FLEx: Language Modeling with Few-shot Language Explanations
Image generated by Gemini AI
Researchers have developed FLEx (Few-shot Language Explanations), a method that enhances language model performance by using a small set of carefully curated explanatory examples. By clustering model errors and summarizing effective corrections, FLEx improves accuracy on tasks like math problem solving and question answering without altering model weights. Evaluated on datasets like CounterBench and GSM8K, FLEx significantly outperformed traditional chain-of-thought prompting, reducing errors by up to 83%. This approach could streamline the correction process in domains requiring expert input.
FLEx Introduces Few-shot Language Explanations to Improve Language Model Accuracy
A new approach, FLEx (Few-shot Language Explanations), aims to enhance the accuracy of language models by utilizing minimal explanatory examples. FLEx identifies and selects representative errors made by the model through embedding-based clustering methods and verifies associated explanations to correct these mistakes. The result is a prompt prefix added at inference time, guiding the model to avoid similar errors in future inputs without changing its underlying weights.
Evaluation and Performance
FLEx was evaluated using three datasets: CounterBench, GSM8K, and ReasonIF. Results show FLEx consistently outperformed the traditional chain-of-thought (CoT) prompting approach, reducing up to 83% of the errors that persist with CoT prompting.
Related Topics:
📰 Original Source: https://arxiv.org/abs/2601.04157v1
All rights and credit belong to the original publisher.