Ensuring Reliable Few-Shot Prompt Selection for Language Models (SEO-friendly, Appealing to Humans)

Ensuring Reliable Few-Shot Prompt Selection for Language Models (SEO-friendly, Appealing to Humans)

Introduction:

In this article, Chris Mauck and Jonas Mueller explore the use of the Davinci Large Language Model (LLM) from OpenAI for classifying customer service requests at a large bank. They employ few-shot prompting, a natural language processing technique, to provide the LLM with a limited number of input-output pairs. However, they find that the predictions of the LLM are unreliable due to messy and error-prone real-world data. By using data-centric AI algorithms like Confident Learning, they are able to curate high-quality few-shot examples for more accurate predictions. The authors study a variant of the Banking-77 Dataset and evaluate the performance of the LLM using different prompt templates. Ultimately, they find that removing noisy examples and auto-correcting label issues lead to the best results.

Full Article: Ensuring Reliable Few-Shot Prompt Selection for Language Models (SEO-friendly, Appealing to Humans)

How AI Models Handle Customer Service Requests at a Large Bank

Customer service plays a crucial role in the banking industry, and companies are always looking for ways to improve their response to customer requests. In this article, we explore how the Davinci Large Language Model (LLM) from OpenAI, which powers GPT-3/ChatGPT, can be used to classify the intent of customer service requests at a large bank. We examine the effectiveness of few-shot prompts and the impact of data quality on LLM performance.

The Challenge with Real-World Data

To classify customer service intent, it is common practice to use few-shot prompts. These prompts consist of a limited number of input-output pairs that provide context to the model. However, real-world data is often messy and error-prone, leading to unreliable predictions from the LLM. Even when the prompt template is manually modified to mitigate noisy data, the LLM’s performance in customer service intent classification is only marginally improved.

The Importance of High-Quality Few-Shot Examples

To ensure reliable predictions from LLMs, it is crucial to curate high-quality few-shot examples. Many engineers may not be aware that there are algorithms and software available to help with this data curation process. Algorithmic data curation offers automation, systematic selection, and wide applicability, making it beneficial for general LLM applications beyond intent classification.

The Banking Intent Dataset

In this study, we focus on a variant of the Banking-77 Dataset that contains online banking queries labeled with their corresponding intents. The dataset consists of 50 intent categories, and models are evaluated based on their ability to predict these labels using a fixed test dataset of approximately 500 phrases. A pool of around 1000 labeled phrases is available as candidates for few-shot examples.

Utilizing Few-Shot Prompting

Few-shot prompting, also known as in-context learning, is a valuable technique in NLP. It allows pretrained models like LLMs to perform complex tasks without additional training. For our study, we construct a 50-shot prompt template by randomly selecting one example from each of the 50 intent categories. This prompt template, along with the list of possible classes, is used to instruct the LLM on how to classify each example in the test set.

Baseline Model Performance

Using the 50-shot prompt template, we achieve an accuracy of 59.6% in classifying customer service intent. Although this accuracy is reasonable for a 50-class problem, it falls short of meeting the requirements for a bank’s customer service application.

Data Issues and Their Impact

Upon inspecting the candidate pool of few-shot examples, we discover mislabeled phrases and out-of-scope examples. Data annotation teams are often imperfect, leading to incorrectly labeled examples. These issues can significantly impact LLM performance when used in prompts.

Addressing Noisy Examples

One approach to mitigating the impact of noisy examples is to include a disclaimer warning in the prompt. However, even with this modification, the accuracy only increases to 62%. Another approach is to rely entirely on the LLM’s pretrained knowledge by removing the poor-quality few-shot examples from the prompt. This zero-shot prompting technique improves accuracy to 67.4%, indicating that noisy examples can actually harm model performance.

Automated Correction with Confident Learning

To improve dataset quality, a more complex solution involves manually finding and fixing label issues. However, this can be time-consuming. Alternatively, Cleanlab Studio, a platform that utilizes Confident Learning algorithms, can automatically identify and correct label issues. By effortlessly correcting the data, the accuracy of the LLM using few-shot prompting can be further improved.

Conclusion

Improving the classification of customer service intent in the banking sector is crucial. While few-shot prompts can be effective, they heavily rely on the quality of the few-shot examples. Real-world data often contains noisy and mislabeled examples, impacting the LLM’s performance. Strategies like including warnings in prompts or removing noisy examples entirely can help but don’t provide optimal accuracy. Automated solutions using Confident Learning algorithms offer a smarter way to enhance dataset quality and improve LLM predictions.

Summary: Ensuring Reliable Few-Shot Prompt Selection for Language Models (SEO-friendly, Appealing to Humans)

In this article, authors Chris Mauck and Jonas Mueller explore the use of few-shot prompts in the Davinci Large Language Model from OpenAI for classifying the intent of customer service requests at a large bank. They sourced few-shot examples from a dataset of human-labeled request examples, but found that the resulting predictions were unreliable due to messy and error-prone real-world data. They discovered that using data-centric AI algorithms like Confident Learning to curate high-quality few-shot examples improved the accuracy of the predictions. The article also discusses the issues in the dataset and explores different approaches to mitigating them.

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