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cs.CL2024
A Reality Check on Context Utilisation for Retrieval-Augmented Generation
Lovisa Hagström, Sara Vera Marjanović, Haeun Yu +5
Retrieval-augmented generation (RAG) helps address the limitations of parametric knowledge embedded within a language model (LM). In real world settings, retrieved information can…
cs.CL2024
Graph-Guided Textual Explanation Generation Framework
Shuzhou Yuan, Jingyi Sun, Ran Zhang +4
Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned…
cs.CL2024
Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods
Haeun Yu, Pepa Atanasova, Isabelle Augenstein
Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant cha…