26 citations · 112 across the 80 of their papers we have counts for
9 papers · 1 filter
Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
Yijie Tong, Yifan Hou, Shaobo Cui +2
Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their application and benefits for vision-lan…
Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees
Xueyan Li, Johannes Zenn, Ekaterina Fadeeva +3
Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like math and code, this strategy i…
Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs
Xueyan Li, Guinan Su, Mrinmaya Sachan +1
Large Language Models (LLMs) are increasingly applied to complex tasks that require extended reasoning. In such settings, models often benefit from diverse chains-of-thought to arr…
Dense SAE Latents Are Features, Not Bugs
Xiaoqing Sun, Alessandro Stolfo, Joshua Engels +4
Sparse autoencoders (SAEs) are designed to extract interpretable features from language models by enforcing a sparsity constraint. Ideally, training an SAE would yield latents that…
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs
Andreas Opedal, Haruki Shirakami, Bernhard Schölkopf +2
Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult t…
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing
Yilmazcan Ozyurt, Stefan Feuerriegel, Mrinmaya Sachan
Knowledge tracing (KT) is a popular approach for modeling students' learning progress over time, which can enable more personalized and adaptive learning. However, existing KT appr…