2023 Fiscal Year Annual Research Report
Knowledge-Base-Grounded Language Models
| Project/Area Number |
21K17814
|
|---|
| Research Institution | Institute of Physical and Chemical Research |
|---|
Principal Investigator |
HEINZERLING BENJAMIN 国立研究開発法人理化学研究所, 革新知能統合研究センター, 副チームリーダー (50846491)
|
|---|
| Project Period (FY) |
2021-04-01 – 2024-03-31
|
| Keywords | language model / knowledge base / world knowledge / numeric properties / knowledge representation / interpretability |
|---|
| Outline of Annual Research Achievements |
In 2023, we developed an interpretation method for analyzing how well LMs represent a specific kind of structured knowledge, namely an numeric properties such as a person's year of birth or a city's population.
This method allows directly analyzing and manipulating the internal state of LMs in order to control its behavior when generating output involving numeric properties.
In the broader context of interpretability, transparency, and explainability of LMs, this method contributes to a improved understanding of how LMs encode structured knowledge.
|
