge: | Propaganda can be defined as a form of communication that aims to influence opinions or the actions of people towards a specific goal. |
| Approach: | They propose to detect the type of propaganda techniques used in memes by annotating them with 22 techniques. |
| Outcome: | The proposed model identifies 22 propaganda techniques in memes, which can appear in text, image or both . |
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning (2023.findings-acl)
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| Challenge: | Large language models have impressive fewshot performance on many NLP tasks and domains. |
| Approach: | They propose a meta-training approach that uses demonstration retrieval to train parameter-efficient models that generalize well on a larger variety of tasks. |
| Outcome: | The proposed approach outperforms many parameter-efficient methods on QA, NLI, and text classification tasks. |
RESPROMPT: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models (2024.naacl-long)
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Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz
| Challenge: | Chain-of-thought (CoT) has impressively unlocked the reasoning potential of large language models (LLMs), but it falls short when tackling problems that require multiple reasoning steps. |
| Approach: | They propose a new prompting strategy that advances multi-step reasoning in LLMs by integrating necessary connections into prompts. |
| Outcome: | The proposed strategy improves multi-step reasoning accuracy and improves reasoning accuracy across math, sequential, and commonsense domains. |
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding (2021.findings-emnlp)
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| Challenge: | Current knowledge distillation models are limited and lack performance on multimodal datasets. |
| Approach: | They propose a multimodal knowledge distillation framework to transfer knowledge from a teacher on multimodal tasks by learning the teacher's behavior within each modality. |
| Outcome: | The proposed framework achieves better performance than KD on four multimodal datasets. |
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation (2023.emnlp-main)
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Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang, Shaoliang Nie
| Challenge: | Personalized text generation (PTG) is a key component of our digital lives but can inadvertently associate different levels of linguistic quality with users’ protected attributes. |
| Approach: | They propose a framework to achieve measure-specific counterfactual fairness in explanation generation by focusing on one of the most studied settings: generating natural language explanations for recommendations. |
| Outcome: | The proposed framework achieves measure-specific counterfactual fairness in explanation generation. |
CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations (2025.acl-long)
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| Challenge: | In-context learning and CoT are still poorly understood, but the precise mechanisms and architectural factors driving ICL and Co T are still unclear. |
| Approach: | They propose a framework and methodology to generate synthetic tokenized datasets and study chain-of-thought (CoT) in-context learning in language models. |
| Outcome: | The proposed framework and methodology allows fine grained control over the complexity of in-context examples by decoupling causal structure from underlying token processing functions. |
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
| Approach: | They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time. |
| Outcome: | The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets. |
ER-Test: Evaluating Explanation Regularization Methods for Language Models (2022.findings-emnlp)
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| Challenge: | Explanation regularization (ER) aims to improve NLM generalization by pushing the NLM’s machine rationales to align with human rationale. |
| Approach: | They propose a framework for evaluating ER models’ OOD generalization along three dimensions: unseen datasets, contrast set tests, and functional tests. |
| Outcome: | The proposed framework evaluates ER models’ OOD generalization across unseen datasets, contrast set tests, and functional tests. |
To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples (2026.acl-long)
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| Challenge: | Chain-of-thought (CoT) prompting and in-context learning (ICL) have unlocked significant reasoning capabilities in large language models (LLMs). |
| Approach: | They propose a meta-training technique to learn reasoning tasks in-context using CoT examples. |
| Outcome: | The proposed methods improve performance on novel reasoning tasks even when there are no CoT examples available in-context. |