activity
20182026
most citedWhat do you learn from context? Probing for sentence structure in contextualized word representations

139 citations · 256 across the 34 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CL2025

Findings of the BlackboxNLP 2025 Shared Task: Localizing Circuits and Causal Variables in Language Models

Dana Arad, Yonatan Belinkov, Hanjie Chen +5

Mechanistic interpretability (MI) seeks to uncover how language models (LMs) implement specific behaviors, yet measuring progress in MI remains challenging. The recently released M…

cs.CL2025

Are they lovers or friends? Evaluating LLMs' Social Reasoning in English and Korean Dialogues

Eunsu Kim, Junyeong Park, Juhyun Oh +5

As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore their capabilities, we introduce SC…

cs.CL2025

Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity

Arkadiy Saakyan, Najoung Kim, Smaranda Muresan +1

N-gram novelty is widely used to evaluate language models' ability to generate text outside of their training data. More recently, it has also been adopted as a metric for measurin…

cs.CL2025

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It

Yulu Qin, Dheeraj Varghese, Adam Dahlgren Lindström +3

Does vision-and-language (VL) training change the linguistic representations of language models in meaningful ways? Most results in the literature have shown inconsistent or margin…

cs.CL2025

RExBench: Can coding agents autonomously implement AI research extensions?

Nicholas Edwards, Yukyung Lee, Yujun Audrey Mao +3

Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, there has been progress towards d…

cs.CL2025

Is analogy enough to draw novel adjective-noun inferences?

Hayley Ross, Kathryn Davidson, Najoung Kim

Recent work (Ross et al., 2025, 2024) has argued that the ability of humans and LLMs respectively to generalize to novel adjective-noun combinations shows that they each have acces…

cs.CV2025

Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts

Ibtihel Amara, Ahmed Imtiaz Humayun, Ivana Kajic +12

Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstra…