Categories: FAANG

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking

In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, ‘Can we efficiently generate synthetic data for any dialogue schema…
AI Generated Robotic Content

Recent Posts

SamsungCam UltraReal – Qwen-Image LoRA

Hey everyone, Just dropped the first version of a LoRA I've been working on: SamsungCam…

9 hours ago

40 Best Early Amazon Prime Day Deals on WIRED-Tested Gear (2025)

Amazon Prime Day is back, starting on October 7, but we’ve already found good deals…

10 hours ago

These little robots literally walk on water

HydroSpread, a breakthrough fabrication method, lets scientists build ultrathin soft robots directly on water. These…

10 hours ago

VHS filters work great with AI footage (WAN 2.2 + NTSC-RS)

submitted by /u/mtrx3 [link] [comments]

1 day ago

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data

Imbalanced datasets are a common challenge in machine learning.

1 day ago

Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5

Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline…

1 day ago