In this article, the authors discuss the evaluation of large language models (LLMs) on their linguistic reasoning capabilities, specifically in the context of abstract multilingual reasoning. The goal is to understand the limitations and gaps in these models’ skills when it comes to performing complex linguistic tasks in low-resource languages.
The authors propose a two-stage procedure to address this evaluation. The first stage involves generating analogical exemplars using a language model. Analogical reasoning is a critical aspect of human cognition, so exploring its application in language models is valuable. The generated exemplars are then used in-context along with target language exemplars to perform the reasoning tasks.
The results of their experiments on the modeLing dataset show that analogical prompting is effective in improving the models’ performance on abstract multilingual reasoning tasks. Specifically, GPT-4o’s performance improved by 8.1% and Llama-3.1-405B-Instruct’s performance improved by 5.9% over chain-of-thought approaches. These gains can be attributed to the analogical demonstrations, whether they are self-generated or produced by weaker multilingual models.
Furthermore, the authors demonstrate that their method generalizes well to other tasks present in Linguistics Olympiad competitions. They achieved sizable improvements across all problem types and difficulty levels included in the LINGOLY dataset with GPT-4o. This suggests that the proposed approach is not only effective for abstract linguistic reasoning but also applicable to a wide range of linguistic problem-solving tasks.
The authors also highlight several interesting phenomena that drive linguistic reasoning performance, which they discovered during their experiments. These findings indicate that linguistic puzzles, like the ones used in this study, can serve as valuable benchmarks for evaluating and advancing reasoning methods in language models.
Overall, this work provides valuable insights into the abilities and limitations of large language models when it comes to abstract multilingual reasoning. The proposed two-stage procedure with analogical prompting shows promising results in improving the models’ performance. Future research can build upon these findings to further enhance the reasoning capabilities of language models and address the identified gaps and limitations.