What is LLM RLHF ?
How LLMs are augmented by Human Reviewers?
Human Reviewers’ Skills
ChatGPT has made AI a sensation among all enterprises and the everyday common man. It surpassed the user base to 1M within a week and broke many records. Even Bill gates compared the success of ChatGPT to the most important milestones such as the birth of the internet, personal computers, and GUI. “This will change our world,” he continued. The applications of generative AI like OpenAI’s ChatGPT could improve office efficiency, drafting invoices and letters, Gates said in a podcast conversation with the German-language business paper.
Large Language Models (LLMs) are becoming increasingly popular for natural language processing(NLP) tasks, such as language translation, text summarization, and text generation. While LLMs have shown remarkable performance in these tasks, they are not without their limitations and potential drawbacks. LLMs are often trained on large datasets of text, which may contain biased or sensitive information. If these biases are not corrected, the LLM may perpetuate or amplify them in its language generation. Human reviewers are needed to evaluate the quality and appropriateness of LLM-generated text and to correct any errors or biases that are identified. LLMs are not yet capable of understanding the nuances and complexities of human language in the same way that humans can. Human reviewers are needed to ensure that LLM-generated text is understandable, coherent, and consistent with the intended meaning.
LLM reinforcement learning human feedback (LLM RHLF) refers to a method of training large language models (LLMs) using a combination of reinforcement learning and human feedback. Reinforcement learning is a type of machine learning where an algorithm learns to make decisions by trial and error. In the context of LLMs, reinforcement learning can be used to optimize the performance of the LLM by providing it with feedback on the quality of its generated text.
Overview of ChatGPT’s training protocol, from the data collection to the RL part. Source: OpenAI’s ChatGPT blogpost
Human feedback, on the other hand, is provided by human reviewers who evaluate the quality and appropriateness of LLM-generated text. Human feedback can be used to correct any errors or biases in the LLM-generated text and to improve the overall performance of the LLM.
LLM RHLF combines these two approaches by training the LLM using reinforcement learning and then using human feedback to evaluate and improve the quality of the LLM-generated text. This approach can help to improve the accuracy, quality, and appropriateness of LLM-generated text and to reduce any biases or errors that may be present in the training data.
There are several typical human review tasks for Large Language Models (LLMs):
Human reviewers for Large Language Models (LLMs) need to have specific skills and experience to effectively evaluate and correct LLM-generated text. Here are some of the key skills and experience needed:
At Objectways, we have a team of highly skilled human
reviewers with education in creative writing, literature, journalism, and
arts. They have deep expertise in natural language processing, and the
ability to evaluate and correct the output of large language models (LLMs). Our team
is experienced in working with a wide range of LLMs summarization, prompt
generation, creative writing(movie, story, poem, code, essays, etc.),
question & answer generation and is equipped with the knowledge and tools necessary
to identify and correct any errors, biases, or inconsistencies in LLM-generated
text. We have worked with companies who have generated foundational LLMs and
improved on a variety of domains such as legal, chatbots, insurance, finance,
medical, and hospitality to name a few.
In summary, while LLMs are a powerful tool for natural language processing tasks, they are not a replacement for skilled human reviewers. Human reviewers are needed to ensure the accuracy, appropriateness, and quality of LLM-generated text, and to correct any errors or biases that may arise. Please contact firstname.lastname@example.org to harness the power of LLMs