Dr.ICL: Demonstration-Retrieved In-context Learning

Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, Vincent Y Zhao

Keywords: In context learning, retrieval augmented language model, chain-of-thoughts prompting, instruction fine-tuning

TL;DR: The study highlights that using relevant demonstrations in large language models, especially for instruction-finetuned LLMs and Chain-of-Thought prompting, enhances performance over random or no demonstrations.

Abstract: In-context learning (ICL), which teaches a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs. While early studies primarily used a fixed or random set of demonstrations for all test queries, recent research suggests that retrieving semantically similar demonstrations to the input from a pool of available demonstrations results in better performance. This work expands the applicability of retrieval-based ICL approaches along several dimensions. We extend the success of retrieval-based ICL to instruction-finetuned LLMs as well as Chain-of-Thought (CoT) prompting. While the prior work utilizes general Large Language Models (LLMs), such as GPT-3, we find that retrieved demonstrations also enhance instruction-finetuned LLMs. This insight implies that training data, despite being exposed during the fine-tuning phase, can still be effectively used through retrieval and in-context demonstrations during testing, resulting in superior outcomes when compared to utilizing no demonstrations or selecting them at random. For CoT, when the demonstrations contain reasoning chains, we get improvements by retrieving based on such chains. Finally, we train a task-specific demonstration retriever that outperforms off-the-shelf retrievers.

Submission Number: 54