Evaluating LLMs with CommonGen using CommonGen-lite dataset (400 examples + 900 human references). We use GPT-4 to evaluate the constrained text generation ability of LLMs. Please see more in our paper.
| model | len | cover | pos | win_tie | overall |
|---|---|---|---|---|---|
| human (upper bound) | 12.84 | 99.00 | 98.11 | 100.00 | 97.13 |
| human (lower bound) | 12.84 | 99.00 | 98.11 | 50.00 | 48.57 |
| gpt-4-0613 | 14.13 | 97.44 | 91.78 | 50.44 | 45.11 |
| gpt-4-1106-preview | 14.90 | 96.33 | 90.11 | 50.78 | 44.08 |
| gpt-3.5-turbo | 12.76 | 92.11 | 83.00 | 49.78 | 38.06 |
| Yi-34b-chat | 13.45 | 80.11 | 75.11 | 39.44 | 23.73 |
| Pallas-0.5 | 14.83 | 86.67 | 79.56 | 32.22 | 22.22 |
| vicuna-13b-v1.5 | 15.02 | 85.89 | 79.56 | 27.44 | 18.75 |
| tulu-2-dpo-70b | 17.89 | 88.78 | 80.11 | 23.00 | 16.36 |
| Mixtral-8x7B-Instruct-v0.1 | 20.15 | 84.11 | 73.33 | 17.89 | 11.03 |
| Llama-2-7b-chat-hf | 16.06 | 88.56 | 76.44 | 15.44 | 10.45 |
| zephyr-7b-beta | 15.76 | 82.44 | 72.78 | 16.89 | 10.13 |
| Yi-6b-chat | 13.32 | 71.67 | 63.56 | 22.11 | 10.07 |
- length: the number of words on average in the generated sentences
- cover: the percentage of examples where all given concepts are covered by model outputs
- PoS: the percentage of examples where the part-of-speech (PoS) of ALL given concepts are correct in model outputs
- win_tie: the percentage of examples where GPT-4-turbo prefers the model outputs over the human-written references (or thinks they are equally good)
- overall:
cover%xpos%xwin_tie% - human: the upper bound here considers win_tie=100% and the lower bound considers win_tie=50%.
Link: https://inklab.usc.edu/CommonGen/leaderboard.html
pip install -r requirements.txt
python -m spacy download en_core_web_lg- Dataset: CommonGen-lite
- Scripts: see
scripts/{model_name}.sh
Example:
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python vllm_infer.py \
--data_name "commongen" \
--model_name 01-ai/Yi-34b-chat --tensor_parallel_size 4 --dtype bfloat16 \
--output_folder "model_outputs/" \
--top_p 1 --temperature 0 --batch_size 8 --max_tokens 128Instruction Prompt Template (2-shots prompting)
# Instruction
Given several concepts (i.e., nouns or verbs), write a short and simple sentence that contains *all* the required words.
The sentence should describe a common scene in daily life, and the concepts should be used in a natural way.
# Examples
## Example 1
- Concepts: "dog(noun), frisbee(noun), catch(verb), throw(verb)"
- Sentence: The dog catches the frisbee when the boy throws it into the air.
## Example 2
- Concepts: "apple(noun), place(verb), tree(noun), pick(verb)"
- Sentence: A girl picks some apples from a tree and places them into her basket.
# Your Task
- Concepts: "{$concept_list}"
- Sentence: To make your model on the leaderboard, please create an issue or PR to submit the inference script. I'll run the following evaluation script and update the leaderboard. You will not need to run the evaluation script yourself (the script needs special access to a HF dataset.)
Scripts: see scripts/all_gpt_eval.sh and evaluate.py for knowing more details.
Example:
models=("zephyr-7b-beta" "tulu-2-dpo-70b" "vicuna-13b-v1.5")
for model in "${models[@]}"
do
python evaluate.py --mode "compare" \
--model_output_file "model_outputs/${model}.json" \
--eval_output_file "eval_outputs/${model}.eval_result.gpt-4-1106-preview.json" \
--model gpt-4-1106-preview &
doneEvaluation Prompt Template (Pairwise Comparison)
# Data
Given several concepts (i.e., nouns or verbs), we ask models to write a short and simple sentence that contains *all* the required words.
The sentence should describe a common scene in daily life, and the concepts should be used in a natural way.
