Summary of Data-augmentation-based Dialectal Adaptation For Llms, by Fahim Faisal and Antonios Anastasopoulos
Data-Augmentation-Based Dialectal Adaptation for LLMs
by Fahim Faisal, Antonios Anastasopoulos
First submitted to arxiv on: 11 Apr 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 paper reports on GMUNLP’s participation in the Dialect-Copa shared task at VarDial 2024, which evaluates large language models’ (LLMs) commonsense reasoning capabilities on South Slavic micro-dialects. The task aims to assess LLMs’ performance on non-standard dialectal varieties, building upon their well-established capabilities on standard languages. To achieve this, the authors propose an approach that combines strengths of different language models and leverages data augmentation techniques to improve task performance on three South Slavic dialects: Chakavian, Cherkano, and Torlak. Experiments are conducted using a language-family-focused encoder-based model (BERTić) and a domain-agnostic multilingual model (AYA-101). The results demonstrate substantial performance gains across all three test datasets in the open-source model category, highlighting the practical utility of data augmentation and the potential of LLMs in handling non-standard dialectal varieties. This work contributes to advancing natural language understanding in low-resource and dialectal settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But did you know they’re not perfect? They struggle with languages that are different from the ones they were trained on, like special dialects spoken by small groups of people. To help them get better, researchers at GMUNLP created a new way to train these models using old texts and making slight changes to make it more challenging for them. They tested this method on three special dialects spoken in Eastern Europe and found that it worked really well! This is important because it can help computers understand languages that are hard to learn, which can be helpful for people who speak those languages. |
Keywords
» Artificial intelligence » Data augmentation » Encoder » Language understanding
