Search Results for author:
Found 9 papers, 6 papers with code
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models
To address these challenges, this paper introduces a set of KPIs tailored for evaluating the performance of in-car ConvQA systems, along with datasets specifically designed for these KPIs.
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data.
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
In our approach, we build on existing strong representations of single modalities and we use hypercomplex algebra to represent both, (i), single-modality embedding as well as, (ii), the interaction between different modalities and their complementary means of knowledge representation.
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard.
RoMe: A Robust Metric for Evaluating Natural Language Generation
Secondly, it should consider the grammatical quality of the generated sentence.
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings
The learning process of such models can be performed by contrasting positive and negative triples.
VANiLLa : Verbalized Answers in Natural Language at Large Scale
We also present results of training our dataset on multiple baseline models adapted from current state-of-the-art Natural Language Generation (NLG) architectures.
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge.
Using a KG-Copy Network for Non-Goal Oriented Dialogues
Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.
