# Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting

arXiv: https://arxiv.org/abs/1908.06288 · HTML: https://mlsanity.com/paper/1908.06288/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding](https://mlsanity.com/paper/2403.00425/) (2024)
- [Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation](https://mlsanity.com/paper/2108.04718/) (2021)
- [Machine Translation Decoding beyond Beam Search](https://mlsanity.com/paper/2104.05336/) (2021)
- [Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2211.07634/) (2022)
- [What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability](https://mlsanity.com/paper/2305.11707/) (2023)
- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [GLUECons: A Generic Benchmark for Learning Under Constraints](https://mlsanity.com/paper/2302.10914/) (2023)
- [Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2410.15021/) (2024)
- [Incremental Beam Manipulation for Natural Language Generation](https://mlsanity.com/paper/2102.02574/) (2021)
- [Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy](https://mlsanity.com/paper/2109.05312/) (2021)
- [Reranking Laws for Language Generation: A Communication-Theoretic Perspective](https://mlsanity.com/paper/2409.07131/) (2024)
- [Theoretical Guarantees for Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2502.12685/) (2025)
- [RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation](https://mlsanity.com/paper/2203.00201/) (2022)
- [On the True Distribution Approximation of Minimum Bayes-Risk Decoding](https://mlsanity.com/paper/2404.00752/) (2024)
- [Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs](https://mlsanity.com/paper/2510.03527/) (2025)

## Abstract (arXiv)

We propose a method for natural language generation, choosing the most representative output rather than the most likely output. By viewing the language generation process from the voting theory perspective, we define representativeness using range voting and a similarity measure. The proposed method can be applied when generating from any probabilistic language model, including n-gram models and neural network models. We evaluate different similarity measures on an image captioning task and a machine translation task, and show that our method generates longer and more diverse sentences, providing a solution to the common problem of short outputs being preferred over longer and more informative ones. The generated sentences obtain higher BLEU scores, particularly when the beam size is large. We also perform a human evaluation on both tasks and find that the outputs generated using our method are rated higher.