# Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models

arXiv: https://arxiv.org/abs/2411.01141 · HTML: https://mlsanity.com/paper/2411.01141/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://mlsanity.com/paper/2212.10001/) (2022)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting](https://mlsanity.com/paper/2305.07004/) (2023)
- [Self-Edit: Fault-Aware Code Editor for Code Generation](https://mlsanity.com/paper/2305.04087/) (2023)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages](https://mlsanity.com/paper/2407.05975/) (2024)
- [Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages](https://mlsanity.com/paper/1909.09524/) (2019)
- [Unveiling the Generalization Power of Fine-Tuned Large Language Models](https://mlsanity.com/paper/2403.09162/) (2024)
- [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://mlsanity.com/paper/1606.02006/) (2016)
- [Using natural language prompts for machine translation](https://mlsanity.com/paper/2202.11822/) (2022)
- [Bridging Neural Machine Translation and Bilingual Dictionaries](https://mlsanity.com/paper/1610.07272/) (2016)
- [PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation](https://mlsanity.com/paper/2208.08110/) (2022)
- [Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References](https://mlsanity.com/paper/2305.15067/) (2023)
- [Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations](https://mlsanity.com/paper/2407.04093/) (2024)

## Built on by (most cited first, 4 of 4)

- [SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models](https://mlsanity.com/paper/2510.02648/) (2025)
- [x1: Learning to Think Adaptively Across Languages and Cultures](https://mlsanity.com/paper/2604.16917/) (2026)
- [Large Language Models for Low-Resource Languages: A Conceptual Framework for an Electronic Explanatory Dictionary of the Tajik Language](https://mlsanity.com/paper/2608.04186/) (2026)
- [Rewrite What Matters: Adaptive Multilingual Query Rewriting for Reasoning via Agentic Reinforcement Learning](https://mlsanity.com/paper/2610.04899/) (2026)

## Abstract (arXiv)

There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-centric and performance is limited on multilingual reasoning. The second is the place of external knowledge to be used, where most retrieved knowledge is prepended to the user queries (maybe sub-optimal). This paper presents a novel and simple yet effective method called Dictionary Insertion Prompting (DIP). When providing a non-English prompt, DIP looks up a word dictionary and inserts words' English counterparts into the middle of the prompt for LLMs. It then enables better translation into English and better English model thinking steps which leads to obviously better results. We experiment with 10 to 200 languages from FLORES-200.footnoteThe number of languages varies on the datasets, and we experiment with 200 languages on GSM8K as in Appendix Since there are no adequate datasets, we use the NLLB translator to create synthetic multilingual benchmarks from the existing 4 English reasoning benchmarks such as GSM8K and AQuA. The synthetic benchmarks are translated back into English for quality assurance with manual annotation. Interestingly, the place for injecting the dictionary plays an important factor in the performance gains, and we found that interleaving the dictionary with the original words gives a better performance compared to prepending/appending the dictionary, under the same dictionary constructed.