  By training on text in various languages, large language models (LLMs)
typically possess multilingual support and demonstrate remarkable capabilities
in solving tasks described in different languages. However, LLMs can exhibit
linguistic discrimination due to the uneven distribution of training data
across languages. That is, LLMs are hard to keep the consistency of responses
when faced with the same task but depicted in different languages.
  In this study, we first explore the consistency in the LLMs' outputs
responding to queries in various languages from two aspects: safety and
quality. We conduct this analysis with two datasets (AdvBench and NQ) based on
four LLMs (Llama2-13b, Gemma-7b, GPT-3.5-turbo and Gemini-pro). The results
show that LLMs exhibit stronger human alignment capabilities with queries in
English, French, Russian, and Spanish (only 1.04\% of harmful queries
successfully jailbreak on average) compared to queries in Bengali, Georgian,
Nepali and Maithili (27.7\% of harmful queries jailbreak successfully on
average). Moreover, for queries in English, Danish, Czech and Slovenian, LLMs
tend to produce responses with a higher quality (with 0.1494 $F_1$ score on
average) compared to the other languages. Upon these findings, we propose
LDFighter, a similarity-based voting, to mitigate the linguistic discrimination
in LLMs. LDFighter ensures consistent service for different language speakers.
We evaluate LDFighter with both benign queries and harmful queries. The results
show that LDFighter not only significantly reduces the jailbreak success rate
but also improve the response quality on average, demonstrating its
effectiveness.
