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Found 6 papers, 4 papers with code
$\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics
Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios.
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Recent studies show the growing significance of document retrieval in the generation of LLMs, i. e., RAG, within the scientific domain by bridging their knowledge gap.
Finetuning Large Language Model for Personalized Ranking
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, motivating researchers to investigate their potential use in recommendation systems.
A Survey of Confidence Estimation and Calibration in Large Language Models
Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems.
SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems.
Ranked #3 on
Slot Filling
on MixSNIPS
