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Found 9 papers, 5 papers with code
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages
While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages.
Meta Ranking: Less Capable Language Models are Capable for Single Response Judgement
Although Large Language Models (LLMs) have demonstrated strong performance on a wide range of tasks, they still face reliability challenges such as hallucination.
Towards Unified Alignment Between Agents, Humans, and Environment
We also conduct proof-of-concept studies by introducing realistic features to WebShop, including user profiles to demonstrate intentions, personalized reranking for complex environmental dynamics, and runtime cost statistics to reflect self-constraints.
Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization
We further design an automatic agent team optimization algorithm based on an unsupervised metric termed $\textit{Agent Importance Score}$, enabling the selection of best agents based on the contribution each agent makes.
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation
Recently, multi-aspect controllable text generation that controls the generated text in multiple aspects (e. g., sentiment, topic, and keywords) has attracted increasing attention.
FX Resilience around the World: Fighting Volatile Cross-Border Capital Flows
We show that capital flow (CF) volatility exerts an adverse effect on exchange rate (FX) volatility, regardless of whether capital controls have been put in place.
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space
Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system.
