1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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"…
Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains
Chia-Chien Hung, Wiem Ben Rim, Lindsay Frost +2
High-risk domains pose unique challenges that require language models to provide accurate and safe responses. Despite the great success of large language models (LLMs), such as Cha…
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim +5
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) task…