Large Language Models (LLMs) have recently made a significant impact on the Artificial Intelligence (AI) community. These models utilize advanced Natural Language Processing (NLP) techniques to imitate human behavior accurately. LLMs have gained recognition for their ability to engage in realistic conversations, answer simple and complex questions, generate content, complete code, translate text, and summarize information.

The objective of NLP is to enable computer systems to understand and respond to commands given in natural language, allowing for more natural and flexible interactions. Instruction following models exemplify this capability.

LLMs are trained using supervised examples, LLMs themselves, or other types of supervision. Thousands of tasks written as natural language instructions are used to train these models. Researchers from Mila Quebec AI Institute, McGill University, and Facebook CIFAR AI Chair have conducted a study to evaluate instruction-following models’ performance in question-answering (QA) tasks using a set of given text passages.

Instruction-following models can respond fluently to user queries by incorporating relevant documents and instructions. However, this increased verbosity poses challenges for conventional QA evaluation metrics such as exact match (EM) and F1 score. The model’s response may include additional details that are not explicitly mentioned in the reference answer, yet still accurate.

To address this issue, the research team proposed two criteria for measuring instruction-following models in retrieval-augmented quality assurance (QA):

1. Information Necessity and Accuracy: This criterion evaluates whether the model includes relevant information beyond what is mentioned in the reference answer.

2. Fidelity in Relation to Provided Information: This criterion assesses how well the model grounds its answers in the presented knowledge, avoiding irrelevant information while providing precise responses.

The researchers evaluated several instruction-following models on diverse QA datasets, manually analyzing 900 model responses. They compared the results with various automatic metrics for accuracy and faithfulness. The study found that recall, which measures the percentage of tokens from the reference answer present in the model response, correlates more strongly with correctness than lexical overlap metrics like EM or F1 score.

This research contributes to a comprehensive assessment of instruction-following models for QA tasks, considering their strengths and limitations. The research team has made their code and data accessible on their GitHub repository, promoting further advancements in this field.

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