Summary of Evaluating Morphological Compositional Generalization in Large Language Models, by Mete Ismayilzada et al.
Evaluating Morphological Compositional Generalization in Large Language Models
by Mete Ismayilzada, Defne Circi, Jonne Sälevä, Hale Sirin, Abdullatif Köksal, Bhuwan Dhingra, Antoine Bosselut, Duygu Ataman, Lonneke van der Plas
First submitted to arxiv on: 16 Oct 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 Large language models (LLMs) have achieved notable advancements in natural language generation and understanding tasks, but their ability to generalize linguistically remains uncertain. This uncertainty stems from the fact that LLMs are designed to mimic human-like language use, which exhibits compositional generalization and linguistic creativity. In contrast, the extent to which LLMs replicate these abilities, particularly in morphology, is under-explored. To address this gap, researchers have designed a novel suite of generative and discriminative tasks to assess morphological productivity and systematicity. These tasks focus on agglutinative languages such as Turkish and Finnish, and are evaluated using state-of-the-art instruction-finetuned multilingual models like GPT-4 and Gemini. The results show that LLMs struggle with morphological compositional generalization, particularly when applied to novel word roots, with performance declining sharply as morphological complexity increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study investigates how well large language models (LLMs) can understand and generate new words in different languages. Researchers compared the abilities of LLMs to that of humans in understanding and generating new words. They found that while LLMs are good at identifying individual word parts, they struggle when it comes to combining these parts to create new words. This is an important area of study because it can help us understand how well AI models can understand language and potentially improve their performance. |
Keywords
» Artificial intelligence » Gemini » Generalization » Gpt
