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20242026
most citedFrom Pixels to Components: Eigenvector Masking for Visual Representation Learning

2 citations · 4 across the 6 of their papers we have counts for

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cs.CL2026

When Do Language Models Endorse Limitations on Human Rights Principles?

Keenan Samway, Nicole Miu Takagi, Rada Mihalcea +4

As Large Language Models (LLMs) increasingly mediate global information access with the potential to shape public discourse, their alignment with universal human rights principles…

cs.CL2025

Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models

David Guzman Piedrahita, Irene Strauss, Bernhard Schölkopf +2

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior…

cs.CL2025

Improving Large Language Model Safety with Contrastive Representation Learning

Samuel Simko, Mrinmaya Sachan, Bernhard Schölkopf +1

Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to…

cs.CL2025

Counterfactual reasoning: an analysis of in-context emergence

Moritz Miller, Bernhard Schölkopf, Siyuan Guo

Large-scale neural language models exhibit remarkable performance in in-context learning: the ability to learn and reason about the input context on the fly. This work studies in-c…

cs.CL2024

Analyzing the Role of Semantic Representations in the Era of Large Language Models

Zhijing Jin, Yuen Chen, Fernando Gonzalez +5

Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of l…