Prompt engineering for text generation: Difference between revisions

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[[Few-shot prompting]] have a task description, a few examples and then a prompt.

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In few-shot prompting, the model is presented with high-quality demonstrations, including input and desired output, for the target task. This approach enables the model to understand the human intention better and the desired criteria for answers, often resulting in improved performance compared to zero-shot prompting. However, this comes at the expense of increased token consumption and may reach the context length limit for longer input and output texts.

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.<ref name="”111”">Zhao et al. (2021) Calibrate Before Use: Improving Few-Shot Performance of Language Models https://arxiv.org/abs/2102.09690</ref>

Liu et al. (2021) suggested choosing examples that are semantically similar to the test example by employing [[k-nearest neighbors]] (KNN) 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.