# XFL: Naming Functions in Binaries with Extreme Multi-label Learning

arXiv: https://arxiv.org/abs/2107.13404 · HTML: https://mlsanity.com/paper/2107.13404/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 73 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [Generative Code Modeling with Graphs](https://mlsanity.com/paper/1805.08490/) (2018)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [Structured Neural Summarization](https://mlsanity.com/paper/1811.01824/) (2018)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [SAFE: Self-Attentive Function Embeddings for Binary Similarity](https://mlsanity.com/paper/1811.05296/) (2018)
- [A General Path-Based Representation for Predicting Program Properties](https://mlsanity.com/paper/1803.09544/) (2018)
- [Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs](https://mlsanity.com/paper/1808.04706/) (2018)
- [An Observational Investigation of Reverse Engineers' Processes](https://mlsanity.com/paper/1912.00317/) (2019)
- [Multi-Label Adversarial Perturbations](https://mlsanity.com/paper/1901.00546/) (2019)
- [A simple yet effective baseline for non-attributed graph classification](https://mlsanity.com/paper/1811.03508/) (2018)
- [Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned](https://mlsanity.com/paper/2011.10749/) (2020)
- [Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation](https://mlsanity.com/paper/1808.08627/) (2018)

## Built on by (most cited first, 5 of 5)

- [Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models](https://mlsanity.com/paper/2312.09601/) (2023)
- [BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding](https://mlsanity.com/paper/2409.07889/) (2024)
- [AGNOMIN -- Architecture Agnostic Multi-Label Function Name Prediction](https://mlsanity.com/paper/2509.25514/) (2025)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)
- [Reveree: Diagnosing LLM Reverse-Engineering Agents](https://mlsanity.com/paper/2609.01185/) (2026)

## Abstract (arXiv)

Reverse engineers benefit from the presence of identifiers such as function names in a binary, but usually these are removed for release. Training a machine learning model to predict function names automatically is promising but fundamentally hard: unlike words in natural language, most function names occur only once. In this paper, we address this problem by introducing eXtreme Function Labeling (XFL), an extreme multi-label learning approach to selecting appropriate labels for binary functions. XFL splits function names into tokens, treating each as an informative label akin to the problem of tagging texts in natural language. We relate the semantics of binary code to labels through DEXTER, a novel function embedding that combines static analysis-based features with local context from the call graph and global context from the entire binary. We demonstrate that XFL/DEXTER outperforms the state of the art in function labeling on a dataset of 10,047 binaries from the Debian project, achieving a precision of 83.5%. We also study combinations of XFL with alternative binary embeddings from the literature and show that DEXTER consistently performs best for this task. As a result, we demonstrate that binary function labeling can be effectively phrased in terms of multi-label learning, and that binary function embeddings benefit from including explicit semantic features.