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Found 9 papers, 7 papers with code
HwTscSU’s Submissions on WAT 2022 Shared Task
In this paper we describe our submission to the shared tasks of the 9th Workshop on Asian Translation (WAT 2022) on NICT–SAP under the team name ”HwTscSU”.
From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT.
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.
PUMA: Efficient Continual Graph Learning with Graph Condensation
Our prior work, CaT is a replay-based framework with a balanced continual learning procedure, which designs a small yet effective memory bank for replaying data by condensing incoming graphs.
CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Previous neural legal case retrieval models mostly encode the unstructured raw text of case into a case representation, which causes the lack of important legal structural information in a case and leads to poor case representation; (2) Lengthy legal text limitation.
Sparsify-then-Classify: From Internal Neurons of Large Language Models To Efficient Text Classifiers
Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification.
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.
CaT: Balanced Continual Graph Learning with Graph Condensation
Recent replay-based methods intend to solve this problem by updating the model using both (1) the entire new-coming data and (2) a sampling-based memory bank that stores replayed graphs to approximate the distribution of historical data.
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
LogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380. 7% compared with simple prompts.
