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Found 11 papers, 7 papers with code
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
The second step involves preserving dataset diversity through a clustering process. In our experiment, CaR selected a subset containing only 1. 96% of Alpaca's IT data, yet the underlying AlpaCaR model trained on this subset outperforms Alpaca by an average of 32. 1% in GPT-4 evaluations.
Using Large Language Model for End-to-End Chinese ASR and NER
In this work, we connect the Whisper encoder with ChatGLM3 and provide in-depth comparisons of these two approaches using Chinese automatic speech recognition (ASR) and name entity recognition (NER) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions.
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations, and terminologies not frequently encountered in the training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A Simple but Effective Bidirectional Framework for Relational Triple Extraction
Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods.
Community detection in censored hypergraph
In practice, a network may has censored (or missing) values and it is shown that censored values have non-negligible effect on the structural properties of a network.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Next, the mined global associations are integrated into the table feature of each relation.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research interest.
A Conditional Cascade Model for Relational Triple Extraction
Tagging based methods are one of the mainstream methods in relational triple extraction.
An Effective System for Multi-format Information Extraction
Finally, our system ranks No. 4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79. 887%, 85. 179%, and 70. 828% respectively.
