Training AI with Human Feedback for Better Summaries: RLHF

Thu, 10 Aug 2023 08:02:30 +1000

Artificial intelligence has become very capable at summarizing text. But current models are still trained indirectly - to mimic human reference summaries, not to directly satisfy users.

Researchers at OpenAI investigated whether getting regular people to compare AI-generated summaries can produce higher quality models.

Their technique worked as follows:

They built a text summarization system based on a neural network language model.
For each piece of text, the system generated multiple summary options.
They showed human evaluators pairs of summaries for the same text and had them pick the better one.
They used these comparisons to train a separate "reward model" to predict which summary humans would prefer.
They then used this reward model to fine-tune the main summarization system, via a reinforcement learning algorithm. The system learns to generate summaries that score highly on the reward model.
By repeating this loop of gathering feedback, training the reward model, and tuning the summarizer, the system improves over time.

The researchers found that optimizing the AI for direct human preferences significantly boosted performance compared to just training it to mimic reference summaries.

With enough feedback data, the AI's summaries surpassed the quality of the original human-written summaries used for training.

The key is the tight feedback loop between users and the system - the AI learns dynamically what people see as a high quality summary.

This technique provides a template for any application where humans can evaluate outputs, like speech or translation. The AI learns to satisfy users, not just match a benchmark.

For business leaders, it shows the value of measuring how well AI fulfills human needs, beyond just technical metrics. As AI advances, aligning it with people may require creative feedback techniques like this.

Source:

arxiv

What is RLHF: Reinforcement Learning from Human Feedback

Tue, 08 Aug 2023 15:19:18 +1000

What is RLHF?

Reinforcement learning from human feedback (RLHF) is an advanced technique to train large AI language models to generate higher quality text outputs. It incorporates direct human feedback into the training loop.

In simple terms, RLHF works by:

Starting with a large pre-trained language model that can generate text snippets from prompts
Systematically collecting data on human preferences by having people compare and rank different AI-generated options from the same prompt. This preference data is used to train a separate "reward model" that will score new AI-generated text.
Using reinforcement learning algorithms to fine-tune and optimize the original language model to maximize the scores from the reward model when generating text, while constraining the model from changing too radically.

The key innovation is creating training data from human comparisons of AI outputs, rather than relying on human-labeled datasets. This allows the AI to learn more complex, subjective attributes like creativity, truthfulness, and harmlessness that are difficult to directly program rules for.

Why does RLHF matter?

RLHF was a pivotal technique used by AI labs like Anthropic and OpenAI to develop large chatbot models with strong language generation abilities like Claude and ChatGPT.

It allows AI text generation to better align with human preferences and values, beyond just predicting the next word accurately. This makes the AI more usable for open-ended real world applications.

However, RLHF requires extensive data, compute resources, and engineering to scale up. There are also core limitations around dependency on ongoing human feedback and balancing performance gains with potential inaccuracies or harms.

Current state and limitations

RLHF remains an active area of research with open challenges. There are some open source tools available but no simple universal implementation.

Key limitations include:

Costly data needs - RLHF requires substantial volumes of human feedback data, which can require extensive paid human labeling.
Potential for poor generalization - Performance is highly dependent on human annotator quality and biases. The AI has no inherent ground truth, so can learn harmful or factually inaccurate behaviors.

Leading AI labs view advancing RLHF capabilities and mitigating limitations as important open problems. This includes developing new algorithms, analyzing model dynamics, and techniques to reduce harms.

RLHF allows cutting edge AI to better align with human values but still has major challenges around data, harms, and implementation complexity. It shows promise for the future with more research.

Now Next Later AI - Blog #RLHF

Training AI with Human Feedback for Better Summaries: RLHF

What is RLHF: Reinforcement Learning from Human Feedback