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

arXiv: https://arxiv.org/abs/2306.13865 · HTML: https://mlsanity.com/paper/2306.13865/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 28 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [KALA: Knowledge-Augmented Language Model Adaptation](https://mlsanity.com/paper/2204.10555/) (2022)
- [Neurosymbolic AI -- Why, What, and How](https://mlsanity.com/paper/2305.00813/) (2023)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [Process Knowledge-infused Learning for Clinician-friendly Explanations](https://mlsanity.com/paper/2306.09824/) (2023)
- ["Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision](https://mlsanity.com/paper/2102.01222/) (2021)
- ["Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit](https://mlsanity.com/paper/2105.06398/) (2021)
- [Knowledge Infused Policy Gradients for Adaptive Pandemic Control](https://mlsanity.com/paper/2102.06245/) (2021)
- [NLP is Not enough -- Contextualization of User Input in Chatbots](https://mlsanity.com/paper/2105.06511/) (2021)
- [KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts](https://mlsanity.com/paper/2210.04307/) (2022)
- [COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data](https://mlsanity.com/paper/2010.14628/) (2020)
- [Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case](https://mlsanity.com/paper/2304.00025/) (2023)
- [eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket](https://mlsanity.com/paper/2103.15764/) (2021)
- [Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust](https://mlsanity.com/paper/2305.04989/) (2023)
- [Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes](https://mlsanity.com/paper/2306.01805/) (2023)

## Built on by (most cited first, 3 of 3)

- [Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devices](https://mlsanity.com/paper/2502.19410/) (2025)
- [L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models](https://mlsanity.com/paper/2311.06493/) (2023)
- [RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding](https://mlsanity.com/paper/2312.09932/) (2023)

## Abstract (arXiv)

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.