Concepts: "{$concept_list}"
Model A: "{$candidate_A}"
Model B: "{$candidate_B}"
# Your Task
Your task is to choose a better sentence from the two candidates. Decide which model's sentence is better in terms of the naturalness and commonness of the scenes they describe.
## Rules:
- A better sentence should describe a common scene in daily life, and all concepts should be used in a natural way.
- You should prefer sentences that use all given concepts with correct part-of-speech tags.
- A simpler and shorter sentence is preferred if it describes the same scene as the other sentence.
- If you think both sentences are equally good or bad, please choose "tie".
Now, please output your choice ("A" or "B" or "tie").
Your choice: Here are some examples of the generated sentences from the models.
Example 1
- Concepts:
['demonstrate_V', 'machine_N', 'sew_V', 'use_V'] - Human Reference:
She used the sewing machine to demonstrate how well she could sew. - Mixtral-8x7B-Instruct-v0.1:
During the fashion design class, the teacher demonstrates how to use the sewing machine to create a stylish outfit.-->Versus=lose; Cover=False; PoS=False; Len=20 - Yi-34b-chat:
The tailor demonstrates how to use the sewing machine.-->Versus=win; Cover=False; PoS=False; Len=9 - Yi-6b-chat:
The machine demonstrates how to sew a useful item.-->Versus=lose; Cover=False; PoS=False; Len=9 - Llama-2-7b-chat-hf:
The teacher demonstrates how to use the sewing machine to sew a new dress, which the student will use to create a beautiful outfit for the school play.-->Versus=lose; Cover=True; PoS=True; Len=28 - zephyr-7b-beta:
The tailor demonstrates how to use the sewing machine to create garments by allowing the customer to try it out themselves.-->Versus=lose; Cover=False; PoS=False; Len=21 - tulu-2-dpo-70b:
A woman demonstrates how to use a machine to sew, showing the audience step by step.-->Versus=lose; Cover=True; PoS=True; Len=16 - vicuna-13b-v1.5:
The tailor demonstrates how to use the sewing machine to make a dress.-->Versus=lose; Cover=False; PoS=False; Len=14 - gpt-3.5-turbo:
The tailor uses the sewing machine to demonstrate how to sew a straight line.-->Versus=lose; Cover=True; PoS=True; Len=14 - gpt-4-0613:
The instructor demonstrates how to use the sewing machine by sewing a piece of fabric.-->Versus=lose; Cover=True; PoS=True; Len=15 - gpt-4-1106-preview:
The tailor demonstrates how to use the sewing machine by making a small pouch.-->Versus=win; Cover=False; PoS=False; Len=14
Example 2
- Concepts:
['car_N', 'drive_V', 'road_N', 'water_N'] - Human Reference:
A man drives the car slowly because there is water on the road - Mixtral-8x7B-Instruct-v0.1:
During the road trip, the family takes breaks to drink water next to the car driving on the open road.-->Versus=lose; Cover=True; PoS=True; Len=21 - Yi-34b-chat:
The car drives down the road splashing water from the puddles.-->Versus=win; Cover=True; PoS=True; Len=11 - Yi-6b-chat:
The car drives along the road, avoiding the puddle of water.-->Versus=win; Cover=True; PoS=True; Len=11 - Llama-2-7b-chat-hf:
The driver drives the car along the road, feeling the water splashing against the tires.-->Versus=lose; Cover=True; PoS=True; Len=15 - zephyr-7b-beta:
The driver takes the car along the road until they reach a place with water, where they cannot drive anymore.-->Versus=lose; Cover=True; PoS=True; Len=20 - tulu-2-dpo-70b:
A person drives their car on the road next to a river, where the water is flowing smoothly.-->Versus=lose; Cover=True; PoS=True; Len=18 - vicuna-13b-v1.5:
The car drives on the road by the water.-->Versus=lose; Cover=True; PoS=True; Len=10 - gpt-3.5-turbo:
I drive my car on the road next to the water.-->Versus=win; Cover=True; PoS=True; Len=11 - gpt-4-0613:
The man drives his car on the road that runs alongside the sparkling water.-->Versus=lose; Cover=True; PoS=True; Len=14 - gpt-4-1106-preview:
