5 citations · 18 across the 14 of their papers we have counts for
15 papers
Annotations Mitigate Post-Training Mode Collapse
Jacob Mitchell Springer, Madhu Advani, Lukas Aichberger +7
Post-training (via supervised fine-tuning) improves instruction-following, but often induces semantic mode collapse by biasing models toward low-entropy fine-tuning data at the exp…
Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
Ishaan Watts, Catherine Li, Sachin Goyal +2
Pretraining optimizers are tuned to produce the strongest possible base model, on the assumption that a stronger starting point yields a stronger model after subsequent changes lik…
Early Data Exposure Improves Robustness to Subsequent Fine-Tuning
Lawrence Feng, Gaurav R. Ghosal, Jacob Mitchell Springer +2
How can we train models whose post-trained capabilities survive subsequent fine-tuning? Rather than focusing on downstream interventions to mitigate forgetting of upstream capabili…
Disentangling Geometry, Performance, and Training in Language Models
Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian +1
Geometric properties of Transformer weights, particularly the unembedding matrix, have been widely useful in language model interpretability research. Yet, their utility for estima…
Understanding the Influence of Synthetic Data for Text Embedders
Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy +2
Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLM-generated data. Nonetheless, no publicly available…
Overtrained Language Models Are Harder to Fine-Tune
Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen +5
Large language models are pre-trained on ever-growing token budgets under the assumption that better pre-training performance translates to improved downstream models. In this work…
Mitigating Bias in RAG: Controlling the Embedder
Taeyoun Kim, Jacob Springer, Aditi Raghunathan +1
In retrieval augmented generation (RAG) systems, each individual component -- the LLM, embedder, and corpus -- could introduce biases in the form of skews towards outputting certai…
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning
Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
Sharpness-Aware Minimization (SAM) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD). The originally-proposed motivation behind SAM was to bias…
Repetition Improves Language Model Embeddings
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried +2
Bidirectional models are considered essential for strong text embeddings. Recent approaches to adapt autoregressive language models (LMs) into strong text embedding models have lar…
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
We lack a systematic understanding of the effects of fine-tuning (via methods such as instruction-tuning or reinforcement learning from human feedback), particularly on tasks outsi…
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Adversarial examples for neural network image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as wel…
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Neural networks trained on visual data are well-known to be vulnerable to often imperceptible adversarial perturbations. The reasons for this vulnerability are still being debated…
It's Hard for Neural Networks To Learn the Game of Life
Jacob M. Springer, Garrett T. Kenyon
Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however…
STRATA: Simple, Gradient-Free Attacks for Models of Code
Jacob M. Springer, Bryn Marie Reinstadler, Una-May O'Reilly
Neural networks are well-known to be vulnerable to imperceptible perturbations in the input, called adversarial examples, that result in misclassification. Generating adversarial e…
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Jacob M. Springer, Charles S. Strauss, Austin M. Thresher +2
Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drasti…