Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction
CoRR(2024)
摘要
We propose an approach for estimating the latent knowledge embedded inside
large language models (LLMs). We leverage the in-context learning (ICL)
abilities of LLMs to estimate the extent to which an LLM knows the facts stored
in a knowledge base. Our knowledge estimator avoids reliability concerns with
previous prompting-based methods, is both conceptually simpler and easier to
apply, and we demonstrate that it can surface more of the latent knowledge
embedded in LLMs. We also investigate how different design choices affect the
performance of ICL-based knowledge estimation. Using the proposed estimator, we
perform a large-scale evaluation of the factual knowledge of a variety of open
source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set
of relations and facts from the Wikidata knowledge base. We observe differences
in the factual knowledge between different model families and models of
different sizes, that some relations are consistently better known than others
but that models differ in the precise facts they know, and differences in the
knowledge of base models and their finetuned counterparts.
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