4 papers · 1 filter
Compact Example-Based Explanations for Language Models
Loris Schoenegger, Benjamin Roth
Training data influence estimation methods quantify the contribution of training documents to a model's output, making them a promising source of information for example-based expl…
Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations
Lukas Hinterleitner, Loris Schoenegger, Benjamin Roth
Gradient-based methods for instance-based explanation for large language models (LLMs) are hindered by the immense dimensionality of model gradients. In practice, influence estimat…
Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs
Yuxi Xia, Loris Schoenegger, Benjamin Roth
Large language models (LLMs) can increase users' perceived trust by verbalizing confidence in their outputs. However, prior work has shown that LLMs are often overconfident, making…
Influence-driven Curriculum Learning for Pre-training on Limited Data
Loris Schoenegger, Lukas Thoma, Terra Blevins +1
Curriculum learning, a training technique where data is presented to the model in order of example difficulty (e.g., from simpler to more complex documents), has shown limited succ…