Summary of Natlan: Native Language Prompting Facilitates Knowledge Elicitation Through Language Trigger Provision and Domain Trigger Retention, by Baixuan Li et al.
NatLan: Native Language Prompting Facilitates Knowledge Elicitation Through Language Trigger Provision and Domain Trigger Retention
by Baixuan Li, Yunlong Fan, Tianyi Ma, Zhiqiang Gao
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the limitations of multilingual large language models (MLLMs) when answering questions in non-dominant languages. Current translate-then-answer methods alleviate this issue, but the underlying mechanisms are unclear. The study analogizes the dominant language of MLLMs to the native language of humans and uses two human cognitive features: Language Trigger (LT) and Domain Trigger (DT), to interpret these mechanisms. This reveals that while LTs are sufficient, there is a deficiency in DT retention. To mitigate this issue, the paper proposes Native Language Prompting (NatLan), employing a Multi-MLLM collaboration strategy and introducing an additional role-enhanced domain-specific MLLM with stronger multilingual understanding capabilities as the translator. The proposed method achieves up to a 31.28% improvement in accuracy across five language QA benchmarks, providing comparable or greater retention of DTs in up to 87% of cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models can answer questions when speaking languages that aren’t their own. Right now, there are ways to fix this by translating the question first, but it’s not clear why these methods work. The study uses ideas from human thinking, like language and domain triggers, to understand what’s going on. It shows that while language triggers are okay, domain triggers are still missing. To solve this problem, the paper proposes a new way called Native Language Prompting (NatLan). This method works by having multiple models work together and using one model as a translator with special skills for understanding different languages. NatLan does better than current best methods on tests, getting questions right up to 31.28% more often and keeping domain triggers in place up to 87% of the time. |
Keywords
» Artificial intelligence » Prompting
