3 papers
cs.CL2025
All for law and law for all: Adaptive RAG Pipeline for Legal Research
Figarri Keisha, Prince Singh, Pallavi +5
Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks by grounding Large Language Model (LLM) outputs in retrieved knowledge. This capability i…
cs.IR2025
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed +11
Question Answering (QA) accounts for a significant portion of LLM usage "in the wild". However, LLMs sometimes produce false or misleading responses, also known as "hallucinations"…
cs.LG2025
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Makoto Takamoto, Daniel Oñoro-Rubio, Wiem Ben Rim +2
Knowledge graph embedding (KGE) models encode the structural information of knowledge graphs to predicting new links. Effective training of these models requires distinguishing bet…