Agent Spotlight: Nisha Kassam, a Lightning Fast Learner
January 13, 2023
March 21, 2023
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The rapid advancements of AI have led to the development of more sophisticated and intelligent systems. One such approach that has gained traction is Reinforcement Learning from Human Feedback (RLHF), which combines the best of human expertise and AI capabilities.
In this blog, we'll break down RLHF, understand the core concepts of human-in-the-loop AI, and explore some practical examples where this technique is applied.
Pioneered by OpenAI, Reinforcement Learning from Human Feedback (RLHF) is a subset of reinforcement learning that incorporates human input to improve the learning process. The primary idea behind RLHF is to blend the adaptive nature of RL algorithms with the expertise and intuition of humans, effectively creating a human-in-the-loop AI system.
In traditional RL, an agent learns by interacting with its environment and receiving rewards or penalties based on its actions. However, in RLHF, the agent learns from human feedback, which can come in the form of explicit evaluations, demonstrations, or corrections.
This human-in-the-loop approach allows the agent to acquire more nuanced insights and adapt its behavior to better suit the task at hand.
Reinforcement Learning from Human Feedback is important because it addresses several key challenges faced by AI systems, particularly reinforcement learning agents. By incorporating human feedback, RLHF helps improve the performance and learning of these agents in a variety of ways.
One significant advantage of RLHF is that it enables better learning from limited data. Real-world data can be scarce, expensive, or difficult to obtain, and RLHF allows AI systems to make the most of this limited data by utilizing human insights to guide learning. This can lead to more accurate and efficient learning processes.
Additionally, RLHF helps address the challenge of reward specification. In traditional reinforcement learning, defining a suitable reward function can be difficult, as it often requires anticipating all possible scenarios and outcomes. By leveraging human feedback, RLHF helps create a more effective and adaptive reward system that aligns better with desired outcomes and behaviors.
RLHF makes AI systems safer, too. Human feedback teaches AI agents to avoid unsafe or undesirable actions, which is particularly important when deploying AI systems in sensitive or high-stakes environments.
RLHF also contributes to the development of more generalizable AI systems. Incorporating feedback from human experts, AI learns to adapt to novel situations and environments that were not encountered during their training, leading to more robust and versatile AI systems.
In RLHF, several techniques have been developed to incorporate human insights into the learning process of AI systems.
Here are two basic, high-level examples of different techniques.
One technique used in RLHF is the collection of comparison data, where humans rank different actions or trajectories taken by the AI agent in terms of their desirability. This comparison data can then be used to create a reward model that guides the AI agent's learning.
For instance, in a simulated driving scenario, an AI agent might choose different paths to reach a destination. The human expert would then compare these paths and rank them based on factors such as safety, efficiency, and adherence to traffic rules. By incorporating this human feedback, the AI agent can learn to make better driving decisions in the future.
Another is reward modeling. Reward modeling is the process of learning a reward function from data, in this case human feedback, to guide an AI agent's behavior and decision-making toward desired outcomes.
Why isn’t RLHF more popular as a method of training AI? The answer is that it’s really hard to scale.
RLHF is difficult to scale mainly because human input can be time-consuming, labor-intensive, and subject to the availability and expertise of human evaluators. From most vendors, this type of work isn’t cost-effective.
Invisible is different. We help the most innovative companies in AI scale RLHF with an agile recruiting machine and cost-per-unit pricing model.
For one major AI platform, Invisible overcame scale limitations when other contractors couldn’t by recruiting over 200 skilled operators in 3 months, completing over 5,000 comparison tasks weekly, and beating quality benchmarks by 10%.
Now, we’re providing 3,000+ hours of high-quality RLHF for that client every day.
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The rapid advancements of AI have led to the development of more sophisticated and intelligent systems. One such approach that has gained traction is Reinforcement Learning from Human Feedback (RLHF), which combines the best of human expertise and AI capabilities.
In this blog, we'll break down RLHF, understand the core concepts of human-in-the-loop AI, and explore some practical examples where this technique is applied.
Pioneered by OpenAI, Reinforcement Learning from Human Feedback (RLHF) is a subset of reinforcement learning that incorporates human input to improve the learning process. The primary idea behind RLHF is to blend the adaptive nature of RL algorithms with the expertise and intuition of humans, effectively creating a human-in-the-loop AI system.
In traditional RL, an agent learns by interacting with its environment and receiving rewards or penalties based on its actions. However, in RLHF, the agent learns from human feedback, which can come in the form of explicit evaluations, demonstrations, or corrections.
This human-in-the-loop approach allows the agent to acquire more nuanced insights and adapt its behavior to better suit the task at hand.
Reinforcement Learning from Human Feedback is important because it addresses several key challenges faced by AI systems, particularly reinforcement learning agents. By incorporating human feedback, RLHF helps improve the performance and learning of these agents in a variety of ways.
One significant advantage of RLHF is that it enables better learning from limited data. Real-world data can be scarce, expensive, or difficult to obtain, and RLHF allows AI systems to make the most of this limited data by utilizing human insights to guide learning. This can lead to more accurate and efficient learning processes.
Additionally, RLHF helps address the challenge of reward specification. In traditional reinforcement learning, defining a suitable reward function can be difficult, as it often requires anticipating all possible scenarios and outcomes. By leveraging human feedback, RLHF helps create a more effective and adaptive reward system that aligns better with desired outcomes and behaviors.
RLHF makes AI systems safer, too. Human feedback teaches AI agents to avoid unsafe or undesirable actions, which is particularly important when deploying AI systems in sensitive or high-stakes environments.
RLHF also contributes to the development of more generalizable AI systems. Incorporating feedback from human experts, AI learns to adapt to novel situations and environments that were not encountered during their training, leading to more robust and versatile AI systems.
In RLHF, several techniques have been developed to incorporate human insights into the learning process of AI systems.
Here are two basic, high-level examples of different techniques.
One technique used in RLHF is the collection of comparison data, where humans rank different actions or trajectories taken by the AI agent in terms of their desirability. This comparison data can then be used to create a reward model that guides the AI agent's learning.
For instance, in a simulated driving scenario, an AI agent might choose different paths to reach a destination. The human expert would then compare these paths and rank them based on factors such as safety, efficiency, and adherence to traffic rules. By incorporating this human feedback, the AI agent can learn to make better driving decisions in the future.
Another is reward modeling. Reward modeling is the process of learning a reward function from data, in this case human feedback, to guide an AI agent's behavior and decision-making toward desired outcomes.
Why isn’t RLHF more popular as a method of training AI? The answer is that it’s really hard to scale.
RLHF is difficult to scale mainly because human input can be time-consuming, labor-intensive, and subject to the availability and expertise of human evaluators. From most vendors, this type of work isn’t cost-effective.
Invisible is different. We help the most innovative companies in AI scale RLHF with an agile recruiting machine and cost-per-unit pricing model.
For one major AI platform, Invisible overcame scale limitations when other contractors couldn’t by recruiting over 200 skilled operators in 3 months, completing over 5,000 comparison tasks weekly, and beating quality benchmarks by 10%.
Now, we’re providing 3,000+ hours of high-quality RLHF for that client every day.
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
