arXiv AI August 7, 2026 By Arya Labroo, Mengjie Qian, Kate Knill

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

arXiv:2608. 06300v1 Announce Type: new Abstract: Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 18

From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability

arXiv:2606. 18383v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable features from language models (LMs), yet a central question remains: when can an SAE-based explanation be treated as a faithful view of an underlying frozen LM We study this through a post-hoc generalization framework that certifies the LM via a sparse proxy, obtained by replacing a native hidden activation with its pretrained SAE reconstruction.

By Dibyanayan Bandyopadhyay, Asif Ekbal

llms efficiency safety

arXiv AI

Jun 11

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

arXiv:2506. 20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer analyses to miss this cross-layer structure.

By Ankur Garg, Xuemin Yu, Hassan Sajjad, Samira Ebrahimi Kahou

llms diffusion efficiency

arXiv AI

Jul 13

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.

By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

llms fine-tuning multimodal safety

arXiv Machine Learning

Aug 10

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

arXiv:2608. 06417v1 Announce Type: new Abstract: The proliferation of misinformation online has driven demand for scalable detection systems.

By Pedro Barcelos, Ot\'avio Parraga, Marcelo M. Mussi, Lucas M. Fraga, Lucas S. Kupssinsk\"u, Rodrigo C. Barros

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jul 21

Measuring Monosemanticity in Sparse Autoencoders via Latent Activation Coherence

arXiv:2607. 17770v1 Announce Type: cross Abstract: Within Explainable Artificial Intelligence, mechanistic interpretability uses Sparse Autoencoders (SAEs) to extract more interpretable features from neural representations.

By Katarzyna Filus, Sebastian Pokuci\'nski

llms rag efficiency multimodal safety

arXiv AI

Aug 10

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests

arXiv:2608. 06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT).

By Seitaro Ono, Senna Ross, Jun Saiki

llms rag nlp benchmarks safety

Related stories

From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

Measuring Monosemanticity in Sparse Autoencoders via Latent Activation Coherence

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests