Papers by Dimitrios Dimitriadis
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias (2023.findings-emnlp)
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| Challenge: | In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. |
| Approach: | They propose a method for correcting presentation bias using generated counterfactual labels by augmentation of the labels by the user. |
| Outcome: | The proposed method improves performance in an oracle setting compared to uncorrected models and existing bias-correction methods. |
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs (2025.findings-naacl)
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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates, Chenyang Tao, Anil Ramakrishna, Dimitrios Dimitriadis, Jieyu Zhao, Salman Avestimehr
| Challenge: | Existing methods for probability-based UE are limited by their inability to handle biased probabilities and complex semantic dependencies between tokens. |
| Approach: | They propose a learning-based scoring function that captures complex dependencies between tokens and probabilities and produces more reliable responses. |
| Outcome: | The proposed function outperforms existing scoring functions in question-answering and arithmetical reasoning tasks with different datasets. |
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (2022.naacl-main)
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Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
| Challenge: | Currently, global models are not able to produce personalized responses for individual users, based on their data. |
| Approach: | They propose a scheme for training a single shared model for all users by prepending a fixed, user-specific non-trainable string to each user’s input text. |
| Outcome: | The proposed method outperforms the state-of-the-art model on a suite of sentiment analysis datasets by up to 13 points. |
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs (2024.acl-long)
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Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
| Challenge: | Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. however, their tendency to produce inaccurate or misleading outputs poses a potential risk. |
| Approach: | They propose a new scoring function that considers the semantic contribution of each token in the generated sequence in the context of the question. |
| Outcome: | The proposed scoring function improves UE performance on a medical QA dataset. |