Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024)

This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper Adapting Large Language Models via Reading Comprehension.

We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B.

[2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at AdaMLLM, for adapting MLLMs to domains 🤗

**************************** Updates ****************************

2024/11/29: Released AdaMLLM for adapting MLLMs to domains
2024/9/20: Our research paper for Instruction-Pretrain has been accepted by EMNLP 2024
2024/8/29: Updated guidelines on evaluating any 🤗Huggingface models on the domain-specific tasks
2024/6/22: Released the benchmarking code
2024/6/21: Released the general version of AdaptLLM at Instruction-Pretrain
2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
2024/1/16: Our research paper for AdaptLLM has been accepted by ICLR 2024
2023/12/19: Released our 13B base models developed from LLaMA-1-13B
2023/12/8: Released our chat models developed from LLaMA-2-Chat-7B
2023/9/18: Released our paper, code, data, and base models developed from LLaMA-1-7B

1. Domain-Specific Models

LLaMA-1-7B

In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: Biomedicine-LLM, Finance-LLM and Law-LLM, the performances of our AdaptLLM compared to other domain-specific LLMs are:

LLaMA-1-13B

Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: Biomedicine-LLM-13B, Finance-LLM-13B and Law-LLM-13B.

LLaMA-2-Chat

Our method is also effective for aligned models! LLaMA-2-Chat requires a specific data format, and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: Biomedicine-Chat, Finance-Chat and Law-Chat

For example, to chat with the law-chat model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat")

# Put your input here:
user_input = '''Question: Which of the following is false about ex post facto laws?
Options:
- They make criminal an act that was innocent when committed.
- They prescribe greater punishment for an act than was prescribed when it was done.
- They increase the evidence required to convict a person than when the act was done.
- They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity.

Please provide your choice first and then provide explanations if possible.'''

# Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"

# # NOTE:
# # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
# your_system_prompt = "Please, answer this question faithfully."
# prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"

inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]

answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)

print(pred)

LLaMA-3-8B (💡New!)

In our recent research on Instruction-Pretrain, we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, enabling Llama3-8B to be comparable to or even outperform Llama3-70B: Finance-Llama3-8B, Biomedicine-Llama3-8B.

2. Domain-Specific Tasks

To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: biomedicine-tasks, finance-tasks, and law-tasks.

Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	52.88
AI2 Reasoning Challenge (25-Shot)	53.41
HellaSwag (10-Shot)	76.16
MMLU (5-Shot)	50.24
TruthfulQA (0-shot)	43.53
Winogrande (5-shot)	75.45
GSM8k (5-shot)	18.50

Citation

If you find our work helpful, please cite us:

@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}

AdaptLLM
/

law-chat