Advances in Language Model Interpretability and Representation

The field of natural language processing is witnessing significant developments in understanding and interpreting large language models (LLMs). Recent research has focused on analyzing the internal workings of LLMs, including their ability to represent linguistic information, encode factual knowledge, and make predictions. Studies have shown that LLMs can be decomposed into interpretable components, revealing insights into their decision-making processes and the role of different components in storing and retrieving information. Furthermore, new methods have been proposed to represent sentence embeddings in a more transparent and controllable manner. Notably, the use of self-supervised objectives has been explored for improving low-resource morphological inflection tasks. Overall, the field is moving towards a deeper understanding of LLMs, enabling the development of more reliable and interpretable models. Noteworthy papers include:

  • Large Language Models are Locally Linear Mappings, which demonstrates that LLMs can be mapped to equivalent linear systems, providing insights into their internal representations.
  • Mechanistic Decomposition of Sentence Representations, which proposes a method to decompose sentence embeddings into interpretable components, bridging token-level and sentence-level analysis.

Sources

The Surprising Soupability of Documents in State Space Models

Mamba Knockout for Unraveling Factual Information Flow

Large Language Models are Locally Linear Mappings

Model Internal Sleuthing: Finding Lexical Identity and Inflectional Morphology in Modern Language Models

Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics

Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs

Time Course MechInterp: Analyzing the Evolution of Components and Knowledge in Large Language Models

Is linguistically-motivated data augmentation worth it?

On Support Samples of Next Word Prediction

Mechanistic Decomposition of Sentence Representations

Static Word Embeddings for Sentence Semantic Representation

Line of Sight: On Linear Representations in VLLMs

Improving Low-Resource Morphological Inflection via Self-Supervised Objectives

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