summarize from feedback

We have hosted the application summarize from feedback in order to run this application in our online workstations with Wine or directly.

Quick description about summarize from feedback:

The summarize-from-feedback repository implements the methods from the paper �Learning to Summarize from Human Feedback�. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling component, and the reinforcement learning (or preference-based fine-tuning) phase. The repo also includes utilities for dataset handling, modeling architectures, inference, and evaluation. Because the codebase is experimental, parts of it may not run out-of-box depending on dependencies or environment, but it remains a canonical reference for how to implement summarization via human feedback.

Features:

• Supervised baseline summarization model to initialize performance
• Reward model trained from human comparisons of summary pairs
• Preference-based fine-tuning / RL stage to optimize summarizer toward human judgments
• Dataset handling modules (loading, comparisons, splits)
• Inference and evaluation scripts to generate and score summaries
• Architecture layout files (e.g. model_layout.py) supporting modular model definitions

Programming Language: Python.
Categories: