Bradley Love

Are you a graduate student interested in working at Los Alamos National Laboratory (LANL) this summer? LANL has student internships, apply here: lanl.jobs/search/jobde... Please apply ASAP and before February 13th (sorry for the rush) 1/2

"The inevitability and superfluousness of cell types in spatial cognition". Intuitive cell types are found in random artificial networks using the same selection criteria neuroscientists use with actual data. elifesciences.org/reviewed-pre... 1/2

Intuitive cell types don't necessarily play the ascribed functional role in the overall computation. This is not a message the field wants to hear as it suggests better baselines, controls, and some reflection. elifesciences.org/reviewed-pre... 2/2

Exciting "new" work illustrating our broken publishing system. Seb presented this work online at neuromatch 2.0 at the height of the pandemic. Then, Xin-Ya worked years on addressing reviewer comments, which added some rigor but didn't change the message. 1/2

When AI surpasses human performance, what's left for humans? We find that human judgment boosts performance of human-AI teams because humans and machines make different errors. cell.com/patterns/ful... 1/2

Some UK dude is trying to extort me, demanding money to not spread made-up stories. I reported to the poilice after getting flooded with phone messages I never listen to, etc. @bsky.app has been good about deleting his posts and accounts. If contacted, don't interact, but instead report to...1/2

"Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies" Oddly, we prove LLMs should be equivalent for any word ordering: forward, backward, scrambled. In practice, LLMs diverge from one another. Why? 1/2 arxiv.org/abs/2505.08739

When LLMs diverge from one another because of word order (data factorization), it indicates their probability distributions are inconsistent, which is a red flag (not trustworthy). We trace deviations to self-attention positional and locality biases. 2/2 arxiv.org/abs/2505.08739

"Coordinating multiple mental faculties during learning" There's lots of good work in object recognition and learning, but how do we integrate the two? Here's a proposal and model that is more interactive than perception provides the inputs to cognition. www.nature.com/articles/s41...

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. nature.com/articles/s41... 1/8

Finally, LLMs can be augmented with neuroscience knowledge for better performance. We tuned Mistral on 20 years of the neuroscience literature using LoRA. The tuned model, which we refer to as BrainGPT, performed better on BrainBench. 7/8

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. nature.com/articles/s41... 1/8

In the Nature HB paper, both human experts and LLMs were well calibrated - when they were more certain of their decisions, they were more likely to be correct. Calibration is beneficial for human-machine teaming. 5/8

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. nature.com/articles/s41... 1/8

All 15 LLMs considered crushed human experts at BrainBench's predictive task. LLMs correctly predicted neuroscience results (across all sub areas) dramatically better than human experts, including those with decades of experience. 3/8

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. nature.com/articles/s41... 1/8

"Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific Text" Our take is that large language models (LLMs) are neither stochastic parrots nor faithful models of human language processing. arxiv.org/abs/2411.11061 1/2

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. arxiv.org/abs/2403.03230 1/6

LLMs can be augmented with neuroscience knowledge for better performance. We tuned Llama-2-7b (chat) on 20 years of the neuroscience literature using LoRA. The tuned model, which we refer to as BrainGPT, performed better on BrainBench. 5/6

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. arxiv.org/abs/2403.03230 1/6

All 15 LLMs considered crushed human experts at BrainBench's predictive task. LLMs correctly predicted neuroscience results (across all sub areas) dramatically better than human experts, including those with decades of experience. 3/6

"Large language models surpass human experts in predicting neuroscience results" w @ken-lxl.bsky.social and braingpt.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. arxiv.org/abs/2403.03230 1/6