Recent work in time series analysis has increasingly focused on adapting pretrained large language models (LLMs) forforecasting (TSF), classification, and anomaly detection. These studies suggest that language models, designed for sequential dependencies in text, could generalize to time series data. ...
In this work, we leverage pre-trained Large Language Models (LLMs) to enhance time-series forecasting. Mirroring the growing interest in unifying models for Natural Language Processing and Computer Vision, we envision creating an analogous model for long-term time-series forecasting. Due to limited...
import {Completion, CompletionResponse, Parea} from "parea-ai"; const p = new Parea('PAREA_API_KEY'); const deployedPromptCall = async (query: string): Promise<string> => { const completion: Completion = { deployment_id: 'Deployment_ID', llm_inputs: { query: query }, }; const ...
LLM-Sentry represents a novel black-box defense strategy to safeguard Large Language Models (LLMs) against jailbreaking attacks. A key advantage of our approach is its model-agnostic nature, as it does not rely on specific information about the model’s architecture or parameters, thereby...
The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This paper introduces CriticBench, a comprehensive benchmark designed to assess LLMs' abilities to critique and rectify their ...