Evangelia Spiliopoulou

Muse Spark Safety & Preparedness Report

Cristina Menghini, Peter Ney, Hamza Kwisaba, Zifan et autres

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, …

us (code pays fourni par la source)

Muse Spark Safety & Preparedness Report

Cristina Menghini, Peter Ney, Hamza Kwisaba, Zifan et autres

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, …

Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning

Duygu Nur Yaldiz, Evangelia Spiliopoulou, Qi Zheng, Siddharth Varia et autres

Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this …

Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning

Duygu Nur Yaldiz, Evangelia Spiliopoulou, Qi Zheng, Siddharth Varia et autres

Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this …

Detecting Training Data of Large Language Models via Expectation Maximization

Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, Jie Ma et autres

Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, Jie Ma, William Yang Wang. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

us (code pays fourni par la source)

Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning

Duygu Nur Yaldiz, Evangelia Spiliopoulou, Zheng Qi, Siddharth Varia et autres

Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential.Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms.In this work, we …

us (code pays fourni par la source)

Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge

Evangelia Spiliopoulou, Riccardo Fogliato, Hanna Burnsky, Tamer H. M. Soliman et autres

Large language models (LLMs) can serve as judges that offer rapid and reliable assessments of other LLM outputs. However, models may systematically assign overly favorable ratings to their own outputs, a phenomenon known as self-bias, which can distort evaluations of true model …

Detecting Training Data of Large Language Models via Expectation Maximization

Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, Jie Ma et autres

Membership inference attacks (MIAs) aim to determine whether a specific example was used to train a given language model. While prior work has explored prompt-based attacks such as ReCALL, these methods rely heavily on the assumption that using known non-members as prompts …

General Purpose Verification for Chain of Thought Prompting

Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou, Qi Zheng et autres

Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information. In this paper, we explore ways to improve reasoning capabilities of LLMs through (1) exploration of different chains of thought and (2) …

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma et autres

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a …

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma et autres

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. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). …

EvEntS ReaLM: Event Reasoning of Entity States via Language Models

Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, Eduard H. Hovy

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail …