DataHour: Reducing ChatGPT Hallucinations by 80%

Introduction

Natural Language Processing (NLP) models have become increasingly popular in recent years, with applications ranging from chatbots to language translation. However, one of the biggest challenges in NLP is reducing ChatGPT hallucinations or incorrect responses generated by the model. In this article, we will discuss the techniques and challenges involved in reducing hallucinations in NLP models.

Observability, Tuning, and Testing

The first step in reducing hallucinations is to improve the observability of the model. This involves building feedback loops to capture user feedback and model performance in production. Tuning involves improving poor responses by adding more data, correcting retrieval issues, or changing prompts. Testing is necessary to ensure changes improve results and do not cause regressions. The challenges faced in observability include customers sending screenshots of bad responses, leading to frustration. To address this, logs can be monitored daily using data ingestion and secret code.

Debugging and Tuning a Language Model

The process of debugging and tuning a language model involves understanding the model input and response. To debug, logging is necessary to identify the raw prompt and filter it down to specific chunks or references. The logs need to be actionable and easy to understand for anyone. Tuning involves determining how many documents should be fed into the model. Default numbers are not always accurate, and a similarity search may not yield the correct answer. The goal is to figure out why something went wrong and how to fix it.

Optimizing OpenAI Embeddings

Developers of a vector database query application faced challenges in optimizing the performance of the OpenAI embeddings used in the application. The first challenge was determining the optimal number of documents to pass to the model, which was addressed by controlling the chunking strategy and introducing a controllable hyperparameter for the number of documents.

The second challenge was prompt variation, which was addressed using an open-source library called Better Prompt that evaluates the performance of different prompt versions based on perplexity. The third challenge was improving the results from the OpenAI embeddings, which were found to perform better than sentence transformers in multilingual scenarios.

Techniques in AI Development

The article discusses three different techniques used in AI development. The first technique is perplexity, which is used to evaluate the performance of a prompt on a given task. The second technique is building a package that allows users to test different prompt strategies easily. The third technique is running an index, which involves updating the index with additional data when something is missing or not ideal. This allows for more dynamic handling of questions.

Using GPT-3 API to Calculate Perplexity

The speaker discusses their experience with using the GPT-3 API to calculate perplexity based on a query. They explain the process of running a prompt through the API and returning the log probabilities for the best next token. They also mention the possibility of fine-tuning a large language model to imitate a particular writing style, rather than embedding new information.

Evaluating Responses to Multiple Questions

The text discusses the challenges of evaluating responses to 50+ questions at a time. Manually grading every response takes a lot of time, so the company considered using an auto-evaluator. However, a simple yes/no decision framework was insufficient because there are multiple reasons why an answer may not be correct. The company broke down the evaluation into different components, but found that a single run of the auto-evaluator was erratic and inconsistent. To solve this, they ran multiple tests per question and classified the responses as perfect, almost perfect, incorrect but containing some correct information, or completely incorrect.

Reducing Hallucinations in NLP Models

The speaker discusses their process for reducing hallucinations in natural language processing models. They broke down the decision-making process into four categories and used an auto feature for the 50 plus category. They also rolled out the evaluation process into the core product, allowing for evaluations to be run and exported to CSB. The speaker mentions a GitHub repo for more information on the project. They then discuss the steps they took to reduce hallucinations, including observability, tuning, and testing. They were able to reduce the hallucination rate from 40% to sub 5%.

Conclusion

Reducing ChatGPT hallucinations in NLP models is a complex process that involves observability, tuning, and testing. Developers must also consider prompt variation, optimizing embeddings, and evaluating responses to multiple questions. Techniques such as perplexity, building a package for testing prompt strategies, and running an index can also be useful in AI development. The future of AI development lies in small, private, or task-specific elements.

Key Takeaways

• Reducing ChatGPT hallucinations in NLP models involves observability, tuning, and testing.
• Developers must consider prompt variation, optimizing embeddings, and evaluating responses to multiple questions.
• Techniques such as perplexity, building a package for testing prompt strategies, and running an index can also be useful in AI development.
• The future of AI development lies in small, private, or task-specific elements.

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