**The Significance of Large Language Models (LLMs) in Natural Language Processing**
Large Language Models (LLMs) have proven to be incredibly powerful in natural language processing and are used across various fields, with factual question-answering being a common application. However, providing factual answers accurately at different levels of granularity poses a challenge. This leads to discrepancies between lexical matching and human assessment. Standard question-answering (QA) evaluation settings do not consider the versatility of factual answers, leading to an underestimation of the knowledge of LLMs, referred to as a knowledge evaluation gap.
**The Introduction of GRANOLA QA**
To address this issue, the authors of a research paper from Google have introduced GRANOLA QA, a multi-granularity QA evaluation setting that not only evaluates the accuracy of answers but also their informativeness. The GRANOLA answer generation process involves using an external knowledge graph and having an LLM create an ordered list of answers of varying granularity levels. This unique approach aims to accurately evaluate the knowledge of LLMs.
**The Results and Limitations of the Research**
The researchers have also developed GRANOLA-EQ and evaluated models using different decoding methods, including a proposed novel decoding strategy called DRAG. The results showed that LLMs tend to generate specific answers that are often incorrect, but when evaluated using DRAG for multi-granularity answers, there is an increase in accuracy. However, there are some limitations to their work, including the involvement of the extraction process and the need to distinguish between correct answers based on true knowledge and educated guesses.
Nevertheless, the authors’ work serves as a significant step towards aligning the responses of LLMs with their uncertainty level. Their approach opens doors for future research in this area.
This research shows the potential of using GRANOLA QA to enhance the evaluation of large language models, shedding light on a crucial aspect of natural language processing.