  In-context learning (ICL), teaching 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 by
demonstrating that even simple word-overlap similarity measures such as BM25
outperform randomly selected demonstrations. Furthermore, we extend the success
of retrieval-based ICL to instruction-finetuned LLMs as well as
Chain-of-Thought (CoT) prompting. For instruction-finetuned LLMs, we find that
although a model has already seen the training data at training time,
retrieving demonstrations from the training data at test time yields better
results compared to using no demonstrations or random demonstrations. Last but
not least, we train a task-specific demonstration retriever that outperforms
off-the-shelf retrievers.
