Recent research shows that pre-trained language models (PLMs) suffer from" prompt bias"
in factual knowledge extraction, ie, prompts tend to introduce biases toward specific labels …

Prompt Transfer (PoT) is a recently-proposed approach to improve prompt-tuning, by
initializing the target prompt with the existing prompt trained on similar source tasks …

Relation Extraction (RE) is a crucial task in Information Extraction, which entails predicting
relationships between entities within a given sentence. However, extending pre-trained RE …

Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of
task-oriented dialogue domains without the cost of collecting in-domain data. Existing works …

Quantization is a promising approach for reducing memory overhead and accelerating
inference, especially in large pre-trained language model (PLM) scenarios. While having no …

The Mixture of Experts (MoE) has emerged as a highly successful technique in deep
learning, based on the principle of divide-and-conquer to maximize model capacity without …

This technical report briefly describes our JDExplore d-team's submission Vega v1 on the
General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a …

The paper presents the JSI and WüNLP systems submitted to the DIALECT-COPA shared
task on causal commonsense reasoning in dialectal texts. Jointly, we compare LLM-based …

In recent years, the Mixture-of-Experts (MoE) technique has gained widespread popularity
as a means to scale pre-trained models to exceptionally large sizes. Dynamic activation of …

Dynamic networks, eg, Dynamic Convolution (DY-Conv) and the Mixture of Experts (MoE),
have been extensively explored as they can considerably improve the model's …