66 citations · 69 across the 5 of their papers we have counts for
5 papers
Future Token Prediction -- Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction
Nicholas Walker
Causal decoder-only transformer models used for generative language modelling, such as Generative Pre-trained Transformers (GPT), are trained to predict the next token in a sequenc…
A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction
Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
Knowledge graphs are often used to represent structured information in a flexible and efficient manner, but their use in situated dialogue remains under-explored. This paper presen…
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring
Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases. This paper presents a novel…
Structured information extraction from complex scientific text with fine-tuned large language models
Alexander Dunn, John Dagdelen, Nicholas Walker +5
Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language proc…
GraphWOZ: Dialogue Management with Conversational Knowledge Graphs
Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state. To this end, we introduce a new dataset, GraphW…