1 citations · 1 across the 5 of their papers we have counts for
7 papers
Sequence Repetition Enhances Token Embeddings and Improves Sequence Labeling with Decoder-only Language Models
Matija Luka Kukić, Marko Čuljak, David Dukić +2
Modern language models (LMs) are trained in an autoregressive manner, conditioned only on the prefix. In contrast, sequence labeling (SL) tasks assign labels to each individual inp…
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…
PragWorld: A Benchmark Evaluating LLMs' Local World Model under Minimal Linguistic Alterations and Conversational Dynamics
Sachin Vashistha, Aryan Bibhuti, Atharva Naik +2
Real-world conversations are rich with pragmatic elements, such as entity mentions, references, and implicatures. Understanding such nuances is a requirement for successful natural…
Context Parametrization with Compositional Adapters
Josip Jukić, Martin Tutek, Jan Šnajder
Large language models (LLMs) often seamlessly adapt to new tasks through in-context learning (ICL) or supervised fine-tuning (SFT). However, ICL is inefficient when handling many d…
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
David Dukić, Ana Barić, Marko Čuljak +2
Measuring how semantics of words change over time improves our understanding of how cultures and perspectives change. Diachronic word embeddings help us quantify this shift, althou…
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe +20
How can we know whether new mechanistic interpretability methods achieve real improvements? In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretabili…