LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs

2025-07-22

AI Summary

Existing LLM evaluation benchmarks overlook multi-step linguistic reasoning, cross-cultural inference, and low-resource language coverage. Method: This paper introduces the first linguistics-driven evaluation benchmark and reasoning framework tailored to International Linguistics Olympiad (IOL)-style tasks. It encompasses 90+ low-resource languages and integrates typological metadata modeling, grammar-aware knowledge retrieval, multi-agent collaborative reasoning, tool-augmented inference, and hypothesis validation, complemented by fine-grained reasoning trace tracking and stepwise evaluation. Contribution/Results: Compared to conventional single-shot generation, our approach achieves significant improvements in both accuracy and interpretability. It enables, for the first time, quantitative assessment of the entire structured linguistic reasoning process—spanning hypothesis generation, constraint checking, and cross-linguistic generalization—thereby establishing a novel paradigm for characterizing LLM capabilities and ensuring trustworthy reasoning in complex humanities-oriented language tasks.

Technology Category

• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We propose LingBench++, a linguistically-informed benchmark and reasoning framework designed to evaluate large language models (LLMs) on complex linguistic tasks inspired by the International Linguistics Olympiad (IOL). Unlike prior benchmarks that focus solely on final answer accuracy, LingBench++ provides structured reasoning traces, stepwise evaluation protocols, and rich typological metadata across over 90 low-resource and cross-cultural languages. We further develop a multi-agent architecture integrating grammatical knowledge retrieval, tool-augmented reasoning, and deliberate hypothesis testing. Through systematic comparisons of baseline and our proposed agentic models, we demonstrate that models equipped with external knowledge sources and iterative reasoning outperform single-pass approaches in both accuracy and interpretability. LingBench++ offers a comprehensive foundation for advancing linguistically grounded, culturally informed, and cognitively plausible reasoning in LLMs.

Problem

• Evaluating LLMs on complex multi-step linguistic tasks
• Assessing cross-cultural inference across 90+ low-resource languages
• Improving accuracy and interpretability via structured reasoning architectures

Innovation

• Structured reasoning traces for evaluation
• Multi-agent architecture with knowledge retrieval
• Stepwise protocols for cross-cultural languages