Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting

2026-03-17 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, quick

Summary

A new framework, "Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting," addresses the challenge of slang interpretation for Large Language Models (LLMs). The research, submitted on January 15, 2026, investigates why LLMs struggle with slang due to its contextual, cultural, and linguistic embeddedness, especially without domain-specific training. Empirical studies reveal that LLM model size and temperature settings have limited impact on slang inference accuracy, with larger Transformer-based models not outperforming smaller ones. Based on these findings, the framework integrates greedy search algorithms with chain-of-thought prompting, specifically for small language models, to improve slang meaning interpretation accuracy. This approach offers a practical solution for enhancing slang comprehension through structured reasoning.

Key takeaway

For AI scientists and Machine Learning Engineers developing language models, recognize that simply scaling up model size or adjusting temperature will not significantly improve slang interpretation. Instead, focus your efforts on implementing structured reasoning frameworks like greedy search-guided chain-of-thought prompting. This approach offers a more effective pathway to enhance your models' ability to accurately understand context-dependent slang, particularly when working with smaller language models.

Key insights

Slang interpretation in LLMs benefits more from structured prompting than from increased model size or temperature.

Principles

• Slang interpretation is context-dependent.
• Larger LLMs do not guarantee better slang accuracy.

Method

The proposed framework combines greedy search algorithms with chain-of-thought prompting to enhance slang interpretation accuracy in small language models.

In practice

• Apply greedy search with CoT for slang tasks.
• Prioritize prompting strategies over model scaling.

Topics

• Slang Interpretation
• Large Language Models
• Chain-of-Thought Prompting
• Greedy Search
• Natural Language Processing

Best for: Machine Learning Engineer, AI Scientist, Research Scientist, AI Researcher, AI Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.