5 papers
SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?
Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng +9
Multilingual text embeddings are often assumed to encode meaning in a perspective-independent semantic space, yielding stable similarity judgments across tasks and languages. Our r…
Assessing Thai Dialect Performance in LLMs with Automatic Benchmarks and Human Evaluation
Peerat Limkonchotiwat, Kanruethai Masuk, Surapon Nonesung +4
Large language models show promising results in various NLP tasks. Despite these successes, the robustness and consistency of LLMs in underrepresented languages remain largely unex…
Space Decomposition for Sentence Embedding
Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Ekapol Chuangsuwanich +1
Determining sentence pair similarity is crucial for various NLP tasks. A common technique to address this is typically evaluated on a continuous semantic textual similarity scale f…
An Efficient Self-Supervised Cross-View Training For Sentence Embedding
Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul +3
Self-supervised sentence representation learning is the task of constructing an embedding space for sentences without relying on human annotation efforts. One straightforward appro…
Typo-Robust Representation Learning for Dense Retrieval
Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat +3
Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries con…