Conifer: A Dataset for Improving Complex Constrained Instruction-Following Ability of Large Language Models
Authors: Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang, Baohua Dong, Lin Ran, Ruohui Huang


Reviewer paper summaries: The paper discusses a new dataset called Conifer, designed to improve how well large language models (LLMs) follow instructions with complex requirements. Conifer is created using GPT-4 which allowed them to refine the data through a series of LLM-driven processes. The dataset itself is structured to present instructions in a way that starts with simpler examples and gradually progresses to more challenging ones. This is similar to how curriculum learning works, where students learn easier concepts before moving on to harder ones.
Summary of strengths: The paper addresses a recognized shortcoming of LLMs - their difficulty in following instructions with multiple constraints. 

Introducing Conifer dataset: The creation of a new, specifically designed dataset (Conifer) to tackle this challenge is a significant contribution.

Progressive learning scheme: Mimicking curriculum learning by incorporating an easy-to-hard progression in the dataset addresses the learning process effectively.Focus on specific LLM (GPT-4): The paper primarily focuses on improving GPT-4, and it's unclear how widely applicable the Conifer dataset is to training other LLMs with different architectures.


Lack of details on "process feedback": The paper mentions a progressive learning scheme with "learning from process feedback.".It's unclear what metrics are used to measure this feedback and how it's fed back into the training process. More information on how this feedback is incorporated would be beneficial.



How did GPT-4 identify and filter out unsuitable examples?

The paper talks about complex constraints but doesn't provide concrete examples within the dataset. It would be helpful to see illustrations of the range of complexity addressed by Conifer.


Generalizability beyond GPT-4: While Conifer shows improvement in GPT-4 performance, it's unclear how well the dataset generalizes to train other LLM architectures. More details on the dataset's structure and its independence from specific LLM features would be useful.
Summary of weaknesses: 1. Focus on specific LLM (GPT-4): The paper primarily focuses on improving GPT-4, and it's unclear how widely applicable the Conifer dataset is to training other LLMs with different architectures. More details on the dataset's structure and its independence from specific LLM features would be useful.

2. Lack of details on "process feedback": The paper mentions a progressive learning scheme with "learning from process feedback.".It's unclear what metrics are used to measure this feedback and how it's fed back into the training process. More information on how this feedback is incorporated would be beneficial.

3. The paper talks about complex constraints but doesn't provide concrete examples within the dataset. It would be helpful to see illustrations of the range of complexity addressed by Conifer.
Comments, suggestions and typos: How did GPT-4 identify and filter out unsuitable examples?
Overall assessment scores: 3