Prompt engineering for text generation: Difference between revisions

Zhao et al. (2021) investigated few-shot classification using LLMs, specifically GPT-3. They identified several biases that contribute to high variance in performance: (1) majority label bias, (2) recency bias, and (3) common token bias. To address these biases, they proposed a method to calibrate label probabilities output by the model to be uniform when the input string is N/A.

Liu et al. (2021) suggested choosing examples that are semantically similar to the test example by employing nearest neighbor (NN) clustering in the embedding space.

Su et al. (2022) proposed a graph-based approach to select a diverse and representative set of examples: (1) construct a directed graph based on the cosine similarity between samples in the embedding space (e.g., using SBERT or other embedding models), and (2) start with a set of selected samples and a set of remaining samples, scoring each sample to encourage diverse selection.

Rubin et al. (2022) suggested training embeddings through contrastive learning specific to one training dataset for in-context learning sample selection. This approach measures the quality of an example based on a conditioned probability assigned by the language model.

Zhang et al. (2022) explored using Q-Learning for sample selection in LLM training.

Diao et al. (2023) proposed identifying examples with high disagreement or entropy among multiple sampling trials based on uncertainty-based active learning. These examples can then be annotated and used in few-shot prompts.