6 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics
Yiming Du, Wenyu Huang, Danna Zheng +5
Memory is fundamental to large language model (LLM)-based agents, but existing surveys emphasize application-level use (e.g., personalized dialogue), while overlooking the atomic o…
Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts
Wenyu Huang, Guancheng Zhou, Mirella Lapata +3
Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially wh…
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters
Sebastien Montella, Alexis Nasr, Johannes Heinecke +2
Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures
Sebastien Montella, Lina Rojas-Barahona, Johannes Heinecke
Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task. The main limitation of such models is their…
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
Sebastien Montella, Betty Fabre, Tanguy Urvoy +2
The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficien…