71 citations · 186 across the 38 of their papers we have counts for
8 papers · 1 filter
vTune: Verifiable Fine-Tuning for LLMs Through Backdooring
Eva Zhang, Arka Pal, Akilesh Potti +1
As fine-tuning large language models (LLMs) becomes increasingly prevalent, users often rely on third-party services with limited visibility into their fine-tuning processes. This…
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices
Andres Potapczynski, Shikai Qiu, Marc Finzi +6
Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts focused on a sm…
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking
Benjamin Feuer, Micah Goldblum, Teresa Datta +5
The release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior…
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi, Yilun Kuang, Brandon Amos +3
Large language models (LLMs) with billions of parameters excel at predicting the next token in a sequence. Recent work computes non-vacuous compression-based generalization bounds…
Compute Better Spent: Replacing Dense Layers with Structured Matrices
Shikai Qiu, Andres Potapczynski, Marc Finzi +2
Dense linear layers are the dominant computational bottleneck in foundation models. Identifying more efficient alternatives to dense matrices has enormous potential for building mo…
Large Language Models Must Be Taught to Know What They Don't Know
Sanyam Kapoor, Nate Gruver, Manley Roberts +7
When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is s…
Adaptive Retention & Correction: Test-Time Training for Continual Learning
Haoran Chen, Micah Goldblum, Zuxuan Wu +1
Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common proble…
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova +5
While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to…