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Probing Large Language Models in Reasoning and Translating Complex Linguistic Puzzles

Zheng-Lin Lin, Yu-Fei Shih, Shu-Kai Hsieh

TL;DR

The paper investigates the use of Large Language Models (LLMs) to solve complex linguistic puzzles that demand both reasoning and translation. It systematically compares prompting strategies—Input-Output Prompting (IO), Chain-of-Thought (CoT), and Solo Performance Prompting (SPP)—using GPT-4 0603 across the Puzzling Machine Challenge and Linguistics Olympiad datasets, with a two-phase reasoning framework. Across datasets, IO generally outperforms CoT and SPP, while CoT can improve certain translation metrics and SPP shows variable gains depending on the metric. The work contributes practical insights into prompting LLMs for linguistic reasoning and translation tasks, informing NLP applications that rely on reasoning-based language tasks and multilingual translation.

Abstract

This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles, a domain requiring advanced reasoning and adept translation capabilities akin to human cognitive processes. We explore specific prompting techniques designed to enhance ability of LLMs to reason and elucidate their decision-making pathways, with a focus on Input-Output Prompting (IO), Chain-of-Thought Prompting (CoT), and Solo Performance Prompting (SPP). Utilizing datasets from the Puzzling Machine Competition and various Linguistics Olympiads, we employ a comprehensive set of metrics to assess the performance of GPT-4 0603, a prominent LLM, across these prompting methods. Our findings illuminate the potential of LLMs in linguistic reasoning and complex translation tasks, highlighting their capabilities and identifying limitations in the context of linguistic puzzles. This research contributes significantly to the broader field of Natural Language Processing (NLP) by providing insights into the optimization of LLM applications for improved reasoning and translation accuracy, thereby enriching the ongoing dialogue in NLP advancements.

Probing Large Language Models in Reasoning and Translating Complex Linguistic Puzzles

TL;DR

The paper investigates the use of Large Language Models (LLMs) to solve complex linguistic puzzles that demand both reasoning and translation. It systematically compares prompting strategies—Input-Output Prompting (IO), Chain-of-Thought (CoT), and Solo Performance Prompting (SPP)—using GPT-4 0603 across the Puzzling Machine Challenge and Linguistics Olympiad datasets, with a two-phase reasoning framework. Across datasets, IO generally outperforms CoT and SPP, while CoT can improve certain translation metrics and SPP shows variable gains depending on the metric. The work contributes practical insights into prompting LLMs for linguistic reasoning and translation tasks, informing NLP applications that rely on reasoning-based language tasks and multilingual translation.

Abstract

This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles, a domain requiring advanced reasoning and adept translation capabilities akin to human cognitive processes. We explore specific prompting techniques designed to enhance ability of LLMs to reason and elucidate their decision-making pathways, with a focus on Input-Output Prompting (IO), Chain-of-Thought Prompting (CoT), and Solo Performance Prompting (SPP). Utilizing datasets from the Puzzling Machine Competition and various Linguistics Olympiads, we employ a comprehensive set of metrics to assess the performance of GPT-4 0603, a prominent LLM, across these prompting methods. Our findings illuminate the potential of LLMs in linguistic reasoning and complex translation tasks, highlighting their capabilities and identifying limitations in the context of linguistic puzzles. This research contributes significantly to the broader field of Natural Language Processing (NLP) by providing insights into the optimization of LLM applications for improved reasoning and translation accuracy, thereby enriching the ongoing dialogue in NLP advancements.

Paper Structure

This paper contains 13 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Experiments
  4. Dataset
  5. Prompting Methods
  6. Evaluation
  7. Result
  8. Performance Analysis Using the Linguistics Olympiad Dataset
  9. Analysis Based on the Puzzling Machine Competition Dataset
  10. Analysis of GPT-4's Linguistic Proficiency Using the Linguistic Olympics Dataset
  11. Discussion
  12. Conclusion
  13. References

Figures (5)

  • Figure 1: Illustration of Rule Contradiction in language Kabyle: GPT-4 breaches its own established rule, wherein 'gh' is designated to signify the first person past tense.
  • Figure 2: An example of dictionary contradiction within GPT-4's reasoning process using CoT Prompting on the Rosetta Stone Problem of Choctaw.
  • Figure 3: Problem format example of northern Algeria language, Kabyle, collected and refined from UKLO.
  • Figure 4: characTER score of zero example
  • Figure 5: The figure shows the baseless assumption occurring in the CoT discussion on Kiche language. Linguistic anthropologists first propose baseless vocabulary pairs and lexicographers reaffirm the opinion.