SemEval-2025 Task 9: The Food Hazard Detection Challenge

In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.

Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey

We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of Large Language Models (LLMs). While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable facilitation agents that not only moderate content, but also actively improve the quality of interactions. Our survey synthesizes ideas from Natural Language Processing (NLP) and Social Sciences to provide (a) a new taxonomy on discussion quality evaluation, (b) an overview of intervention and facilitation strategies, along with a new taxonomy on conversation facilitation datasets, (c) an LLM-oriented roadmap of good practices and future research directions, from technological and societal perspectives.

GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek

We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklishto-Greek transliteration. The toolkit is based on pre-trained Transformers, it is freely available, and can be easily installed in Python (pip install gr-nlp-toolkit). It is also accessible through a demonstration platform on HuggingFace, along with a publicly available API for non-commercial use. We discuss the functionality provided for each task, the underlying methods, experiments against comparable open-source toolkits, and future possible enhancements. The toolkit is available at: https://github.com/nlpaueb/gr-nlp-toolkit

Hate Speech According to the Law: An Analysis for Effective Detection

The issue of hate speech extends beyond the confines of the online realm. It is a problem with real-life repercussions, prompting most nations to formulate legal frameworks that classify hate speech as a punishable offence. These legal frameworks differ from one country to another, contributing to the big chaos that online platforms have to face when addressing reported instances of hate speech. With the definitions of hate speech falling short in introducing a robust framework, we turn our gaze onto hate speech laws. We consult the opinion of legal experts on a hate speech dataset and we experiment by employing various approaches such as pretrained models both on hate speech and legal data, as well as exploiting two large language models (Qwen2-7B-Instruct and Meta-Llama-3-70B). Due to the time-consuming nature of data acquisition for prosecutable hate speech, we use pseudo-labeling to improve our pretrained models. This study highlights the importance of amplifying research on prosecutable hate speech and provides insights into effective strategies for combating hate speech within the parameters of legal frameworks. Our findings show that legal knowledge in the form of annotations can be useful when classifying prosecutable hate speech, yet more focus should be paid on the differences between the laws.

Leveraging LLMs for Translating and Classifying Mental Health Data

Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients. Despite the benefits of LLMs for mental health support, there is limited research on their application in mental health systems for languages other than English. Our study addresses this gap by focusing on the detection of depression severity in Greek through user-generated posts which are automatically translated from English. Our results show that GPT3.5-turbo is not very successful in identifying the severity of depression in English, and it has a varying performance in Greek as well. Our study underscores the necessity for further research, especially in languages with less resources. Also, careful implementation is necessary to ensure that LLMs are used effectively in mental health platforms, and human supervision remains crucial to avoid misdiagnosis.

Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset

Large Language Models (LLMs) are increasingly integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages (Greek, Turkish, French, Portuguese, German, and Finnish). This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. By experimenting with GPT and Llama, we observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency underscores the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of the models. Through comprehensive error analysis, we emphasize the risks of relying exclusively on large language models (LLMs) in medical settings (e.g., their potential to contribute to misdiagnoses). Moreover, our proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation.

Evaluating the Reliability of Self-Explanations in Large Language Models

This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output. We evaluate two kinds of such self-explanations - extractive and counterfactual - using three state-of-the-art LLMs (2B to 8B parameters) on two different classification tasks (objective and subjective). Our findings reveal, that, while these self-explanations can correlate with human judgement, they do not fully and accurately follow the model's decision process, indicating a gap between perceived and actual model reasoning. We show that this gap can be bridged because prompting LLMs for counterfactual explanations can produce faithful, informative, and easy-to-verify results. These counterfactuals offer a promising alternative to traditional explainability methods (e.g. SHAP, LIME), provided that prompts are tailored to specific tasks and checked for validity.

Towards Systematic Monolingual NLP Surveys: GenA of Greek NLP

Natural Language Processing (NLP) research has traditionally been predominantly focused on English, driven by the availability of resources, the size of the research community, and market demands. Recently, there has been a noticeable shift towards multilingualism in NLP, recognizing the need for inclusivity and effectiveness across diverse languages and cultures. Monolingual surveys have the potential to complement the broader trend towards multilingualism in NLP by providing foundational insights and resources necessary for effectively addressing the linguistic diversity of global communication. However, monolingual NLP surveys are extremely rare in literature. This study fills the gap by introducing a method for creating systematic and comprehensive monolingual NLP surveys. Characterized by a structured search protocol, it can be used to select publications and organize them through a taxonomy of NLP tasks. We include a classification of Language Resources (LRs), according to their availability, and datasets, according to their annotation, to highlight publicly-available and machine-actionable LRs. By applying our method, we conducted a systematic literature review of Greek NLP from 2012 to 2022, providing a comprehensive overview of the current state and challenges of Greek NLP research. We discuss the progress of Greek NLP and outline encountered Greek LRs, classified by availability and usability. As we show, our proposed method helps avoid common pitfalls, such as data leakage and contamination, and to assess language support per NLP task. We consider this systematic literature review of Greek NLP an application of our method that showcases the benefits of a monolingual NLP survey. Similar applications could be regard the myriads of languages whose progress in NLP lags behind that of well-supported languages.

A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning

Diagnostic Captioning (DC) automatically generates a diagnostic text from one or more medical images (e.g., X-rays, MRIs) of a patient. Treated as a draft, the generated text may assist clinicians, by providing an initial estimation of the patient's condition, speeding up and helping safeguard the diagnostic process. The accuracy of a diagnostic text, however, strongly depends on how well the key medical conditions depicted in the images are expressed. We propose a new data-driven guided decoding method that incorporates medical information, in the form of existing tags capturing key conditions of the image(s), into the beam search of the diagnostic text generation process. We evaluate the proposed method on two medical datasets using four DC systems that range from generic image-to-text systems with CNN encoders and RNN decoders to pre-trained Large Language Models. The latter can also be used in few- and zero-shot learning scenarios. In most cases, the proposed mechanism improves performance with respect to all evaluation measures. We provide an open-source implementation of the proposed method at https://github.com/nlpaueb/dmmcs.

CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification

Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be applied to automatically detect such risks. We publish a dataset of 7,546 short texts describing public food recall announcements. Each text is manually labeled, on two granularity levels (coarse and fine), for food products and hazards that the recall corresponds to. We describe the dataset and benchmark naive, traditional, and Transformer models. Based on our analysis, Logistic Regression based on a tf-idf representation outperforms RoBERTa and XLM-R on classes with low support. Finally, we discuss different prompting strategies and present an LLM-in-the-loop framework, based on Conformal Prediction, which boosts the performance of the base classifier while reducing energy consumption compared to normal prompting.