On the Versatility of Sparse Autoencoders for In-Context Learning (2025.findings-emnlp)
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| Challenge: | Sparse autoencoders (SAEs) are emerging as a key analytical tool in interpretability for large language models. |
| Approach: | They propose to use SAEs to extract knowledge from billions of tokens for sparse reconstruction. |
| Outcome: | The proposed model can extract knowledge from billions of tokens for sparse reconstruction. |
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Toward Efficient Sparse Autoencoder-Guided Steering for Improved In-Context Learning in Large Language Models (2025.emnlp-main)
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| Challenge: | Sparse autoencoders (SAEs) have emerged as a powerful analytical tool in mechanistic interpretability for large language models (LLMs). |
| Approach: | They propose a novel approach that leverages SAEs to enhance the general in-context learning performance of large language models (LLMs). |
| Outcome: | The proposed method yields a 3.5% improvement across diverse text classification tasks and exhibits greater robustness to hyperparameter variations compared to standard steering approaches. |
Sparse Autoencoder Features for Classifications and Transferability (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) provide potential for uncovering structured, human-interpretable representations in Large Language Models (LLMs). |
| Approach: | They analyze SAEs for interpretable feature extraction from Large Language Models in safety-critical classification tasks. |
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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. |
| Approach: | They propose to use Sparse Autoencoders to disentangle LLM features into more interpretable components. |
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Group-SAE: Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) are a promising unsupervised approach for understanding the representations of layers of Large Language Models (LLMs). |
| Approach: | They propose a method that groups similar models and trains a single SAE per group based on representational similarity across layers. |
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Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders (2026.findings-eacl)
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| Challenge: | Concept-based explanations for large language models are not well understood in text classification. |
| Approach: | They propose a model with a specialized classifier head and activation rate sparsity loss for sentence classification . they compare it to existing models with HI-Concept and ConceptShap . |
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Route Sparse Autoencoder to Interpret Large Language Models (2025.emnlp-main)
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| Challenge: | Sparse autoencoders (SAEs) extract interpretable and monosemantic features in large language models . prior work focused on feature extraction from a single layer, failing to capture activations that span multiple layers. |
| Approach: | They propose a framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers. |
| Outcome: | The proposed framework extracts features from multiple layers while incurring minimal parameter overhead while achieving high interpretability and flexibility. |
SAEs Are Good for Steering – If You Select the Right Features (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) can learn a decomposition of a model’s latent space by analyzing the input tokens that activate them. |
| Approach: | They propose an unsupervised approach to learn a decomposition of a model’s latent space by analyzing the input tokens that activate them. |
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AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations (2026.findings-acl)
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| Challenge: | Existing approaches to decomposing model activations into interpretable features fail to account for input complexity. |
| Approach: | They propose a framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. |
| Outcome: | The proposed framework outperforms fixed-sparsity approaches on reconstruction fidelity, explained variance, cosine similarity and interpretability metrics while eliminating the burden of extensive hyperparameter tuning. |
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders (2025.findings-acl)
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Kristian Kuznetsov, Laida Kushnareva, Anton Razzhigaev, Polina Druzhinina, Anastasia Voznyuk, Irina Piontkovskaya, Evgeny Burnaev, Serguei Barannikov
| Challenge: | Existing algorithms for AI text detection lack interpretability, limiting their reliability in highstakes applications. |
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| Outcome: | The proposed algorithms can extract human-interpretable features from Gemma-2-2b model. |
Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) exhibit impressive abilities in various domains such as text generation, instruction following, and reasoning. |
| Approach: | They propose a method to decompose the activations of Large Language Models into a sparse linear combination of SAE features. |
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