title: Rethinking Interpretability in the Era of Large Language Models
summary: Interpretable machine learning has exploded as an area of interest over the
  last decade, sparked by the rise of increasingly large datasets and deep neural
  networks. Simultaneously, large language models (LLMs) have demonstrated
  remarkable capabilities across a wide array of tasks, offering a chance to
  rethink opportunities in interpretable machine learning. Notably, the
  capability to explain in natural language allows LLMs to expand the scale and
  complexity of patterns that can be given to a human. However, these new
  capabilities raise new challenges, such as hallucinated explanations and
  immense computational costs.
    In this position paper, we start by reviewing existing methods to evaluate
  the emerging field of LLM interpretation (both interpreting LLMs and using LLMs
  for explanation). We contend that, despite their limitations, LLMs hold the
  opportunity to redefine interpretability with a more ambitious scope across
  many applications, including in auditing LLMs themselves. We highlight two
  emerging research priorities for LLM interpretation: using LLMs to directly
  analyze new datasets and to generate interactive explanations.
paper_authors._id: 65c1b38ec79ecb36bea97912
paper_authors.name: Chandan Singh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-06T16:22:09.793Z
paper_authors._id: 62b0bee117fa24819dd0999c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b0bee117fa24819dd0999c/eJ7ryFQ3_ZbqV29qf4KyM.jpeg
paper_authors.fullname: Chandan Singh
paper_authors.type: user
paper_authors.user: csinva
paper_authors._id: 65c1b38ec79ecb36bea97913
paper_authors.name: Jeevana Priya Inala
paper_authors._id: 65c1b38ec79ecb36bea97914
paper_authors.name: Michel Galley
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T10:36:11.363Z
paper_authors._id: 5f6bed855e78cc6b0ed31dee
paper_authors.avatarUrl: /avatars/d67edb079fa3fe7ea6f2081f7a3afe9e.svg
paper_authors.fullname: Michel Galley
paper_authors.type: user
paper_authors.user: mgalley
paper_authors._id: 65c1b38ec79ecb36bea97915
paper_authors.name: Rich Caruana
paper_authors._id: 65c1b38ec79ecb36bea97916
paper_authors.name: Jianfeng Gao
paper_title: Rethinking Interpretability in the Era of Large Language Models
paper_summary: Interpretable machine learning has exploded as an area of interest over the
  last decade, sparked by the rise of increasingly large datasets and deep neural
  networks. Simultaneously, large language models (LLMs) have demonstrated
  remarkable capabilities across a wide array of tasks, offering a chance to
  rethink opportunities in interpretable machine learning. Notably, the
  capability to explain in natural language allows LLMs to expand the scale and
  complexity of patterns that can be given to a human. However, these new
  capabilities raise new challenges, such as hallucinated explanations and
  immense computational costs.
    In this position paper, we start by reviewing existing methods to evaluate
  the emerging field of LLM interpretation (both interpreting LLMs and using LLMs
  for explanation). We contend that, despite their limitations, LLMs hold the
  opportunity to redefine interpretability with a more ambitious scope across
  many applications, including in auditing LLMs themselves. We highlight two
  emerging research priorities for LLM interpretation: using LLMs to directly
  analyze new datasets and to generate interactive explanations.
paper_ai_keywords: large language models (LLMs) natural language hallucinated explanations interpretability auditing LLMs interactive explanations