Summary of Llm For Everyone: Representing the Underrepresented in Large Language Models, by Samuel Cahyawijaya
LLM for Everyone: Representing the Underrepresented in Large Language Models
by Samuel Cahyawijaya
First submitted to arxiv on: 20 Sep 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 This thesis tackles the limitations of large language models (LLMs) in multilingual settings, particularly in underrepresented languages. A comprehensive evaluation of LLMs reveals challenges in generalizing across languages, highlighting the need for more inclusive and culturally sensitive NLP solutions. To address this gap, the authors propose data-and-compute-efficient methods for mitigating disparities in LLM ability, including cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment. Additionally, a novel method to measure cultural values alignment is proposed, ensuring LLMs operating in different languages align with local cultural values. This research aims to enhance the multilingual and multicultural alignment of LLLs, advancing the NLP field toward greater equality and inclusiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve how computers understand and work with many languages, especially ones that are not very well studied. Right now, big language models can do lots of things, but they struggle to generalize or adapt to new languages. The authors want to fix this problem by developing more efficient methods for training these models on underrepresented languages. They also propose a new way to measure how culturally sensitive these models are when working with different languages. Overall, the goal is to make language technology more inclusive and accessible to people around the world. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Nlp
