Papers by Evangelia Spiliopoulou
Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly deployed in decision-making tasks where accuracy and reliable confidence estimates are essential. |
| Approach: | They propose a calibration-aware reinforcement learning formulation that directly adjusts decision-token probabilities. |
| Outcome: | The proposed model preserves RLVR’s accuracy level while mitigating overconfidence, reducing ECE scores up to 9 points. |
EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes. |
| Approach: | They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes. |
| Outcome: | The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available. |
Detecting Training Data of Large Language Models via Expectation Maximization (2026.eacl-long)
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| Challenge: | Membership inference attacks aim to determine whether a specific example was used to train a given language model. |
| Approach: | They propose a membership inference approach that iteratively refines prefix effectiveness and membership scores using an expectation-maximization strategy without requiring labeled non-member examples. |
| Outcome: | The proposed approach outperforms baselines under systematically varied distributional overlap and difficulty. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
Event-Related Bias Removal for Real-time Disaster Events (2020.findings-emnlp)
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| Challenge: | Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. |
| Approach: | They propose to train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification. |
| Outcome: | The proposed model removes event-specific biases and improves on tweet importance classification. |
Definition Frames: Using Definitions for Hybrid Concept Representations (2020.coling-main)
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| Challenge: | a new hybrid representation is proposed that encodes semantic information extracted from definitions. |
| Approach: | They propose a matrix distributed representation extracted from definitions where each dimension is semantically interpretable. |
| Outcome: | The proposed representations have competitive performance with other distributional semantic approaches on word similarity tasks. |