This paper presents the approach of the NLPeople team for the Text-Graph Representations for KGQA Shared Task at TextGraphs-17. The task involved selecting an answer for a given question from a list of candidate entities. We show that prompting Large Language models (LLMs) to break down a natural language question into a series of sub-questions, allows models to understand complex questions. The LLMs arrive at the final answer by answering the intermediate questions using their internal knowledge and without needing additional context. Our approach to the task uses an ensemble of prompting strategies to guide how LLMs interpret various types of questions. Our submission achieves an F1 score of 85.90, ranking 1st among the other participants in the task.

This paper presents our approach submitted to the Language + Molecules 2024 (L+M-24) Shared Task in the Molecular Captioning track. The task involves generating captions that describe the properties of molecules that are provided in SMILES format.We propose a method for the task that decomposes the challenge of generating captions from SMILES into a classification problem,where we first predict the molecule’s properties. The molecules whose properties can be predicted with high accuracy show high translation metric scores in the caption generation by LLMs, while others produce low scores. Then we use the predicted properties to select the captions generated by different types of LLMs, and use that prediction as the final output. Our submission achieved an overall increase score of 15.21 on the dev set and 12.30 on the evaluation set, based on translation metrics and property metrics from the baseline.

This paper presents the approach of the NLPeople team to the Nuanced Arabic Dialect Identification (NADI) 2023 shared task. Subtask 1 involves identifying the dialect of a source text at the country level. Our approach to Subtask 1 makes use of language-specific language models, a clustering and retrieval method to provide additional context to a target sentence, a fine-tuning strategy which makes use of the provided data from the 2020 and 2021 shared tasks, and finally, ensembling over the predictions of multiple models. Our submission achieves a macro-averaged F1 score of 87.27, ranking 1st among the other participants in the task.