arXiv AI
A Financial Brain Scan of the LLM
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Illusory Pattern Perception Drives Spurious Inference in Large Language Models
The paper "Illusory Pattern Perception Drives Spurious Inference in Large Language Models" reports that large language models (LLMs) exhibit a cognitive bias similar to humans’ tendency to see patterns where none exist. By adapting classic psychological experiments to three tasks, the authors find that LLMs often over‑associate frequent positive attributes with majority groups or large organizations and are prone to constructing causal narratives from ambiguous events. Using a Sparse Autoencoder‑based interpretability framework, the study links these illusory perceptions to holistic frequency perception and analytic cognitive orientation within the models.
arXiv AI
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Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models
arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigg...
Hugging Face Trending Papers
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SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization
Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies primarily on external observation. This reliance leads to superficial explanations inferred from observed model behavior and computational inefficiency from collecting such behavioral evidence at scale.
arXiv Machine Learning
Sep 2
Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment
arXiv:2609.01322v1 Announce Type: cross Abstract: In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff i...
Hugging Face Trending Papers
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Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment
In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in constructing text representations for adjustment:...
arXiv Machine Learning
Jul 21
Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
arXiv:2604. 16197v2 Announce Type: replace Abstract: Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods suffer from scalability challenges on LLMs.