The car drives smoothly on the wet road, splashing water as it goes.-->Versus=win; Cover=True; PoS=True; Len=13
Example 3
- Concepts:
['ceiling_N', 'climb_V', 'gym_N', 'rope_N', 'suspend_V'] - Human Reference:
The boys climbed the rope suspended from the gym ceiling. - Mixtral-8x7B-Instruct-v0.1:
During gym class, the students take turns climbing the suspended rope while the others cheer from below, careful not to hit their heads on the ceiling.-->Versus=lose; Cover=True; PoS=True; Len=27 - Yi-34b-chat:
The gymnast climbs the rope and suspends from the ceiling of the gym.-->Versus=lose; Cover=True; PoS=True; Len=13 - Yi-6b-chat:
The climber suspends themselves on the rope as they climb the gym's ceiling.-->Versus=lose; Cover=True; PoS=True; Len=13 - Llama-2-7b-chat-hf:
The gym teacher suspends herself from the ceiling using a rope while climbing to reach the high bar for a workout.-->Versus=lose; Cover=True; PoS=True; Len=21 - zephyr-7b-beta:
Gymnasts suspend themselves from the ceiling by climbing on the ropes in the gym.-->Versus=lose; Cover=True; PoS=True; Len=14 - tulu-2-dpo-70b:
In the gym, an athlete climbs a rope suspended from the ceiling to improve their physical fitness.-->Versus=lose; Cover=True; PoS=True; Len=17 - vicuna-13b-v1.5:
In the gym, she climbs up to the ceiling using a rope that is suspended from the top.-->Versus=lose; Cover=True; PoS=True; Len=19 - gpt-3.5-turbo:
In the gym, the athlete climbs the rope and suspends himself from the ceiling.-->Versus=lose; Cover=True; PoS=True; Len=14 - gpt-4-0613:
In the gym, a man climbs a rope that is suspended from the ceiling.-->Versus=lose; Cover=True; PoS=True; Len=14 - gpt-4-1106-preview:
In the gym, the rope is suspended from the ceiling for athletes to climb during their workout.-->Versus=lose; Cover=True; PoS=True; Len=17
Example 4
- Concepts:
['hat_N', 'knit_V', 'pile_N', 'rock_N', 'sit_V'] - Human Reference:
The woman sat on a pile of rocks and knitted a hat. - Mixtral-8x7B-Instruct-v0.1:
During her break, a woman sits on a rock and starts to knit a pile of hats to sell at the local market.-->Versus=lose; Cover=True; PoS=True; Len=24 - Yi-34b-chat:
The knitted hat sat on a pile of rocks.-->Versus=lose; Cover=False; PoS=False; Len=9 - Yi-6b-chat:
The woman knits a cozy hat while sitting on a rock, placing the pile of rocks around her.-->Versus=lose; Cover=True; PoS=True; Len=18 - Llama-2-7b-chat-hf:
The old man sits on a rock and knits a new hat while piling up the ones he already has, enjoying the peaceful atmosphere.-->Versus=lose; Cover=True; PoS=False; Len=24 - zephyr-7b-beta:
A woman sits on a rock and knits a hat from a pile of yarn.-->Versus=lose; Cover=True; PoS=True; Len=15 - tulu-2-dpo-70b:
An elderly woman knits a colorful hat while sitting on a pile of rocks, enjoying the peaceful scenery around her.-->Versus=lose; Cover=True; PoS=True; Len=20 - vicuna-13b-v1.5:
The old man sits on a rock and knits a hat in a pile of yarn.-->Versus=lose; Cover=True; PoS=True; Len=17 - gpt-3.5-turbo:
The old man sits on a rock, wearing a knitted hat, while a pile of leaves gathers around him.-->Versus=lose; Cover=False; PoS=False; Len=19 - gpt-4-0613:
The woman sits on a rock, knitting a hat, with a pile of yarn beside her.-->Versus=lose; Cover=True; PoS=True; Len=16 - gpt-4-1106-preview:
She sits on a rock, knitting a hat, and places the finished ones in a colorful pile beside her.-->Versus=lose; Cover=True; PoS=True; Len=19
- Contact: Bill Yuchen Lin
- Project website: https://inklab.usc.edu/CommonGen/
- HuggingFace Dataset: CommonGen-lite
@inproceedings{lin-etal-2020-commongen,
title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
author = "Lin, Bill Yuchen and
Zhou, Wangchunshu and
Shen, Ming and
Zhou, Pei and
Bhagavatula, Chandra and
Choi, Yejin and
Ren, Xiang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
pages = "1823--1840",
}
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