35 citations · 38 across the 10 of their papers we have counts for
11 papers
CLIP is All You Need for Human-like Semantic Representations in Stable Diffusion
Cameron Braunstein, Mariya Toneva, Eddy Ilg
Latent diffusion models such as Stable Diffusion achieve state-of-the-art results on text-to-image generation tasks. However, the extent to which these models have a semantic under…
The One Where They Brain-Tune for Social Cognition: Multi-Modal Brain-Tuning on Friends
Nico Policzer, Cameron Braunstein, Mariya Toneva
Recent studies on audio models show brain-tuning - fine-tuning models to better predict corresponding fMRI activity - improves brain alignment and increases performance on downstre…
Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models
Omer Moussa, Mariya Toneva
Pretrained language models are remarkably effective in aligning with human brain responses elicited by natural language stimuli, positioning them as promising model organisms for s…
Large Language Models as Model Organisms for Human Associative Learning
Camila Kolling, Vy Ai Vo, Mariya Toneva
Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Testing hypotheses on how rep…
How do Humans and LLMs Process Confusing Code?
Youssef Abdelsalam, Norman Peitek, Anna-Maria Maurer +2
Already today, humans and programming assistants based on large language models (LLMs) collaborate in everyday programming tasks. Clearly, a misalignment between how LLMs and progr…
Positional Biases Shift as Inputs Approach Context Window Limits
Blerta Veseli, Julian Chibane, Mariya Toneva +1
Large Language Models (LLMs) often struggle to use information across long inputs effectively. Prior work has identified positional biases, such as the Lost in the Middle (LiM) eff…
Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents
Mathis Pink, Qinyuan Wu, Vy Ai Vo +4
As Large Language Models (LLMs) evolve from text-completion tools into fully fledged agents operating in dynamic environments, they must address the challenge of continually learni…
Hints Help Finding and Fixing Bugs Differently in Python and Text-based Program Representations
Ruchit Rawal, Victor-Alexandru Pădurean, Sven Apel +2
With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore--programming tasks can also be e…
SLayR: Scene Layout Generation with Rectified Flow
Cameron Braunstein, Hevra Petekkaya, Jan Eric Lenssen +2
We introduce SLayR, Scene Layout Generation with Rectified flow, a novel transformer-based model for text-to-layout generation which can then be paired with existing layout-to-imag…
Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks
Mathis Pink, Vy A. Vo, Qinyuan Wu +7
Current LLM benchmarks focus on evaluating models' memory of facts and semantic relations, primarily assessing semantic aspects of long-term memory. However, in humans, long-term m…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…