15 citations · 19 across the 5 of their papers we have counts for
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cs.CL2025★ 15 cited
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
cs.CL2023
Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Josh Magnus Ludan, Yixuan Meng, Tai Nguyen +4
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on o…
cs.CL2023★ 4 cited
In-context Example Selection with Influences
Tai Nguyen, Eric Wong
In-context learning (ICL) is a powerful paradigm emerged from large language models (LLMs). Despite its promises, ICL performance is known to be highly sensitive to input examples.…