Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs

• URL: http://arxiv.org/abs/2512.16814v1
• Date: Thu, 18 Dec 2025 17:55:15 GMT
• Title: Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs
• Authors: William English, Dominic Simon, Sumit Kumar Jha, Rickard Ewetz,
• Abstract summary: We propose a framework for NL to TL translation called Grammar Forced Translation (GraFT)<n>GraFT reduces the complexity of both tasks by restricting the set of valid output tokens from the full vocabulary to only a handful in each step.<n>We evaluate the effectiveness of GraFT using the CW, GLTL, and Navi benchmarks.
• Score: 11.37102335479887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Translating natural language (NL) into a formal language such as temporal logic (TL) is integral for human communication with robots and autonomous systems. State-of-the-art approaches decompose the task into a lifting of atomic propositions (APs) phase and a translation phase. However, existing methods struggle with accurate lifting, the existence of co-references, and learning from limited data. In this paper, we propose a framework for NL to TL translation called Grammar Forced Translation (GraFT). The framework is based on the observation that previous work solves both the lifting and translation steps by letting a language model iteratively predict tokens from its full vocabulary. In contrast, GraFT reduces the complexity of both tasks by restricting the set of valid output tokens from the full vocabulary to only a handful in each step. The solution space reduction is obtained by exploiting the unique properties of each problem. We also provide a theoretical justification for why the solution space reduction leads to more efficient learning. We evaluate the effectiveness of GraFT using the CW, GLTL, and Navi benchmarks. Compared with state-of-the-art translation approaches, it can be observed that GraFT the end-to-end translation accuracy by 5.49% and out-of-domain translation accuracy by 14.06% on average.

Related papers

• Testing the Limits of Machine Translation from One Book [0.0]
  Current state-of-the-art models demonstrate capacity to leverage in-context learning to translate into previously unseen language contexts.<n>We focus on Kanuri, a language that, despite having substantial speaker population, has minimal digital resources.
  arXiv  Detail & Related papers  (2025-08-08T19:27:44Z)
• Lost in Literalism: How Supervised Training Shapes Translationese in LLMs [51.04435855143767]
  Large language models (LLMs) have achieved remarkable success in machine translation.<n>However, translationese, characterized by overly literal and unnatural translations, remains a persistent challenge.<n>We introduce methods to mitigate these biases, including polishing golden references and filtering unnatural training instances.
  arXiv  Detail & Related papers  (2025-03-06T12:14:45Z)
• TasTe: Teaching Large Language Models to Translate through Self-Reflection [82.83958470745381]
  Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. We propose the TasTe framework, which stands for translating through self-reflection. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-12T17:21:21Z)
• Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing [12.843274390224853]
  Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks. We show that they have yet to attain state-of-the-art performance in Neural Machine Translation. We propose adapting LLM's as Automatic Post-Editors (APE) rather than direct translators.
  arXiv  Detail & Related papers  (2023-10-23T12:22:15Z)
• Towards Effective Disambiguation for Machine Translation with Large Language Models [65.80775710657672]
  We study the capabilities of large language models to translate "ambiguous sentences" Experiments show that our methods can match or outperform state-of-the-art systems such as DeepL and NLLB in four out of five language directions.
  arXiv  Detail & Related papers  (2023-09-20T22:22:52Z)
• Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languages [0.34998703934432673]
  We build a single-decoder neural machine translation system for Dravidian-Dravidian multilingual translation. Our model achieves scores within 3 BLEU of large-scale pivot-based models when it is trained on 50% of the language directions.
  arXiv  Detail & Related papers  (2023-08-10T13:38:09Z)
• Rethinking Round-Trip Translation for Machine Translation Evaluation [44.83568796515321]
  We report the surprising finding that round-trip translation can be used for automatic evaluation without the references. We demonstrate the rectification is overdue as round-trip translation could benefit multiple machine translation evaluation tasks.
  arXiv  Detail & Related papers  (2022-09-15T15:06:20Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• Phrase-level Active Learning for Neural Machine Translation [107.28450614074002]
  We propose an active learning setting where we can spend a given budget on translating in-domain data. We select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods.
  arXiv  Detail & Related papers  (2021-06-21T19:20:42Z)
• Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation [71.70562795158625]
  Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU) Recent advent of end-to-end neural models, self-supervised via language modeling (LM), and their success on a wide range of LU tasks, questions this belief. We empirically investigate the usefulness of supervised parsing for semantic LU in the context of LM-pretrained transformer networks.
  arXiv  Detail & Related papers  (2020-08-15T21:03:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.