InstructEval Models Explained,
Tk-Instruct

Tk-Instruct represents a collection of encoder-decoder Transformer models meticulously trained to address diverse natural language processing (NLP) tasks. The framework demonstrates remarkable efficacy across tasks such as summarization, question answering, translation, code generation, and natural language inference. The versatility of Tk-Instruct allows it to find applications in a broad spectrum of use cases, including customer service chatbots, personal assistants, educational software, medical diagnosis, and scientific research endeavors.

Model Details

View All Models

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Tk-Instruct

The Tk-Instruct model was developed by a distinguished research team at the Allen Institute for Artificial Intelligence (AI2), led by Yi Zhong, alongside notable co-authors Chris Callison-Burch, Mike Nielsen, Arvind Neelakantan, and Jacob Devlin. Leveraging the foundation of pre-trained T5 models, the team undertook a fine-tuning process using a substantial collection of tasks and instructions from the comprehensive Natural Instructions benchmark. With over 1600 tasks spanning 70+ diverse categories, this extensive training enabled the model to process the provided tasks effectively and demonstrate the ability to generalize to unseen tasks without necessitating further parameter updates. This collaborative endeavor represents a significant advancement in natural language processing research, exemplifying the team's dedication to pushing the boundaries of language model development.

The Tk-Instruct is trained on a dataset of 1600+ NLP tasks, while GPT-3 is trained on a dataset of 500+ NLP tasks.

Robustness

The model can learn from user feedback, allowing for continuous improvement based on performance evaluations. This iterative feedback loop enhances its robustness and reliability over time.

In a study, Tk-Instruct outperformed GPT-3 by over 9% on a benchmark of 119 unseen tasks.

Versatile

The model can work with different modalities, such as text, code, and images. This makes the model more versatile and can be used in various applications.

Tk-Instruct can generate more readable and maintainable code than many prominent large language models.

Open-source

The Tk-Instruct model is released as an open-source framework, enabling unrestricted utilization and customization by individuals and organizations.

Model Details

The Tk-Instruct model comprises a series of encoder-decoder Transformer models characterized by a 12-layer encoder and decoder architecture, each consisting of 1024 hidden units. Hidden units represent computational units within a neural network, and their abundance enables the model to comprehend increasingly intricate patterns. Specifically, the Tk-Instruct model employs the encoder to process instructions as input, while the decoder generates output text aligned with the given instruction. This model underwent fine-tuning on an extensive dataset encompassing 11 billion words. Its impressive performance across renowned benchmarks is noteworthy: achieving 84.4 on the Natural Instructions benchmark, 87.7 on the GLUE benchmark, and 94.2 on the SuperGLUE benchmark. These exceptional capabilities establish the model as a formidable solution for diverse natural language processing (NLP) tasks.

Hugging Face

Model Repositary

Research Paper

Developed by

Model Highlights

The Tk-Instruct model's memory size and training costs vary depending on its specific size. The smallest variant, tk-instruct-base-def-pos, has a memory size of 340MB and requires 1.5 days of training on a single TPU v4 pod. In contrast, the largest variant, tk-instruct-3b-def, has a memory size of 14GB and necessitates 15 days of training on a single TPU v4 pod. Notably, the Tk-Instruct model's memory size and training costs are comparable to other large language models, such as T5 and mT5. However, the unique focus of Tk-Instruct on solving NLP tasks through instruction-following imparts it with enhanced efficiency for such tasks. The versatility of Tk-Instruct enables numerous potential applications, spanning domains like robotics and automation, virtual assistants, education, customer service, and content creation. For instance, Tk-Instruct can enable robots to learn and execute specific tasks in robotics and automation by leveraging natural language instructions or K-shot examples. By learning from these instructions, Tk-Instruct can effectively generalize and tackle novel tasks.

It possesses the capability to comprehend instructions presented in natural language, as well as instructions conveyed through k-shot examples. This versatility enables the model to understand and interpret various instruction formats effectively.
It demonstrates the ability to learn from positive and negative examples, enabling it to discern desirable actions and those to be avoided. Furthermore, the model is adept at assimilating information from multiple sources, empowering it with a comprehensive understanding of diverse contexts.

Training Details

Training Dataset

Tk-Instruct models are trained on a substantial set of tasks and instructions derived from the Natural Instructions benchmark. This benchmark comprises 1600+ tasks across 70+ categories. The Tk-Instruct model series is trained on 757 tasks, while the mTk-Instruct series incorporates training from 1271 tasks, including non-English tasks. These tasks span 64 broad categories, including text categorization, question answering, sentiment analysis, summarization, grammar error detection, and dialogue generation. This comprehensive training approach enables the models to tackle diverse natural language processing (NLP) tasks with enhanced proficiency and adaptability.

