Instruction Tuning For Large Language Models- A Survey

Created: 2023-11-08 09:19
#paper

Main idea

The survey paper "Instruction Tuning for Large Language Models: A Survey" provides an overview of the Instruction fine-tuning technique for large language models. The paper discusses the pros and cons of this technique and describes the main approaches used, along with their advantages and disadvantages. The paper also suggests future research ideas. Here is a summary of the main points:

Introduction: The paper provides an overview of the Instruction fine-tuning technique for large language models.
Pros and Cons of Instruction Fine-tuning: The paper discusses the advantages and disadvantages of Instruction fine-tuning. Some of the pros include improved performance on specific tasks and the ability to fine-tune models with limited data. Some of the cons include the need for manual annotation and the potential for overfitting.
Main Approaches: The paper describes the main approaches used for Instruction fine-tuning, including Instruct-GPT, BLOOMZ, FLAN-T5, Alpaca, Vicuna, GPT-4-LLM, and Claude. For each approach, the paper discusses the model size, whether it uses labeled data, and its strengths and weaknesses.
Future Research Ideas: The paper suggests several future research ideas, including exploring the use of unsupervised methods for Instruction fine-tuning, investigating the impact of different types of instructions, and developing more efficient methods for manual annotation.

Overall, the paper provides a comprehensive overview of Instruction fine-tuning for large language models, including its benefits and limitations, main approaches, and future research directions.

Ideas for future works

Improving instruction adherence and handling unanticipated model responses remain open research problems.

In deep

IT (Instruction tuning) involves further training LLMs using (INSTRUCTION, OUTPUT) pairs, where INSTRUCTION denotes the human instruction for the model, and OUTPUT denotes the desired output that follows the INSTRUCTION.

The benefits of IT are threefold:

Finetuning an LLM on the instruction dataset bridges the gap between the next-word prediction objective of LLMs and the users’ objective of instruction following;
IT allows for a more controllable and predictable model behavior compared to standard LLMs. The instructions serve to constrain the model’s outputs to align with the desired response characteristics or domain knowledge, providing a channel for humans to intervene with the model’s behaviors;
IT is computationally efficient and can help LLMs rapidly adapt to a specific domain without extensive retraining or architectural changes.

Despite its effectiveness, IT also poses challenges:

Crafting high-quality instructions that properly cover the desired target behaviors is non-trivial: existing instruction datasets are usually limited in quantity, diversity, and creativity;
there has been an increasing concern that IT only improves on tasks that are heavily supported in the IT training dataset ;
there has been an intense criticism that IT only captures surface-level patterns and styles (e.g., the output format) rather than comprehending and learning the task

Datasets

Each instance of the dataset consists of three elements:

a natural language text sequence that specifies the task;
an optional input for supplementary information;
the output

There are two approaches to build a dataset of this kind:

Data integration from annotated natural language datasets. In this approach, (instruction, output) pairs are collected from existing annotated natural language datasets by using templates to transform text-label pairs to (instruction, output) pairs
Generating outputs using LLMs: An alternate way to quickly gather the desired outputs to given instructions is to employ LLMs such as GPT-3.5-Turbo or GPT4 instead of manually collecting the outputs. Instructions can come from two sources: (1) manually collected; or (2) expanded based a small handwritten seed instructions using LLMs. Next, the collected instructions are fed to LLMs to obtain outputs.

A list of datasets is reviewed in the paper.
Instruction tuning represents Stage 1 (SFT) of the modern alignment pipeline. For the full evolution of post-training techniques — including RLHF - Reinforcement Learning from Human Feedback, DPO - Direct Preference Optimization, and RLVF - Reinforcement Learning from Verifiable Feedback — see LLM Training and Alignment Evolution.

References

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