Learn how to build solutions with Large Language Models.

개발자를 위한 이력서 작성 가이드, 포맷 그리고 다양한 예시들

Awesome list of Korean Large Language Models.

A multi-purpose LLM framework for RAG and data creation.

Practical course about Large Language Models.

Code and Data for "Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering"

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Download market data from Yahoo! Finance's API

Implementation of the Paper "Goal-Driven Explainable Clustering via Language Descriptions"

The official Meta Llama 3 GitHub site

A framework for few-shot evaluation of language models.

A Survey of Attributions for Large Language Models

[ Text Analytics ] 법률 도메인 특화 한국어 기반 LLM 개발

A Native-PyTorch Library for LLM Fine-tuning

Uncertainty quantification with PyTorch

Instruct-tune LLaMA on consumer hardware

Source code of the paper: RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering [Findings of ACL 2024]

OpenAGI: When LLM Meets Domain Experts

Faithfulness and factuality annotations of XSum summaries from our paper "On Faithfulness and Factuality in Abstractive Summarization" (https://www.aclweb.org/anthology/2020.acl-main.173.pdf).

Forked repo from https://github.com/EleutherAI/lm-evaluation-harness/commit/1f66adc

The Universe of Evaluation. All about the evaluation for LLMs.

Awesome LLM for NLG Evaluation Papers

Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.

The papers are organized according to our survey: Evaluating Large Language Models: A Comprehensive Survey.

Awesome LLM Benchmarks to evaluate the LLMs across text, code, image, audio, video and more.

Time-series-LLM

Unofficial re-implementation of "Trusting Your Evidence: Hallucinate Less with Context-aware Decoding"

Instructional learning for Aspect Based Sentiment Analysis [NAACL-2024]

Code and data for "KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark" (LREC-COLING 2024)