Training Procedure

All TK-Instruct models are initialized from either T5 models or mT5 models, specifically utilizing their LM-adapted versions due to the nature of generating output as language modeling. During training, all data were transformed into a text-to-text format, and the models were fine-tuned to optimize the likelihood of generating the desired output sequence. Our released models come in various sizes, each trained with a specific type of instruction encoding. For example, tk-instruct-3b-def-pos is initialized from t5-xl-lm-adapt and incorporates task definitions and two positive examples as training instructions. Notably, although trained with a single type of instruction encoding, these models have demonstrated the ability to accommodate alternative encoding types during testing generally.

Training Observation1

During the training phase, the model successfully acquired the capability to comprehend and adhere to instructions formulated in natural language, showcasing its ability to generalize to novel tasks. However, it is important to note that the training process incurred significant computational costs and exhibited susceptibility to overfitting.

Training Observation2

Even though the model's training proved to be a fruitful experiment overall, it remains in the developmental stage, necessitating further efforts to overcome certain challenges encountered along the way.

Model Predictions and Performance

Below are the performance metrics (measured in ROUGE-L) for the models that have undergone testing.

	Models	Default Track (en)	X-lingual Track
Heuristic Baselines	Copying Instance Input	14.20	5.44
	Copying Demo. Output	28.54	50.31
Pretrained LMs	T5-LM (11B)	30.16	-
	GPT3 (175B)	45.05	51.20
Instruction-tuned Models	T0 (11B)	32.28	-
	GPT3-Instruct (175B)	52.06	53.74
	Tk-Instruct (Ours, 3B)	54.33	-
	Tk-Instruct (Ours, 11B)	60.07	-
	mTk-Instruct (Ours, 3B)	-	56.72

Limitations and Bias

Detailed investigations into the behaviors of Tk-Instruct models have uncovered several notable limitations. Firstly, these models demonstrate a considerable sensitivity to the specific instructions provided, often resulting in substantial variations in output when the instructions are rephrased. Secondly, instances have been observed where the models do not consistently adhere to the given instructions. For example, when instructed to generate a single sentence, the model may deviate by producing either a single word or an extensive narrative. Lastly, it is important to acknowledge that there are cases where the models may encounter significant difficulties or fail in executing certain tasks. These findings underscore the necessity for continued analysis, refinement, and optimization to enhance the reliability and performance of the model across a range of applications. Ongoing efforts are focused on addressing these identified limitations and advancing the capabilities of these models.

Using the Model

An effortless way to experience the capabilities of Tk-Instruct models is by following these simple steps:


from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def")
model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def")

input_ids = tokenizer.encode(
output = model.generate(input_ids, max_length=10)
output = tokenizer.decode(output[0], skip_special_tokens=True)   # model should output 'Indian Rupee'

input_ids = tokenizer.encode(
output = model.generate(input_ids, max_length=10)
output = tokenizer.decode(output[0], skip_special_tokens=True)   # model should output 'John did not go to shool.'

Other InstructEval Models

Falcon 7B Instruct

Falcon-7B-Instruct is a 7B parameter causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets.

Alpaca LoRA

Alpaca LoRA is a 65B parameter LLM that has undergone quantization to 4 bits, resulting in a smaller and more efficient model compared to other LLMs.

StableVicuna

StableVicuna-13B-HF represents an LLM model that has undergone meticulous fine-tuning through reinforcement learning from human feedback (RLHF).

White Papers

Products

MENU

Tk-Instruct

InstructEval Models Explained,
Tk-Instruct

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Tk-Instruct

The Tk-Instruct is trained on a dataset of 1600+ NLP tasks, while GPT-3 is trained on a dataset of 500+ NLP tasks.

Robustness

In a study, Tk-Instruct outperformed GPT-3 by over 9% on a benchmark of 119 unseen tasks.

Versatile

Tk-Instruct can generate more readable and maintainable code than many prominent large language models.

Open-source

Model Details

Model Highlights

Training Details

Training Dataset

Training Procedure

Training Observation1

Training Observation2

Model Predictions and Performance

Limitations and Bias

Using the Model

An effortless way to experience the capabilities of Tk-Instruct models is by following these simple steps:

Other InstructEval Models

Falcon 7B Instruct

Alpaca LoRA

StableVicuna

White Papers

Products

MENU

InstructEval Models Explained,Tk-Instruct

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Tk-Instruct

The Tk-Instruct is trained on a dataset of 1600+ NLP tasks, while GPT-3 is trained on a dataset of 500+ NLP tasks.

Robustness

In a study, Tk-Instruct outperformed GPT-3 by over 9% on a benchmark of 119 unseen tasks.

Versatile

Tk-Instruct can generate more readable and maintainable code than many prominent large language models.

Open-source

Model Details

Model Highlights

Training Details

Training Dataset

Training Procedure

Training Observation1

Training Observation2

Model Predictions and Performance

Limitations and Bias

Using the Model

An effortless way to experience the capabilities of Tk-Instruct models is by following these simple steps:

Other InstructEval Models

Falcon 7B Instruct

Alpaca LoRA

StableVicuna

InstructEval Models Explained,
Tk-Instruct