The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.

Many nations and organizations have begun collecting and storing clinical trial records for storage and analytical purposes so that medical and clinical practitioners can refer to them on a centralized database over the internet and stay updated with the current clinical information. The amount of clinical trial records have gone through the roof, making it difficult for many medical and clinical practitioners to stay updated with the latest information. To help and support medical and clinical practitioners, there is a need to build intelligent systems that can update them with the latest information in a byte-sized condensed format and, at the same time, leverage their understanding capabilities to help them make decisions. This paper describes our contribution to SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). Our results show that there is still a need to build domain-specific models as smaller transformer-based models can be finetuned on that data and outperform foundational large language models like GPT-3.5. We also demonstrate how the performance of GPT-3.5 can be increased using few-shot prompting by leveraging the semantic similarity of the text samples and the few-shot train snippets. We will also release our code and our models on open source hosting platforms, GitHub and HuggingFace.

In this era of digital communication and social media, texting and chatting among individuals occur mainly through code-mixed or Romanized versions of the native language prevalent in the region. The presence of Romanized and code-mixed language develops the need to build NLP systems in these domains to leverage the digital content for various use cases. This paper describes our contribution to the subtask MCEC of the shared task WASSA 2023:Shared Task on Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages. We explore how one can build sentence transformers models for low-resource languages using unsupervised data by leveraging contrastive learning techniques described in the SIMCSE paper and using the sentence transformer developed to build classification models using the SetFit approach. Additionally, we’ll publish our code and models on GitHub and HuggingFace, two open-source hosting services.

Social Media platforms have grown their reach worldwide. As an effect of this growth, many vernacular social media platforms have also emerged, focusing more on the diverse languages in the specific regions. Tamil has also emerged as a popular language for use on social media platforms due to the increasing penetration of vernacular media like Sharechat and Moj, which focus more on local Indian languages than English and encourage their users to converse in Indic languages. Abusive language remains a significant challenge in the social media framework and more so when we consider languages like Tamil, which are low-resource languages and have poor performance on multilingual models and lack language-specific models. Based on this shared task, “Abusive Comment detection in Tamil@DravidianLangTech-ACL 2022”, we present an exploration of different techniques used to tackle and increase the accuracy of our models using data augmentation in NLP. We also show the results of these techniques.