6 papers
Brain alignment of reasoning and action representations from vision-language and action models during naturalistic gameplay
Subba Reddy Oota, Anant Khandelwal, Khushbu Pahwa +4
Understanding how humans and artificial intelligence systems predict and plan by interacting with their environment is a fundamental challenge at the intersection of neuroscience a…
Linguistic properties and model scale in brain encoding: from small to compressed language models
Subba Reddy Oota, Vijay Rowtula, Satya Sai Srinath Namburi +5
Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains and which represe…
Task-conditioned probing of instruction-tuned multimodal LLMs: Region-specific brain alignment patterns under naturalistic stimuli
Subba Reddy Oota, Khushbu Pahwa, Prachi Jindal +5
Recent voxel-wise multimodal brain encoding studies have shown that multimodal large language models (MLLMs) exhibit a higher degree of brain alignment compared to unimodal models.…
Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Subba Reddy Oota, Akshett Jindal, Ishani Mondal +6
Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models-thr…
Pretrained Hybrids with MAD Skills
Nicholas Roberts, Samuel Guo, Zhiqi Gao +5
While Transformers underpin modern large language models (LMs), there is a growing list of alternative architectures with new capabilities, promises, and tradeoffs. This makes choo…
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
Harit Vishwakarma, Reid, Chen +4
Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual labeling. A prominent variant, threshold-based auto-labeling (TBAL), works…