This work describes an approach to develop Knowledge Graph Question Answering (KGQA) system for TextGraphs-17 shared task. The task focuses on the fusion of Large Language Models (LLMs) with Knowledge Graphs (KGs). The goal is to select a KG entity (out of several candidates) which corresponds to an answer given a textual question. Our approach applies LLM to identify the correct answer among the list of possible candidates. We confirm that integrating external information is particularly beneficial when the subject entities are not well-known, and using RAG can negatively impact the performance of LLM on questions related to popular entities, as the retrieved context might be misleading. With our result, we achieved 2nd place in the post-evaluation phase.

This paper presents the solution of the DeepPavlov team for the Multimodal Sentiment Cause Analysis competition in SemEval-2024 Task 3, Subtask 2 (Wang et al., 2024). In the evaluation leaderboard, our approach ranks 7th with an F1-score of 0.2132. Large Language Models (LLMs) are transformative in their ability to comprehend and generate human-like text. With recent advancements, Multimodal Large Language Models (MLLMs) have expanded LLM capabilities, integrating different modalities such as audio, vision, and language. Our work delves into the state-of-the-art MLLM Video-LLaMA, its associated modalities, and its application to the emotion reasoning downstream task, Multimodal Emotion Cause Analysis in Conversations (MECAC). We investigate the model’s performance in several modes: zero-shot, few-shot, individual embeddings, and fine-tuned, providing insights into their limits and potential enhancements for emotion understanding.