Question: How do Large Language Models perform on the Question Answering tasks? Answer:

TL;DR

This study compares the performance of smaller fine-tuned models and large instruction-following LLMs on question-answering tasks, highlighting their strengths and limitations in both fine-tuned and out-of-distribution scenarios.

Contribution

It introduces a single-inference prompting method for unanswerable questions and evaluates model generalization across different QA datasets without fine-tuning.

Findings

Smaller fine-tuned models outperform SOTA LLMs on fine-tuned QA tasks.

Recent SOTA models close the gap on out-of-distribution datasets and outperform fine-tuned models on most datasets.

Single-inference prompting effectively handles unanswerable questions, reducing computational resources.

Abstract

Large Language Models (LLMs) have been showing promising results for various NLP-tasks without the explicit need to be trained for these tasks by using few-shot or zero-shot prompting techniques. A common NLP-task is question-answering (QA). In this study, we propose a comprehensive performance comparison between smaller fine-tuned models and out-of-the-box instruction-following LLMs on the Stanford Question Answering Dataset 2.0 (SQuAD2), specifically when using a single-inference prompting technique. Since the dataset contains unanswerable questions, previous work used a double inference method. We propose a prompting style which aims to elicit the same ability without the need for double inference, saving compute time and resources. Furthermore, we investigate their generalization capabilities by comparing their performance on similar but different QA datasets, without fine-tuning neither model, emulating real-world uses where the context and questions asked may differ from the original training distribution, for example swapping Wikipedia for news articles. Our results show that smaller, fine-tuned models outperform current State-Of-The-Art (SOTA) LLMs on the fine-tuned task, but recent SOTA models are able to close this gap on the out-of-distribution test and even outperform the fine-tuned models on 3 of the 5 tested QA datasets.