arXiv AI
Uncovering the Computational Ingredients of Human-Like Representations in LLMs
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arXiv AI
Jul 21
What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
arXiv Machine Learning
Jun 26
The Geometry of Updates: Fisher Alignment at Vocabulary Scale
arXiv:2606. 27242v1 Announce Type: new Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction targets.
arXiv AI
Aug 25
From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learning
The paper introduces Align‑LoRA, a unified LoRA framework for multi‑task learning that replaces complex, isolated adapter designs with a single‑adapter model enhanced by a higher rank and an explicit alignment loss. It demonstrates that a router‑free, multi‑head model with high inter‑head redundancy can outperform more elaborate baselines, and that a unified LoRA can achieve competitive performance while enabling weight merging and zero inference latency. Extensive experiments and theoretical analysis confirm that Align‑LoRA surpasses prevailing approaches, offering a simpler, production‑friendly paradigm for parameter‑efficient fine‑tuning of large language models.
arXiv AI
Aug 11
Large Language Models Align with the Human Brain during Creative Thinking
arXiv:2604. 03480v2 Announce Type: replace-cross Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine.
arXiv AI
Aug 14
Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
arXiv:2608. 13069v1 Announce Type: new Abstract: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants.
arXiv AI
Aug 11
In-context superposition: human-like working memory interference in large language models
arXiv:2604. 09670v2 Announce Type: replace-cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals.