TaxoGlimpse

This is the repository for TaxoGlimpse, a benchmark evaluating LLMs' performance on taxonomies.

1. Install requirements

In order to deploy the LLMs and install requirements for the data processing scripts, we need to create the following environments: llama (for Llama-2s), vicuna-self (for vicunas), falcon (for falcons), flan-t5 (for flan-t5s), LLM-probing (for GPTs, Claude-3 and data processing), mixtral (for mistral and mixtral), llama3 (for Llama-3s), and llms4ol (for LLMs4OL). We now introduce how to create these environments with Anaconda.

1.1. llama

$ conda create -n llama python=3.10
$ cd LLMs/llama
$ pip install -e .

1.2. vicuna-self

$ conda create -n vicuna-self python=3.10
$ cd LLMs/vicuna/FastChat
$ pip3 install -e ".[model_worker,webui]"

1.3. falcon

$ conda create -n falcon python=3.10
$ cd requirements
$ pip install -r falcon.txt

1.4. flan-t5

$ conda create -n flan-t5 python=3.10
$ cd requirements
$ pip install -r flan-t5.txt

1.5. LLM-probing

$ conda create -n LLM-probing python=3.10
$ cd requirements
$ pip install -r LLM-probing.txt

1.6. mixtral

$ conda create -n mixtral python=3.8
$ cd requirements
$ pip install -r mixtral.txt

1.7. llama3

$ conda create -n llama3 python=3.10
$ cd requirements
$ pip install -r llama3.txt

1.8. llms4ol

$ conda create -n llms4ol python=3.9
$ cd requirements
$ pip install -r llms4ol.txt

2. Data collection

The data collection process of the taxonomies is as follows:

2.1. eBay

We crawled the eBay taxonomy from link. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/shopping/.

2.2. Google

We obtained the Google Product Category taxonomy from link and crawled the product instances to perform the additional instance typing experiment. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/shopping/.

2.3. Amazon

We crawled Amazon's Product Category and the product instances from the browsenodes.com. We provide the detailed scripts, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/shopping/.

2.4. Schema.org

We downloaded the Schema.org data from link. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/general/.

2.5. ACM-CCS

The ACM-CCS taxonomy was obtained from the following link. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/academic/.

2.6. GeoNames

We download the GeoNames data from link. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/geography/.

2.7. Glottolog

The Glottolog taxonomy (Version 4.8) was obtained from the following link. We provide the data used by us in the README.md in TaxoGlimpse/LLM-taxonomy/language/.

2.8. ICD-10-CM

We accessed the ICD-10-CM taxonomy through the simple-icd-10 package (version 2.0.1), for detailed usage, please refer to the github repo of simple-icd-10. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/medical/.

2.9. OAE

We download the OAE taxonomy from the link. For details, please refer to the README.md in TaxoGlimpse/LLM-taxonomy/OAE/.

2.10. NCBI

The NCBI taxonomy was downloaded through the official download page. We provide the 2023 Sept version as discussed in the README.md in TaxoGlimpse/LLM-taxonomy/biology/.

3. LLMs deployment

We introduce how to deploy the LLMs used in our benchmark.

3.1. Llama-2s

Please refer to steps 3 to 5 of the Quick Start in README.md file to download the model weights (7B-chat, 13B-chat, and 70B-chat).

3.2. Vicunas

Please refer to the Model Weights Section in README.md of Vicuna to download the weights for (lmsys/vicuna-7b-v1.5, lmsys/vicuna-13b-v1.5, and lmsys/vicuna-33b-v1.3).

3.3. Flan-t5s

Use the following Python code to deploy the LLMs:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_3b = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-xl").cuda() # 3B
tokenizer_3b = AutoTokenizer.from_pretrained("google/flan-t5-xl")
model_11b = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-xxl").cuda() # 11B
tokenizer_11b = AutoTokenizer.from_pretrained("google/flan-t5-xxl")

3.4. Falcons

Use the following Python code to deploy the LLMs:

from transformers import AutoTokenizer, AutoModelForCausalLM
model_7b = "tiiuae/falcon-7b-instruct" # 7B
tokenizer_7b = AutoTokenizer.from_pretrained(model_7b)
model_40b = "tiiuae/falcon-40b-instruct" # 40B
tokenizer_40b = AutoTokenizer.from_pretrained(model_40b)

