Summary of Inductive Linguistic Reasoning with Large Language Models, by Raghav Ramji et al.
Inductive Linguistic Reasoning with Large Language Models
by Raghav Ramji, Keshav Ramji
First submitted to arxiv on: 9 Dec 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 A recent study investigates the linguistic reasoning capabilities of large language models (LLMs) on extremely low-resource languages through abstract multilingual reasoning tasks. The researchers examine whether diverse auxiliary demonstrations can be automatically induced from seed exemplars, using analogical prompting to elicit models’ knowledge of language grammar similarities. They employ a two-stage procedure, generating analogical exemplars with a language model and then applying them in-context along with provided target language exemplars. The results on the modeLing dataset show that analogical prompting is effective, boosting the performance of GPT-4o by 8.1% and Llama-3.1-405B-Instruct by 5.9%. The method generalizes to other tasks in Linguistics Olympiad competitions, achieving sizable improvements across all problem types and difficulty levels included in the LINGOLY dataset with GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at understanding languages. But can they really think about words and grammar like humans do? Scientists tested these models on very hard puzzles from different languages that use little writing or speaking. They wanted to see if the models could figure out how language works by looking at examples of correct answers. To help the models learn, they gave them more examples of correct answers that showed similarities between languages. This worked really well! The models got much better at solving these puzzles and even beat their own records on some problems. This is important because it shows that we can use similar methods to make the models even smarter. |
Keywords
» Artificial intelligence » Boosting » Gpt » Language model » Llama » Prompting
