Research Paper#Large Language Models (LLMs), Multilingual NLP, Reasoning Evaluation🔬 ResearchAnalyzed: Jan 3, 2026 19:42
Reasoning-Answer Misalignment in Multilingual LLMs
Analysis
This paper addresses a crucial gap in evaluating multilingual LLMs. It highlights that high accuracy doesn't guarantee sound reasoning, especially in non-Latin scripts. The human-validated framework and error taxonomy are valuable contributions, emphasizing the need for reasoning-aware evaluation.
Key Takeaways
- •LLMs can achieve high accuracy while exhibiting flawed reasoning.
- •Reasoning-answer misalignment is more prevalent in non-Latin scripts.
- •Evidential errors and illogical reasoning steps are primary causes of failure.
- •Current multilingual evaluation practices are insufficient for assessing reasoning.
Reference
“Reasoning traces in non-Latin scripts show at least twice as much misalignment between their reasoning and conclusions than those in Latin scripts.”