Recent advances in language models have substantially improved Natural Language Understanding (NLU). Although widely used benchmarks suggest that Large Language Models (LLMs) can effectively disambiguate, their practical applicability in real-world narrative contexts remains underexplored.SemEval-2026 Task 5 addresses this gap by introducing a task that predicts the human-perceived plausibility of a word sense within a short story. In this work, we propose an LLM-based framework for plausibility scoring of homonymous word senses in narrative texts using a structured reasoning mechanism. We examine the impact of fine-tuning low-parameter LLMs with diverse reasoning strategies, alongside dynamic few-shot prompting for large-parameter models, on accurate sense identification and plausibility estimation. Our results show that commercial large-parameter LLMs with dynamic few-shot prompting closely replicate human-like plausibility judgments. Furthermore, model ensembling slightly improves performance, better simulating the agreement patterns of five human annotators compared to single-model predictions.

In multilingual societies, it is common to observe the blending of multiple languages in communication, a phenomenon known as Code-mixing. Globalization and the increasing influence of social media have further amplified multilingualism, resulting in a wider use of code-mixing. This systematic review analyzes existing translation and transliteration techniques for code-mixed Indo-Aryan languages, spanning rule-based and statistical approaches to neural machine translation and transformer-based architectures. It also examines publicly available code-mixed datasets designed for machine translation and transliteration tasks, along with the evaluation metrics commonly introduced and applied in prior studies. Finally, the paper discusses current challenges and limitations, highlighting future research directions for developing more tailored translation pipelines for code-mixed Indo-Aryan languages.