16 citations · 20 across the 8 of their papers we have counts for
16 papers
Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models
Jingyi Sun, Pepa Atanasova, Sagnik Ray Choudhury +2
Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, remains largely opaque to users,…
Self-Critique and Refinement for Faithful Natural Language Explanations
Yingming Wang, Pepa Atanasova
With the rapid development of Large Language Models (LLMs), Natural Language Explanations (NLEs) have become increasingly important for understanding model predictions. However, th…
Local Differences, Global Lessons: Insights from Organisation Policies for International Legislation
Lucie-Aimée Kaffee, Pepa Atanasova, Anna Rogers
The rapid adoption of AI across diverse domains has led to the development of organisational guidelines that vary significantly, even within the same sector. This paper examines AI…
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…
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…
Accountable and Explainable Methods for Complex Reasoning over Text
Pepa Atanasova
A major concern of Machine Learning (ML) models is their opacity. They are deployed in an increasing number of applications where they often operate as black boxes that do not prov…