Copertina

Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hong, Mengze, Jiang, Di, Song, Yuanfeng, Zhang, Chen Jason
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computation and Language Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
di: Hong, Mengze, et al.
Pubblicazione: (2024)

Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection
di: Zhao, Shiman, et al.
Pubblicazione: (2025)

Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
di: Li, Chengzhengxu, et al.
Pubblicazione: (2023)

InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries
di: Hong, Mengze, et al.
Pubblicazione: (2024)

Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
di: Liu, Haochen, et al.
Pubblicazione: (2024)

Learning to Learn for Few-shot Continual Active Learning
di: Ho, Stella, et al.
Pubblicazione: (2023)

Dialogue Language Model with Large-Scale Persona Data Engineering
di: Hong, Mengze, et al.
Pubblicazione: (2024)

Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
di: Frayling, Erlend, et al.
Pubblicazione: (2024)

PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning
di: Zhang, Tianrong, et al.
Pubblicazione: (2024)

Intent-driven In-context Learning for Few-shot Dialogue State Tracking
di: Yi, Zihao, et al.
Pubblicazione: (2024)

RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
di: Najafi, Saeed, et al.
Pubblicazione: (2024)

FLEx: Language Modeling with Few-shot Language Explanations
di: Avsian, Adar, et al.
Pubblicazione: (2026)

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
di: Hong, Mengze, et al.
Pubblicazione: (2025)

Proactive Dialogue Model with Intent Prediction
di: Luo, Yang
Pubblicazione: (2026)

Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
di: Cheng, Xiang, et al.
Pubblicazione: (2025)

Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
di: Lang, Hao, et al.
Pubblicazione: (2023)

Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning
di: Ziletti, Angelo, et al.
Pubblicazione: (2022)

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach
di: Dong, Shouyang, et al.
Pubblicazione: (2025)

Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
di: Lu, Yen-Ju, et al.
Pubblicazione: (2025)

On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
di: Alam, Md Ibrahim Ibne, et al.
Pubblicazione: (2024)

Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
di: Dong, Zican, et al.
Pubblicazione: (2025)

CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues
di: Sheshanarayana, Disha, et al.
Pubblicazione: (2025)

Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing
di: Li, Zhuoran, et al.
Pubblicazione: (2025)

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
di: Yuan, Yifei, et al.
Pubblicazione: (2025)

Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples
di: Sato, Soma, et al.
Pubblicazione: (2024)

Few-shot Personalization of LLMs with Mis-aligned Responses
di: Kim, Jaehyung, et al.
Pubblicazione: (2024)

Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
di: Pu, Jiashu, et al.
Pubblicazione: (2024)

Tree Matching Networks for Natural Language Inference: Parameter-Efficient Semantic Understanding via Dependency Parse Trees
di: Lunder, Jason
Pubblicazione: (2025)

AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
di: Tang, Yuwei, et al.
Pubblicazione: (2024)

A Truly Joint Neural Architecture for Segmentation and Parsing
di: Levi, Danit Yshaayahu, et al.
Pubblicazione: (2024)

Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text
di: Al-Lawati, Ali, et al.
Pubblicazione: (2025)

DrugLLM: Open Large Language Model for Few-shot Molecule Generation
di: Liu, Xianggen, et al.
Pubblicazione: (2024)

ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages
di: Halitaj, Aida, et al.
Pubblicazione: (2025)

Effectiveness of Pre-training for Few-shot Intent Classification
di: Zhang, Haode, et al.
Pubblicazione: (2021)

Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models
di: Hong, Mengze, et al.
Pubblicazione: (2024)

QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
di: Hong, Mengze, et al.
Pubblicazione: (2025)

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
di: Rieger, Jonas, et al.
Pubblicazione: (2023)

A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
di: Labrak, Yanis, et al.
Pubblicazione: (2023)

Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
di: Wang, Qingyun, et al.
Pubblicazione: (2024)

LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
di: Qin, Zhen, et al.
Pubblicazione: (2024)