Can cross-lingual methods reduce mistakes in low-resource languages?
Yes, but only if the method is designed to align reasoning across languages. The Cross-lingual Tree-of-Thoughts (Cross-ToT) method, introduced in 2023, uses a self-consistent prompting mechanism to generate multiple reasoning paths in different languages, then converges on the final answer. In experiments, it significantly outperformed existing prompting methods and reduced the number of interactions needed, meaning it not only got more answers right but also avoided the back-and-forth that often accompanies errors [2]. This suggests that by forcing the model to reason in multiple languages, it can catch and correct its own mistakes before committing to an answer.
However, the same paper notes that the root problem is the imbalance in pre-training data, which makes non-English reasoning inherently weaker. Cross-ToT mitigates this but doesn't eliminate it—so while mistakes are reduced, they aren't fully avoided.
Does explicitly correcting mistakes help avoid repeating them?
Yes, but only when there's a strong incentive. A 2016 study in a quantum mechanics course gave students identical problems on midterm and final exams. Half were offered up to 50% of lost points back if they corrected their midterm mistakes; the other half just saw the solutions. On the final, the incentivized group significantly outperformed the non-incentivized group, especially among students who had done poorly on the midterm [3]. This shows that simply knowing the correct answer isn't enough—you need active, motivated correction to prevent repeating errors.
This finding transfers to LLMs: if a model is prompted to explicitly revisit and correct its reasoning steps (as Cross-ToT does), it's more likely to avoid the same mistake later. But without that explicit step, errors tend to persist.
What are the limits of these approaches?
The biggest limit is that low-resource languages still suffer from data scarcity. Cross-ToT helps by aligning reasoning across languages, but it doesn't add new knowledge—it just uses the model's existing multilingual abilities more effectively [2]. So for very low-resource languages, the gains may be smaller.
Also, the error-correction benefit from [3] came from a human learning context, not an LLM. While the principle likely applies, it's not directly tested on LLMs. So we can't be sure that LLMs will benefit from explicit correction incentives in the same way humans do.
About These Sources
This answer is built on 3 studies (all preprints) — published from 2016 to 2023 — selected as the most relevant from 3 studies that passed quality screening, drawn from 51 papers retrieved from a database of over 500 million.
Sources used in this answer
ART: Automatic multi-step reasoning and tool-use for large language models
ART (Automatic Reasoning and Tool-use) automatically generates multi-step reasoning and tool calls, improving over few-shot prompting and automatic CoT on BigBench and MMLU, and matching hand-crafted CoT on most tasks—but it does not specifically address low-resource languages.
Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Cross-lingual Tree-of-Thoughts (Cross-ToT) aligns reasoning across languages, significantly outperforming existing prompting methods and reducing interactions, thereby reducing mistakes in multi-step reasoning for non-English languages.
Improving performance in quantum mechanics with explicit incentives to correct mistakes
In a four-year study of quantum mechanics students, those given explicit incentives to correct midterm mistakes (up to 50% of lost points) significantly outperformed those without on identical final exam problems, with the largest effect among lower-performing students.