3.5. GPTs

Use the following Python code to deploy the LLMs:

Azure API

from openai import AzureOpenAI
client = AzureOpenAI(
        azure_endpoint = 'https://hkust.azure-api.net',
        api_key = 'xxxxx',
        api_version = "2023-05-15"
    )
def generateResponse(prompt, gpt_name):
    messages = [{"role": "user","content": prompt}]
    response = client.chat.completions.create(
        model=gpt_name,
        temperature=0,
        messages=messages
    )
    return response.choices[0].message.content
generateResponse("example", "gpt-35-turbo")

OpenAI API

from openai import OpenAI
client = OpenAI(
    base_url = 'xxxx',
    api_key = 'xxxx'
)
def generateResponse(prompt, gpt_name):
    messages = [{"role": "user","content": prompt}]
    response = client.chat.completions.create(
        model=gpt_name,
        temperature=0,
        messages=messages
    )
    return response.choices[0].message.content
generateResponse("example", "gpt-4-1106-preview")

3.6. Claude

import os
from litellm import completion
os.environ["ANTHROPIC_API_KEY"] = "XXX"

def generateResponse(prompt):
    messages = [{"role": "user","content": prompt['user']}]
    response = completion(model="claude-3-opus-20240229", messages=messages, api_base="https://api.openai-proxy.org/anthropic/v1/messages", temperature=0)
    return response['choices'][0]['message']['content']

generateResponse("example")

3.7. Llama-3s

Please refer to README.md for a quick start.

3.8. Mistral and Mixtral

from transformers import AutoModelForCausalLM, AutoTokenizer
model_mistral = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2").cuda()
tokenizer_mistral = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model_mixtral = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="auto")
tokenizer_mixtral = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")

3.9. LLMs4OL

You can use the same code for Flan-T5-3B to deploy the model, by modifying the model weights path.

4. Question generation

You can generate the question pools from scratch by referring to the README page for each domain under the sub-folders in TaxoGlimpse/LLM-taxonomy.

5. Evaluation

To conduct the experiments, please follow these steps.

5.1. LLama-2s

We introduce the steps for Llama-7B, Llama-13B, and Llama-70B respectively, including the main experiments and the instance typing experiment.

Llama-2-7B

$ conda activate llama
$ cd TaxoGlimpse/LLMs/llama/
$ torchrun --nproc_per_node 1 tf-variants.py >> ../logs/llama-2-7b-chat/tf-log.txt
$ torchrun --nproc_per_node 1 mcq-variants.py >> ../logs/llama-2-7b-chat/mcq-log.txt
$ ### instance typing experiment
$ torchrun --nproc_per_node 1 instance.py >> ../logs/llama-2-7b-chat/instance-log.txt

Llama-2-13B

$ conda activate llama
$ cd TaxoGlimpse/LLMs/llama/
$ torchrun --nproc_per_node 2 tf-variants.py >> ../logs/llama-2-13b-chat/tf-log.txt
$ torchrun --nproc_per_node 2 mcq-variants.py >> ../logs/llama-2-13b-chat/mcq-log.txt
$ ### instance typing experiment
$ torchrun --nproc_per_node 2 instance.py >> ../logs/llama-2-13b-chat/instance-log.txt

Llama-2-70B

$ conda activate llama
$ cd TaxoGlimpse/LLMs/llama/
$ torchrun --nproc_per_node 8 tf-variants.py >> ../logs/llama-2-70b-chat/tf-log.txt
$ torchrun --nproc_per_node 8 mcq-variants.py >> ../logs/llama-2-70b-chat/mcq-log.txt
$ ### instance typing experiment
$ torchrun --nproc_per_node 8 instance.py >> ../logs/llama-2-70b-chat/instance.txt

5.2. Vicunas

We introduce the steps for Vicuna-7B, Vicuna-13B, and Vicuna-33B respectively, including the main experiments and the instance typing experiment.

