5 papers
Learning Structured Reasoning via Tractable Trajectory Control
Po-Nien Kung, Zhen Yang, Jeffrey Luo +7
Large language models can exhibit emergent reasoning behaviors, often manifested as recurring lexical patterns (e.g., "wait," indicating verification). However, complex reasoning t…
When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning
Ruixiang Mao, Xiangnan Ma, Dan Chen +12
Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive p…
Scale Can't Overcome Pragmatics: The Impact of Reporting Bias on Vision-Language Reasoning
Amita Kamath, Jack Hessel, Khyathi Chandu +3
The lack of reasoning capabilities in Vision-Language Models (VLMs) has remained at the forefront of research discourse. We posit that this behavior stems from a reporting bias in…
GenEval 2: Addressing Benchmark Drift in Text-to-Image Evaluation
Amita Kamath, Kai-Wei Chang, Ranjay Krishna +3
Automating Text-to-Image (T2I) model evaluation is challenging; a judge model must be used to score correctness, and test prompts must be selected to be challenging for current T2I…
The Hard Positive Truth about Vision-Language Compositionality
Amita Kamath, Cheng-Yu Hsieh, Kai-Wei Chang +1
Several benchmarks have concluded that our best vision-language models (e.g., CLIP) are lacking in compositionality. Given an image, these benchmarks probe a model's ability to ide…