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Found 10 papers, 9 papers with code
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
The two prominent approaches to enhance the performance of LLMs on low-frequent topics are: Retrieval Augmented Generation (RAG) and fine-tuning (FT) over synthetic data.
MMEAD: MS MARCO Entity Annotations and Disambiguations
MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets.
Data Augmentation for Conversational AI
This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems.
Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases
Automatic extraction of funding information from academic articles adds significant value to industry and research communities, such as tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies.
Personal Entity, Concept, and Named Entity Linking in Conversations
It is, however, shown that existing EL methods developed for annotating documents are suboptimal for conversations, where personal entities (e. g., "my cars") and concepts are essential for understanding user utterances.
Entity-aware Transformers for Entity Search
Pre-trained language models such as BERT have been a key ingredient to achieve state-of-the-art results on a variety of tasks in natural language processing and, more recently, also in information retrieval. Recent research even claims that BERT is able to capture factual knowledge about entity relations and properties, the information that is commonly obtained from knowledge graphs.
Conversational Entity Linking: Problem Definition and Datasets
Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting.
Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph
We review existing definitions of bias in the literature: people bias, algorithm bias, and a combination of the two, and further propose different strategies for tackling these biases for conversational search systems.
REL: An Entity Linker Standing on the Shoulders of Giants
Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits.
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Entity Linking
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Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings.