Vicuna-7B

$ conda activate vicuna-self
$ cd TaxoGlimpse/LLMs/vicuna/FastChat/
$ ### main experiments
$ python3 -m fastchat.serve.tf-variants --model-path lmsys/vicuna-7b-v1.5 >> ../logs/vicuna-7b/tf-log.txt
$ python3 -m fastchat.serve.mcq-variants --model-path lmsys/vicuna-7b-v1.5 >> ../logs/vicuna-7b/mcq-log.txt
$ ### instance typing experiments
$ python3 -m fastchat.serve.instance --model-path lmsys/vicuna-7b-v1.5 >> ../logs/vicuna-7b/instance-log.txt

Vicuna-13B

$ conda activate vicuna-self
$ cd TaxoGlimpse/LLMs/vicuna/FastChat/
$ ### main experiments
$ python3 -m fastchat.serve.tf-variants --model-path lmsys/vicuna-13b-v1.5 >> ../logs/vicuna-13b/tf-log.txt
$ python3 -m fastchat.serve.mcq-variants --model-path lmsys/vicuna-13b-v1.5 >> ../logs/vicuna-13b/mcq-log.txt
$ ### instance typing experiments
$ python3 -m fastchat.serve.instance --model-path lmsys/vicuna-13b-v1.5 >> ../logs/vicuna-13b/instance-log.txt

Vicuna-33B

$ conda activate vicuna-self
$ cd TaxoGlimpse/LLMs/vicuna/FastChat/
$ ### main experiments
$ python3 -m fastchat.serve.tf-variants --model-path lmsys/vicuna-33b-v1.3 >> ../logs/vicuna-33b/tf-log.txt
$ python3 -m fastchat.serve.mcq-variants --model-path lmsys/vicuna-33b-v1.3 >> ../logs/vicuna-33b/mcq-log.txt
$ ### instance typing experiments
$ python3 -m fastchat.serve.instance --model-path lmsys/vicuna-33b-v1.3 >> ../logs/vicuna-33b/instance-log.txt

5.3. Flan-t5s

We introduce the steps for Flan-T5-3B and Flan-T5-11B respectively, including the main experiments and the instance typing experiment.

Flan-T5-3B and 11B

$ conda activate flan-t5
$ cd TaxoGlimpse/LLMs/flan-t5
$ ### main experiments
$ python tf-variants.py >> ../logs/flan-t5/tf-log.txt
$ python mcq-variants.py >> ../logs/flan-t5/mcq-log.txt
$ ### instance typing experiments
$ python instance.py >> ../logs/flan-t5/instance-log.txt

5.4. Falcons

We introduce the steps for Falcon-7B and Falcon-40B respectively, including the main experiments and the instance typing experiment.

Falcon-7B

$ conda activate falcon
$ cd TaxoGlimpse/LLMs/falcon/7B
$ ### main experiments
$ python tf-variants.py >> ../logs/falcon-7b/tf-log.txt
$ python mcq-variants.py >> ../logs/falcon-7b/mcq-log.txt
$ ### instance typing experiments
$ python instance.py >> ../logs/falcon-7b/instance-log.txt

Falcon-40B

$ conda activate falcon
$ cd TaxoGlimpse/LLMs/falcon/40B
$ ### main experiments
$ python tf-variants.py >> ../logs/falcon-40b/tf-log.txt
$ python mcq-variants.py >> ../logs/falcon-40b/mcq-log.txt
$ ### instance typing experiments
$ python instance.py >> ../logs/falcon-40b/instance-log.txt

5.5. GPTs

We introduce the steps for GPT-3.5 and GPT-4 respectively, including the main experiments and the instance typing experiment.
Please input your Azure APIs or OpenAI APIs at the beginning of the Python files.

GPT 3.5

$ conda activate LLM-probing
$ cd TaxoGlimpse/LLMs/GPT3.5
$ ### main experiments
$ python tf-variants.py >> ../logs/gpt-3.5/tf-log.txt
$ python mcq-variants.py >> ../logs/gpt-3.5/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/gpt-3.5/instance-log.txt

GPT 4

$ conda activate LLM-probing
$ cd TaxoGlimpse/LLMs/GPT4
$ ### main experiments
$ python tf-variants.py >> ../logs/gpt-4/tf-log.txt
$ python mcq-variants.py >> ../logs/gpt-4/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/gpt-4/instance-log.txt

5.6. Claude

We introduce the steps for Claude-3, including the main experiments and the instance typing experiment.
Please input your Anthropic APIs at the beginning of the Python files.

Claude 3

$ conda activate LLM-probing
$ cd TaxoGlimpse/LLMs/Claude
$ ### main experiments
$ python tf-variants.py >> ../logs/Claude/tf-log.txt
$ python mcq-variants.py >> ../logs/Claude/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/Claude/instance-log.txt

5.7. Llama-3s

We introduce the steps for Llama-3-8B and Llama-3-70B respectively, including the main experiments and the instance typing experiment.

