mlsanityData to 7 Oct 2026

Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models

arXiv 2311.01732 ↗First posted 3 Nov 2023cs.CLcited by 7

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Abstract (arXiv)

Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), but their lack of interpretability has been a major concern. Current methods for interpreting LLMs are post hoc, applied after inference time, and have limitations such as their focus on low-level features and lack of explainability at higher level text units. In this work, we introduce proto-lm, a prototypical network-based white-box framework that allows LLMs to learn immediately interpretable embeddings during the fine-tuning stage while maintaining competitive performance. Our method's applicability and interpretability are demonstrated through experiments on a wide range of NLP tasks, and our results indicate a new possibility of creating interpretable models without sacrificing performance. This novel approach to interpretability in LLMs can pave the way for more interpretable models without the need to sacrifice performance.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 40 references that are in the map. First posted on arXiv 3 Nov 2023.

Builds on

33 of 40 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  2. 2025Bias Attribution in Filipino Language Models: Extending a Bias Interpretability Metric for Application on Agglutinative Languagescited by 2
  3. 2025Protocode: Prototype-Driven Interpretability for Code Generation in LLMscited by 1
  4. 2024The Mysterious Case of Neuron 1512: Injectable Realignment Architectures Reveal Internal Characteristics of Meta's Llama 2 Modelcited by 0
  5. 2025ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classificationcited by 0
  6. 2025ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modelingcited by 0
  7. 2026Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learningcited by 0