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The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the …
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference …
Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases in …
Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT …
Multilingual LLMs have achieved remarkable benchmark performance, but we find they continue to underperform on non-Latin script …
AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, …
Research on trust in AI is limited to several trustors (e.g., end-users) and trustees (especially AI systems), and empirical …
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding …
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We …
Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent …