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Found 8 papers, 3 papers with code
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations
Language models pre-trained on general text have achieved impressive results in diverse fields.
On Large Language Models' Hallucination with Regard to Known Facts
In hallucinated cases, the output token's information rarely demonstrates abrupt increases and consistent superiority in the later stages of the model.
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
Pre-trained language models have been successful in many scenarios.
PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in the correct direction.
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
Ranked #1 on
Few-shot NER
on Few-NERD (INTER)
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
