# Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries

arXiv: https://arxiv.org/abs/2601.01842 · HTML: https://mlsanity.com/paper/2601.01842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models](https://mlsanity.com/paper/2405.01535/) (2024)
- [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://mlsanity.com/paper/2402.14207/) (2024)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [Evaluating a Multi-sense Definition Generation Model for Multiple Languages](https://mlsanity.com/paper/2006.07398/) (2020)
- [Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs](https://mlsanity.com/paper/2408.04217/) (2024)
- [Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?](https://mlsanity.com/paper/2410.03240/) (2024)
- [Low-Cost Generation and Evaluation of Dictionary Example Sentences](https://mlsanity.com/paper/2404.06224/) (2024)
- [Difficult for Whom? A Study of Japanese Lexical Complexity](https://mlsanity.com/paper/2410.18567/) (2024)

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

- [Saying, Not Knowing: Aggressively GGUF-Quantized Small Language Models Still Write Rare Words They Can No Longer Define](https://mlsanity.com/paper/2610.04403/) (2026)

## Abstract (arXiv)

Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly. We thus study dictionary definition generation (DDG), i.e., the generation of non-contextualized definitions for given headwords. Specifically, we address learner's dictionary definition generation (LDDG), where definitions should be written using simple vocabulary. First, we introduce a reliable evaluation approach for DDG, based on newly proposed evaluation criteria and powered by an LLM-as-a-judge. To provide reference definitions for the evaluation, we construct a dataset of Japanese dictionary definitions in collaboration with a professional lexicographer. Validation results demonstrate that our evaluation approach agrees with human annotators at a level comparable to inter-annotator agreement. Second, we propose an LLM-based LDDG approach that employs iterative simplification. Experimental results show that our approach yields definitions that achieve high scores on the proposed criteria and exhibit high lexical simplicity.