mlsanityData to 7 Oct 2026

Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process

arXiv 2402.10699 ↗First posted 16 Feb 2024cs.CLcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation has mainly focused on better utilizing training data, demonstrations, or pre-defined and universal knowledge to improve performance, with a lack of consideration of decision-making like human translators. In this paper, we incorporate Thinker with the Drift-Diffusion Model (Thinker-DDM) to address this issue. We then redefine the Drift-Diffusion process to emulate human translators' dynamic decision-making under constrained resources. We conduct extensive experiments under the high-resource, low-resource, and commonsense translation settings using the WMT22 and CommonMT datasets, in which Thinker-DDM outperforms baselines in the first two scenarios. We also perform additional analysis and evaluation on commonsense translation to illustrate the high effectiveness and efficacy of the proposed method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 26 of its 59 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

26 of 59 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023LLaMA: Open and Efficient Foundation Language Models27 Feb

Other references · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2023Mistral 7Bcited by 3,065
  7. 2023GPTScore: Evaluate as You Desirecited by 357
  8. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  9. 2023Multilingual Machine Translation with Large Language Models: Empirical Results and Analysiscited by 156
  10. 2023System 2 Attention (is something you might need too)cited by 85
  11. 2023Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translationcited by 74
  12. 2023Humanoid Agents: Platform for Simulating Human-like Generative Agentscited by 73
13 more
  1. 2023Extrapolating Large Language Models to Non-English by Aligning Languagescited by 69
  2. 2023BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languagescited by 60
  3. 2023LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordinationcited by 48
  4. 2023Exploring Human-Like Translation Strategy with Large Language Modelscited by 44
  5. 2023BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Modelscited by 42
  6. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  7. 2023Fine-tuning Large Language Models for Adaptive Machine Translationcited by 21
  8. 2023Chain-of-Dictionary Prompting Elicits Translation in Large Language Modelscited by 16
  9. 2023DOMINO: A Dual-System for Multi-step Visual Language Reasoningcited by 12
  10. 2023Generating Valid and Natural Adversarial Examples with Large Language Modelscited by 12
  11. 2023Neural Machine Translation Models Can Learn to be Few-shot Learnerscited by 8
  12. 2023DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinkingcited by 4
  13. 2024Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languagescited by 3

Built on by

Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Creative and Context-Aware Translation of East Asian Idioms with GPT-4cited by 10
  2. 2024Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detectioncited by 5
  3. 2024Domain-specific Guided Summarization for Mental Health Postscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation
  2. 2025Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
  3. 2026Diffusion Language Models: An Experimental Analysis
  4. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
  5. 2025New Trends for Modern Machine Translation with Large Reasoning Models
  6. 2025LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens
  7. 2026Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
  8. 2025Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation