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Large Language Models (LLMs) encode meanings of words in the form ofdistributed semantics. Distributed semantics capture common statisticalpatterns among language tokens (words, phrases, and sentences) from largeamounts of data. LLMs perform exceedingly well across General LanguageUnderstanding Evaluation (GLUE) tasks designed to test a model's understandingof the meanings of the input tokens. However, recent studies have shown thatLLMs tend to generate unintended, inconsistent, or wrong texts as outputs whenprocessing inputs that were seen rarely during training, or inputs that areassociated with diverse contexts (e.g., well-known hallucination phenomenon inlanguage generation tasks). Crowdsourced and expert-curated knowledge graphssuch as ConceptNet are designed to capture the meaning of words from a compactset of well-defined contexts. Thus LLMs may benefit from leveraging suchknowledge contexts to reduce inconsistencies in outputs. We propose a novelensemble learning method, Interpretable Ensemble Representation Learning(IERL), that systematically combines LLM and crowdsourced knowledgerepresentations of input tokens. IERL has the distinct advantage of beinginterpretable by design (when was the LLM context used vs. when was theknowledge context used?) over state-of-the-art (SOTA) methods, allowingscrutiny 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, wedemonstrate our approach using BERT and ConceptNet. We report improved orcompetitive results with IERL across GLUE tasks over current SOTA methods andsignificantly enhanced model interpretability.

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations