Paper | Model | Leaderboard
📣 Introducing Resta: Safety Re-alignment of Language Models. Paper Github
📣 Red-Eval, the benchmark for Safety Evaluation of LLMs has been added: Red-Eval
📣 Introducing Red-Eval to evaluate the safety of the LLMs using several jailbreaking prompts. With Red-Eval one could jailbreak/red-team GPT-4 with a 65.1% attack success rate and ChatGPT could be jailbroken 73% of the time as measured on DangerousQA and HarmfulQA benchmarks. More details are here: Code and Paper.
📣 We developed Flacuna by fine-tuning Vicuna-13B on the Flan collection. Flacuna is better than Vicuna at problem-solving. Access the model here https://huggingface.co/declare-lab/flacuna-13b-v1.0.
📣 The InstructEval benchmark and leaderboard have been released.
📣 The paper reporting Instruction Tuned LLMs on the InstructEval benchmark suite has been released on Arxiv. Read it here: https://arxiv.org/pdf/2306.04757.pdf
📣 We are releasing IMPACT, a dataset for evaluating the writing capability of LLMs in four aspects: Informative, Professional, Argumentative, and Creative. Download it from Huggingface: https://huggingface.co/datasets/declare-lab/InstructEvalImpact.
📣 FLAN-T5 is also useful in text-to-audio generation. Find our work at https://github.com/declare-lab/tango if you are interested.
This repository contains code to evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks. We aim to facilitate simple and convenient benchmarking across multiple tasks and models.
Instruction-tuned models such as Flan-T5 and Alpaca represent an exciting direction to approximate the performance of large language models (LLMs) like ChatGPT at lower cost. However, it is challenging to compare the performance of different models qualitatively. To evaluate how well the models generalize across a wide range of unseen and challenging tasks, we can use academic benchmarks such as MMLU and BBH. Compared to existing libraries such as evaluation-harness and HELM, this repo enables simple and convenient evaluation for multiple models. Notably, we support most models from HuggingFace Transformers 🤗 (check here for a list of models we support):
- AutoModelForCausalLM ( eg GPT-2, GPT-J , OPT-IML, BLOOMZ)
- AutoModelForSeq2SeqLM ( eg Flan-T5, Flan-UL2 , TK-Instruct)
- LlamaForCausalLM ( eg LLaMA , Alpaca, Vicuna)
- ChatGLM
For detailed results, please go to our leaderboard
| Model Name | Model Path | Paper | Size | MMLU | BBH | DROP | HumanEval |
|---|---|---|---|---|---|---|---|
| GPT-4 | Link | ? | 86.4 | 80.9 | 67.0 | ||
| ChatGPT | Link | ? | 70.0 | 64.1 | 48.1 | ||
| seq_to_seq | google/flan-t5-xxl | Link | 11B | 54.5 | 43.9 | ||
| seq_to_seq | google/flan-t5-xl | Link | 3B | 49.2 | 40.2 | 56.3 | |
| llama | eachadea/vicuna-13b | Link | 13B | 49.7 | 37.1 | 32.9 | 15.2 |
| llama | decapoda-research/llama-13b-hf | Link | 13B | 46.2 | 37.1 | 35.3 | 13.4 |
| seq_to_seq | declare-lab/flan-alpaca-gpt4-xl | Link | 3B | 45.6 | 34.8 | ||
| llama | TheBloke/koala-13B-HF | Link | 13B | 44.6 | 34.6 | 28.3 | 11.0 |
| llama | chavinlo/alpaca-native | Link | 7B | 41.6 | 33.3 | 26.3 | 10.3 |
| llama | TheBloke/wizardLM-7B-HF | Link | 7B | 36.4 | 32.9 | 15.2 | |
| chatglm | THUDM/chatglm-6b | Link | 6B | 36.1 | 31.3 | 44.2 | 3.1 |
| llama | decapoda-research/llama-7b-hf | Link | 7B | 35.2 | 30.9 | 27.6 | 10.3 |
| llama | wombat-7b-gpt4-delta | Link | 7B | 33.0 | 32.4 | 7.9 | |
| seq_to_seq | bigscience/mt0-xl | Link | 3B | 30.4 | |||
| causal | facebook/opt-iml-max-1.3b | Link | 1B | 27.5 | 1.8 | ||
| causal | OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5 | Link | 12B | 27.0 | 30.0 | 9.1 | |
| causal | stabilityai/stablelm-base-alpha-7b | Link | 7B | 26.2 | 1.8 | ||
| causal | databricks/dolly-v2-12b | Link | 12B | 25.7 | 7.9 | ||
| causal | Salesforce/codegen-6B-mono | Link | 6B | 27.4 | |||
| causal | togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1 | Link | 7B | 38.1 | 31.3 | 24.7 | 5.5 |
Evaluate on Massive Multitask Language Understanding (MMLU) which includes exam questions from 57 tasks such as mathematics, history, law, and medicine. We use 5-shot direct prompting and measure the exact-match score.
python main.py mmlu --model_name llama --model_path chavinlo/alpaca-native
# 0.4163936761145136
python main.py mmlu --model_name seq_to_seq --model_path google/flan-t5-xl
# 0.49252243270189433
Evaluate on Big Bench Hard (BBH) which includes 23 challenging tasks for which PaLM (540B) performs below an average human rater. We use 3-shot direct prompting and measure the exact-match score.
python main.py bbh --model_name llama --model_path TheBloke/koala-13B-HF --load_8bit
# 0.3468942926723247
Evaluate on DROP which is a math question answering benchmark. We use 3-shot direct prompting and measure the exact-match score.
python main.py drop --model_name seq_to_seq --model_path google/flan-t5-xl
# 0.5632458233890215
Evaluate on HumanEval which includes 164 coding questions in python. We use 0-shot direct prompting and measure the pass@1 score.
python main.py humaneval --model_name llama --model_path eachadea/vicuna-13b --n_sample 1 --load_8bit
# {'pass@1': 0.1524390243902439}
Install dependencies and download data.
conda create -n instruct-eval python=3.8 -y
conda activate instruct-eval
pip install -r requirements.txt
mkdir -p data
wget https://people.eecs.berkeley.edu/~hendrycks/data.tar -O data/mmlu.tar
tar -xf data/mmlu.tar -C data && mv data/data data/mmlu