JechLee's starred repositories
NeMo-Guardrails
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
LLMDataHub
A quick guide (especially) for trending instruction finetuning datasets
promptbench
A unified evaluation framework for large language models
transformers_tasks
⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.
DeepLearing-Interview-Awesome-2024
AIGC-interview/CV-interview/LLMs-interview面试问题与答案集合仓,同时包含工作和科研过程中的新想法、新问题、新资源与新项目
awesome_LLMs_interview_notes
LLMs interview notes and answers:该仓库主要记录大模型(LLMs)算法工程师相关的面试题和参考答案
awesome-llm-security
A curation of awesome tools, documents and projects about LLM Security.
Awesome-LLM-Safety
A curated list of safety-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the safety implications, challenges, and advancements surrounding these powerful models.
CipherChat
A framework to evaluate the generalization capability of safety alignment for LLMs
lost-in-the-middle
Code and data for "Lost in the Middle: How Language Models Use Long Contexts"
LeetCode021
🚀 LeetCode From Zero To One & 题单整理 & 题解分享 & 算法模板 & 刷题路线,持续更新中...
DecodingTrust
A Comprehensive Assessment of Trustworthiness in GPT Models
korean-safety-benchmarks
Official datasets and pytorch implementation repository of SQuARe and KoSBi (ACL 2023)
LLMs-Finetuning-Safety
We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.
LLM-data-aug-survey
The official GitHub page for the survey paper "A Survey on Data Augmentation in Large Model Era"
SuperCLUE-Safety
SC-Safety: 中文大模型多轮对抗安全基准
SALAD-BENCH
【ACL 2024】 SALAD benchmark & MD-Judge
red-instruct
Codes and datasets of the paper Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Safety-Evaluating
本文提出了一个基于“文心一言”的**LLMs的安全评估基准,其中包括8种典型的安全场景和6种指令攻击类型。此外,本文还提出了安全评估的框架和过程,利用手动编写和收集开源数据的测试Prompts,以及人工干预结合利用LLM强大的评估能力作为“共同评估者”。
LiveSum-TTT
Codes and Datasets for the Paper: Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction