Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust
This work addresses the opacity of language models for societal trust issues, but it is incremental as it builds on existing evaluation methods with new metrics and benchmarks.
The study evaluated the semantics captured by self-attention transformer language models using knowledge graphs, finding that these models generate plausible language patterns but lack object and concept-level meaning, as evidenced by reconstruction error metrics. It also introduced an augmented benchmark based on GLUE to assess concept understanding, highlighting implications for user trust in high-stakes applications.
A fundamental question in natural language processing is - what kind of language structure and semantics is the language model capturing? Graph formats such as knowledge graphs are easy to evaluate as they explicitly express language semantics and structure. This study evaluates the semantics encoded in the self-attention transformers by leveraging explicit knowledge graph structures. We propose novel metrics to measure the reconstruction error when providing graph path sequences from a knowledge graph and trying to reproduce/reconstruct the same from the outputs of the self-attention transformer models. The opacity of language models has an immense bearing on societal issues of trust and explainable decision outcomes. Our findings suggest that language models are models of stochastic control processes for plausible language pattern generation. However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs. Furthermore, to enable robust evaluation of concept understanding by language models, we construct and make public an augmented language understanding benchmark built on the General Language Understanding Evaluation (GLUE) benchmark. This has significant application-level user trust implications as stochastic patterns without a strong sense of meaning cannot be trusted in high-stakes applications.