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Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
The rise of foundation models fine-tuned on human feedback from potentially untrusted users has increased the risk of adversarial data …
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference …
Generating a Low-code Complete Workflow via Task Decomposition and RAG
AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, …
Societal Alignment Frameworks Can Improve LLM Alignment
Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared …
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We …
MMTEB: Massive Multilingual Text Embedding Benchmark

Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent …

LitLLMs, LLMs for Literature Review: Are We There Yet?

Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, …

StarVector: Generating Scalable Vector Graphics Code from Images and Text
Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation …
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models
Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability, referring to an LLM’s capability to …