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Seungeon Lee · Soumi Das · Manish Gupta · Krishna P. Gummadi
Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters are typically trained for a single task, limiting their applicability in real-world settings, where inputs may span multiple, diverse task domains. A...
Bishwamittra Ghosh · Soumi Das · Till Speicher · Qinyuan Wu · Mohammad Aflah Khan · Deepak Garg · Krishna P. Gummadi · Evimaria Terzi
Large language models (LLMs) operate in two fundamental learning modes – fine-tuning (FT) and in-context learning (ICL) – raising key questions about which mode yields greater language proficiency and whether they differ in their inductive biases. Prior studies comparing FT and ICL have yielded m...
Elisabeth Kirsten · Jost Große Perdekamp · Qinyuan Wu · Mihir Upadhyay · Krishna P. Gummadi · Muhammad Bilal Zafar
The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response. This paradigm differs fundamentally from traditional web search, where results are returned as a ran...