Compact Example-Based Explanations for Language Models
Loris Schoenegger, Benjamin Roth · Jan 7, 2026 · Citations: 0
How to use this paper page
Coverage: RecentUse this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.
Best use
Background context only
Metadata: RecentTrust level
Low
Signals: RecentWhat still needs checking
Extraction flags indicate low-signal or possible false-positive protocol mapping.
Signal confidence: 0.35
Abstract
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 explanations. As humans cannot interpret thousands of documents, only a small subset of the training data can be presented as an explanation. Although the choice of which documents to include directly affects explanation quality, previous evaluations of such systems have largely ignored any selection strategies. To address this, we propose a novel selection relevance score, a retraining-free metric that quantifies how useful a set of examples is for explaining a model's output. We validate this score through fine-tuning experiments, confirming that it can predict whether a set of examples supports or undermines the model's predictions. Using this metric, we further show that common selection strategies often underperform random selection. Motivated by this finding, we propose a strategy that balances influence and representativeness, enabling better use of selection budgets than naively selecting the highest-ranking examples.