  Large Language Models (LLMs) encode meanings of words in the form of
distributed semantics. Distributed semantics capture common statistical
patterns among language tokens (words, phrases, and sentences) from large
amounts of data. LLMs perform exceedingly well across General Language
Understanding Evaluation (GLUE) tasks designed to test a model's understanding
of the meanings of the input tokens. However, recent studies have shown that
LLMs tend to generate unintended, inconsistent, or wrong texts as outputs when
processing inputs that were seen rarely during training, or inputs that are
associated with diverse contexts (e.g., well-known hallucination phenomenon in
language generation tasks). Crowdsourced and expert-curated knowledge graphs
such as ConceptNet are designed to capture the meaning of words from a compact
set of well-defined contexts. Thus LLMs may benefit from leveraging such
knowledge contexts to reduce inconsistencies in outputs. We propose a novel
ensemble learning method, Interpretable Ensemble Representation Learning
(IERL), that systematically combines LLM and crowdsourced knowledge
representations of input tokens. IERL has the distinct advantage of being
interpretable by design (when was the LLM context used vs. when was the
knowledge context used?) over state-of-the-art (SOTA) methods, allowing
scrutiny of the inputs in conjunction with the parameters of the model,
facilitating the analysis of models' inconsistent or irrelevant outputs.
Although IERL is agnostic to the choice of LLM and crowdsourced knowledge, we
demonstrate our approach using BERT and ConceptNet. We report improved or
competitive results with IERL across GLUE tasks over current SOTA methods and
significantly enhanced model interpretability.
