Papers by Maziar Sanjabi
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP (2024.emnlp-main)
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| Challenge: | Image-text contrastive models like CLIP struggle on compositional visio-linguistic tasks where their performance is no better than random chance. |
| Approach: | They propose a distillation method to enhance CLIP's compositional visio-linguistic reasoning by using a model-derived distillation objective borrowed from large text-to-image generative models like Stable-Diffusion. |
| Outcome: | The proposed method improves CLIP models' visio-linguistic performance on the Winoground benchmark by 7% while on the ARO dataset, it boosts performance by 3%. |
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. |