Papers by Ioannis Patras
Confidence Should Be Calibrated More Than One Turn Deep (2026.acl-long)
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| Challenge: | Existing work on confidence estimation and calibration focuses on single-turn settings . existing work on multi-turn calibration ignores the risks and potential of multi-turned conversations . |
| Approach: | They propose a multi-turn calibration task that reframes calibration from a static property into a dynamic challenge central to reliable multi- turn conversations. |
| Outcome: | The proposed model minimizes ECE@T and leverages ConfChat to improve confidence . the proposed model preserves and even enhances model performance in multi-turn interactions. |
GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for assessing the reliability of Large Language Models (LLMs) by confidence elicitation require expensive computational overhead or suffer from poor calibration, making them unreliable for real-world deployment. |
| Approach: | They propose a Generative Approach to Confidence Elicitation that enables reliable confidence elicitation for Large Language Models. |
| Outcome: | The proposed method achieves the best discriminative capacity and calibration on open-ended tasks without resorting to additional sampling or an auxiliary model. |
FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models (2025.findings-emnlp)
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| Challenge: | FairCoT enhances fairness in text-to-image generative models by integrating iterative reasoning . experimental evaluations demonstrate FairCot significantly enhances diversity without sacrificing image quality or semantic fidelity. |
| Approach: | FairCoT is a framework that enhances fairness in text-to-image generative models . it employs iterative CoT refinement to mitigate biases and dynamically adjusts textual prompts . |
| Outcome: | FairCoT combines iterative CoT refinement with iterating reasoning processes . it addresses limitations of zero-shot CoT in sensitive scenarios, authors say . |
Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization (2025.coling-main)
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| Challenge: | Existing methods for erasure of memorized text fail to unlearn large numbers of memorizable samples without jeopardizing model utility. |
| Approach: | They propose a method that allows LLMs to memorize and recite some training sequences verbatim . they propose an entropy-based loss method that is shown to be more stable . |
| Outcome: | The proposed method improves model utility and accuracy while preserving model ability in language generation and understanding. |
A Simple Baseline for Knowledge-Based Visual Question Answering (2023.emnlp-main)
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| Challenge: | Recent studies emphasize the importance of incorporating both explicit and implicit knowledge to answer questions requiring external knowledge. |
| Approach: | They propose a pipeline that incorporates both explicit and implicit knowledge . their method is training-free and does not require access to external databases or APIs . |
| Outcome: | The proposed method achieves state-of-the-art accuracy on OK-VQA and A-OK-VQ datasets. |