7 papers
Beyond Accuracy: Decomposing the Reasoning Efficiency of LLMs
Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya +1
As reasoning LLMs increasingly trade tokens for accuracy through deliberation, search, and self-correction, a single accuracy score can no longer tell whether those tokens buy usef…
Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions
Kjetil Indrehus, Adrian Duric, Changkyu Choi +1
Document Visual Question Answering (DocVQA) requires vision-language models to reason not only about what information in a document is relevant to a question, but also where the an…
Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detection
Brage Eilertsen, Røskva Bjørgfinsdóttir, Francielle Vargas +1
The opaque nature of deep learning models presents significant challenges for the ethical deployment of hate speech detection systems. To address this limitation, we introduce Supe…
Principled Operator Learning in Ocean Dynamics: The Role of Temporal Structure
Vahidreza Jahanmard, Ali Ramezani-Kebrya, Robinson Hordoir
Neural operators are becoming the default tools to learn solutions to governing partial differential equations (PDEs) in weather and ocean forecasting applications. Despite early p…
CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Density
Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya +1
Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor interference, and task length.…
Layer-wise Quantization for Quantized Optimistic Dual Averaging
Anh Duc Nguyen, Ilia Markov, Frank Zhengqing Wu +4
Modern deep neural networks exhibit heterogeneity across numerous layers of various types such as residuals, multi-head attention, etc., due to varying structures (dimensions, acti…