Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks
This work addresses the challenge of assessing LLMs' language comprehension in complex, domain-specific tasks, though it appears incremental as it builds on existing methods like CoT and question decomposition.
The researchers tackled the problem of evaluating large language models on domain-specific multi-hop question answering tasks using the HotpotQA dataset, finding that a two-stage selector-reader architecture with techniques like Chain of Thought and question decomposition improved F1 scores by up to 4%.
In recent years, the use of large language models (LLMs) has significantly increased, and these models have demonstrated remarkable performance in a variety of general language tasks. However, the evaluation of their performance in domain-specific tasks, particularly those requiring deep natural language understanding, has received less attention. In this research, we evaluate the ability of large language models in performing domain-specific tasks, focusing on the multi-hop question answering (MHQA) problem using the HotpotQA dataset. This task, due to its requirement for reasoning and combining information from multiple textual sources, serves as a challenging benchmark for assessing the language comprehension capabilities of these models. To tackle this problem, we have designed a two-stage selector-reader architecture, where each stage utilizes an independent LLM. In addition, methods such as Chain of Thought (CoT) and question decomposition have been employed to investigate their impact on improving the model's performance. The results of the study show that the integration of large language models with these techniques can lead to up to a 4% improvement in F1 score for finding answers, providing evidence of the models' ability to handle domain-specific tasks and their understanding of complex language.