Challenge: Existing interpretation methods only support tasks with specific inputs, limiting their practical applications.
Approach: They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs.
Outcome: The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs.

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Challenge: Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution.
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Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Challenge: specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
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How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

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Challenge: Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
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A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025.findings-emnlp)

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Challenge: Sparse Autoencoders (SAEs) can disentangle complex features into more interpretable components.
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LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)

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Challenge: Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
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A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

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Challenge: Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
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Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach (2023.findings-emnlp)

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Challenge: Existing studies on LLMs evaluation with exams are lacking in cognitive research on their overall knowledge structure.
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Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures (2026.acl-long)

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Challenge: Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations.
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Explaining Language Model Predictions with High-Impact Concepts (2024.findings-eacl)

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Challenge: Existing methods to explain large language models (LLMs) are mostly correlational and lack causal features due to compositional nature of languages.
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Do Large Language Models Know How Much They Know? (2024.emnlp-main)

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Challenge: Large Language Models are highly capable systems, but their capabilities and limitations are unclear.
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