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Found 9 papers, 1 papers with code
Analysis of Zero-Shot Crosslingual Learning between English and Korean for Named Entity Recognition
This paper presents a English-Korean parallel dataset that collects 381K news articles where 1, 400 of them, comprising 10K sentences, are manually labeled for crosslingual named entity recognition (NER).
FantasyCoref: Coreference Resolution on Fantasy Literature Through Omniscient Writer’s Point of View
This paper presents a new corpus and annotation guideline for a novel coreference resolution task on fictional texts, and analyzes its unique characteristics.
Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks.
Tinker Tales: Interactive Storytelling Framework for Early Childhood Narrative Development and AI Literacy
This paper presents Tinker Tales, an interactive storytelling framework in the format of a board game, designed to support both narrative development and AI literacy in early childhood.
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems
RAG has become a key technique for enhancing LLMs by reducing hallucinations, especially in domain expert systems where LLMs may lack sufficient inherent knowledge.
RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine
Large Language Models (LLMs) excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries.
SLM as Guardian: Pioneering AI Safety with Small Language Models
In this paper, we leverage a smaller LLM for both harmful query detection and safeguard response generation.
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search
Although there has been a growing interest among industries in integrating generative LLMs into their services, limited experience and scarcity of resources act as a barrier in launching and servicing large-scale LLM-based services.
Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection
Contextual word embeddings obtained from pre-trained language model (PLM) have proven effective for various natural language processing tasks at the word level.
