# Compact Example-Based Explanations for Language Models

arXiv: https://arxiv.org/abs/2601.03786 · HTML: https://mlsanity.com/paper/2601.03786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://mlsanity.com/paper/2402.04333/) (2024)
- [Studying Large Language Model Generalization with Influence Functions](https://mlsanity.com/paper/2308.03296/) (2023)
- [TRAK: Attributing Model Behavior at Scale](https://mlsanity.com/paper/2303.14186/) (2023)
- [Influence Functions in Deep Learning Are Fragile](https://mlsanity.com/paper/2006.14651/) (2020)
- [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018)
- [OLMES: A Standard for Language Model Evaluations](https://mlsanity.com/paper/2406.08446/) (2024)
- [Revisiting Methods for Finding Influential Examples](https://mlsanity.com/paper/2111.04683/) (2021)
- [apricot: Submodular selection for data summarization in Python](https://mlsanity.com/paper/1906.03543/) (2019)
- [On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation](https://mlsanity.com/paper/2106.04753/) (2021)
- [DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement](https://mlsanity.com/paper/2107.05978/) (2021)
- [AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations](https://mlsanity.com/paper/2407.16010/) (2024)
- [The Susceptibility of Example-Based Explainability Methods to Class Outliers](https://mlsanity.com/paper/2407.20678/) (2024)

## Built on by (most cited first, 1 of 1)

- [Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations](https://mlsanity.com/paper/2601.16651/) (2026)

## Abstract (arXiv)

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.