From the 2 of 13 linked papers with an AI index.
12 papers
Data-Efficient Adaptation of LLMs via Attention Head Reweighting
Tuomas Oikarinen, Zixiao Chen, Charlotte Siska +3
The paper introduces Attention Head Reweighting (AHR), a method that adapts large language models to new text‑classification tasks by learning a single scalar weight per attention…
Test-Time Learning with an Evolving Library
Weijia Xu, Alessandro Sordoni, Chandan Singh +4
The paper introduces EvoLib, a test-time learning framework that lets large language models build, reuse, and evolve a shared library of knowledge abstractions across tasks without…
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
Richard Antonello, Chandan Singh, Shailee Jain +5
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is uncl…
Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks
Vamshi Sunku Mohan, Kaustubh Gupta, Aneesha Das +1
State-space models (SSMs) have emerged as an efficient strategy for building powerful language models, avoiding the quadratic complexity of computing attention in transformers. Des…
Sanity Checks for Agentic Data Science
Zachary T. Rewolinski, Austin V. Zane, Hao Huang +4
Agentic data science (ADS) pipelines have grown rapidly in both capability and adoption, with systems such as OpenAI Codex now able to directly analyze datasets and produce answers…
Test-time Recursive Thinking: Self-Improvement without External Feedback
Yufan Zhuang, Chandan Singh, Liyuan Liu +5
Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…