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Found 8 papers, 5 papers with code
Multi-view Content-aware Indexing for Long Document Retrieval
As they do not consider content structures, the resultant chunks can exclude vital information or include irrelevant content.
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
Recommender systems aim to predict user interest based on historical behavioral data.
Aligning Crowd Feedback via Distributional Preference Reward Modeling
In this paper, we introduce the Distributional Preference Reward Model (DPRM), a simple yet effective framework to align large language models with a diverse set of human preferences.
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples.
Open Information Extraction via Chunks
Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC.
Syntactic Multi-view Learning for Open Information Extraction
In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures.
Ranked #1 on
Open Information Extraction
on LSOIE-wiki
DocOIE: A Document-level Context-Aware Dataset for OpenIE
Both DocOIE dataset and DocIE model are released for public.
Ranked #1 on
Open Information Extraction
on DocOIE-transportation