Llama-3-8B

$ conda activate llama3
$ cd TaxoGlimpse/LLMs/llama3/
$ torchrun --nproc_per_node 1 tf-variants.py >> ../logs/llama-3-8b/tf-log.txt
$ torchrun --nproc_per_node 1 mcq-variants.py >> ../logs/llama-3-8b/mcq-log.txt
$ ### instance typing experiment
$ torchrun --nproc_per_node 1 instance.py >> ../logs/llama-3-8b/instance-log.txt

Llama-3-70B

$ conda activate llama3
$ cd TaxoGlimpse/LLMs/llama3/
$ torchrun --nproc_per_node 8 tf-variants.py >> ../logs/llama-3-70b/tf-log.txt
$ torchrun --nproc_per_node 8 mcq-variants.py >> ../logs/llama-3-70b/mcq-log.txt
$ ### instance typing experiment
$ torchrun --nproc_per_node 8 instance.py >> ../logs/llama-3-70b/instance.txt

5.8. Mistral and Mixtral

We introduce the steps for Mistral and Mixtral respectively, including the main experiments and the instance typing experiment.

Mistral

$ conda activate mixtral
$ cd TaxoGlimpse/LLMs/Mistral-Mixtral/
$ python tf-variants.py >> ../logs/mistral/tf-log.txt
$ python mcq-variants.py >> ../logs/mistral/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/mistral/instance-log.txt

Mixtral

$ conda activate mixtral
$ cd TaxoGlimpse/LLMs/Mistral-Mixtral/
$ python tf-variants.py >> ../logs/mixtral/tf-log.txt
$ python mcq-variants.py >> ../logs/mixtral/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/mixtral/instance-log.txt

5.9. LLMs4OL

We introduce the steps for LLMs4OL, including the main experiments and the instance typing experiment.

$ conda activate llms4ol
$ cd TaxoGlimpse/LLMs/LLMs4OL/tuning
$ ### instruction tuning for main experiments
$ python3 trainer.py
$ ### instruction tuning for instance typing experiments
$ python3 trainer-instance.py
$ cd TaxoGlimpse/LLMs/LLMs4OL/taxoglimpse
$ ### main experiments
$ python tf-variants.py >> ../logs/llms4ol/tf-log.txt
$ python mcq-variants.py >> ../logs/llms4ol/mcq-log.txt 
$ ### instance typing experiments
$ python instance.py >> ../logs/llms4ol/instance-log.txt

Name		Name	Last commit message	Last commit date
Latest commit History 230 Commits
LLM-taxonomy		LLM-taxonomy
LLMs		LLMs
exp-results		exp-results
figures		figures
question_pools		question_pools
requirements		requirements
README.md		README.md
tech_report.pdf		tech_report.pdf

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TaxoGlimpse

1. Install requirements

1.1. llama

1.2. vicuna-self

1.3. falcon

1.4. flan-t5

1.5. LLM-probing

1.6. mixtral

1.7. llama3

1.8. llms4ol

2. Data collection

2.1. eBay

2.2. Google

2.3. Amazon

2.4. Schema.org

2.5. ACM-CCS

2.6. GeoNames

2.7. Glottolog

2.8. ICD-10-CM

2.9. OAE

2.10. NCBI

3. LLMs deployment

3.1. Llama-2s

3.2. Vicunas

3.3. Flan-t5s

3.4. Falcons

3.5. GPTs

Azure API

OpenAI API

3.6. Claude

3.7. Llama-3s

3.8. Mistral and Mixtral

3.9. LLMs4OL

4. Question generation

5. Evaluation

5.1. LLama-2s

Llama-2-7B

Llama-2-13B

Llama-2-70B

5.2. Vicunas

Vicuna-7B

Vicuna-13B

Vicuna-33B

5.3. Flan-t5s

Flan-T5-3B and 11B

5.4. Falcons

Falcon-7B

Falcon-40B

5.5. GPTs

GPT 3.5

GPT 4

5.6. Claude

Claude 3

5.7. Llama-3s

Llama-3-8B

Llama-3-70B

5.8. Mistral and Mixtral

Mistral

Mixtral

5.9. LLMs4OL

6. Main results

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