This paper discusses about the SKETCH approach which enhances text retrieval and context relevancy on large corpuses compared to the traditional baseline methods. The abstract attached below discusses this further. Abstract: Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a comprehensive understanding of the context. This paper introduces SKETCH, a novel methodology that enhances the RAG retrieval process by integrating semantic text retrieval with knowledge graphs, thereby merging structured and unstructured data for a more holistic comprehension. SKETCH, demonstrates substantial improvements in retrieval performance and maintains superior context integrity compared to traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER, NarrativeQA, and Italian Cuisine—SKETCH consistently outperforms baseline approaches on key RAGAS metrics such as answer relevancy, faithfulness, context precision and context recall. Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99, representing the highest performance across all evaluated metrics. These results highlight SKETCH’s capability in delivering more accurate and contextually relevant responses, setting new benchmarks for future retrieval systems.

The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse lin- guistic contexts. In this paper, we propose a novel methodology to enhance CS generation by aligning Large Language Models (LLMs) using Supervised Fine-Tuning (SFT) and Di- rect Preference Optimization (DPO). Our ap- proach leverages DPO to align LLM outputs with human preferences, ensuring contextu- ally appropriate and linguistically adaptable responses. Additionally, we incorporate knowl- edge grounding to enhance the factual accuracy and relevance of generated CS. Experimental results demonstrate that DPO-aligned models significantly outperform SFT baselines on CS benchmarks while scaling effectively to mul- tiple languages. These findings highlight the potential of preference-based alignment tech- niques to advance CS generation across var- ied linguistic settings. The model supervision and alignment is done in English and the same model is used for reporting metrics across other languages like Basque, Italian, and Spanish.

Detection of Homophobia and Transphobia in social media comments serves as an important step in the overall development of Equality, Diversity and Inclusion (EDI). In this research, we describe the system we formulated while participating in the shared task of Homophobia/ Transphobia detection as a part of the Fourth Workshop On Language Technology For Equality, Diversity, Inclusion (LT-EDI- 2024) at EACL 2024. We used an ensemble of three state-of-the-art multilingual transformer models, namely Multilingual BERT (mBERT), Multilingual Representations for Indic Languages (MuRIL) and XLM-RoBERTa to detect the presence of Homophobia or Transphobia in YouTube comments. The task comprised of datasets in ten languages - Hindi, English, Telugu, Tamil, Malayalam, Kannada, Gujarati, Marathi, Spanish and Tulu. Our system achieved rank 1 for the Spanish and Tulu tasks, 2 for Telugu, 3 for Marathi and Gujarati, 4 for Tamil, 5 for Hindi and Kannada, 6 for English and 8 for Malayalam. These results speak for the efficacy of our ensemble model as well as the data augmentation strategy we adopted for the detection of anti-LGBT+ language in social media data.

Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models’ ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of State-of-the-Art pre-trained multilingual language models using WADER and analyze the use of sampling techniques to mitigate bias in data and optimally select augmentation candidates. Our results show that WADER outperforms the baseline model and provides a direction for mitigating data imbalance and scarcity in text regression tasks.