Search Augmented Instruction Learning (2023)

Abstract: Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information. In this work, we propose search-augmented instruction learning (SAIL), which grounds the language generation and instruction following abilities on complex search results generated by in-house and external search engines. With an instruction tuning corpus, we collect search results for each training case from different search APIs and domains, and construct a new search-grounded training set containing (instruction, grounding information, response) triplets. We then fine-tune the LLaMA-7B model on the constructed training set. Since the collected results contain unrelated and disputing languages, the model needs to learn to ground on trustworthy search results, filter out distracting passages, and generate the target response. The search result-denoising process entails explicit trustworthy information selection and multi-hop reasoning, since the retrieved passages might be informative but not contain the instruction-following answer. Experiments show that the fine-tuned SAIL-7B model has a strong instruction-following ability, and it performs significantly better on transparency-sensitive tasks, including open-ended question answering and fact checking.

## introduction large language models (llms) have demonstrated many impressive capabilities, including zero-shot inference and few-shot in-context learning @xcite . recent research has shown that llms benefit from instruction tuning @xcite , and that such instruction-tuned llms significantly outperform plain llms on zero-shot language tasks @xcite . instructiontuned llms have shown an ability to generate both natural and programming languages following natural language guidance and requests. to achieve the same goal, a pretrained llm needs a number of annotated examples as in-context learning prompts. despite their impressive behavior, llms have a number of issues, including obsolescence and nontransparency. understandably, llms are trained with corpora constructed up to a certain time point. with this fixed, pretrained or fine-tuned model, subsequently occurring information cannot appear in any informed generation by the llm. one way to update the knowledge in llms is to re-train the entire model with an updated training corpus. however, this would be costly and time-consuming. in terms of transparency, the predictions of llms are opaque because generations are not grounded on trustworthy sources. it is possible for an llm to generate undesirable language that looks like human-generated text, including misinformation, stereotypes, and toxic language @xcite . without providing legitimate sources for llm generated texts, it is difficult to catch and avoid these undesirable llm behaviors. to overcome these difficulties, a straightforward solution is to connect llms to information retrieval systems, especially commercial search engines. by doing so, the llm can ground its predictions on information retrieved from an upto-date knowledge base, and the sources of the generations would be transparent to users. before llms became large enough to memorize a significant amount of world knowledge, retrievalbased grounding had been heavily studied for opendomain question answering @xcite @xcite . recent llms have also shown the potential of using information retrieval tools, e.g., toolformer (schick (1) from climate change 'certainty' to rapid decline: a timeline of ipcc ... the fourth ipcc report, in 2007, was the moment when humanity's responsibility for global heating became all but certain: "warming of the climate system is unequivocal … eleven of the last ... ## conclusion in this work, we found that disputed and distracting search results can significantly mislead the predictions of large language models. several transparency-sensitive tasks, including opendomain question answering and language checking can be negatively influenced by this phenomenon. to solve this problem, we propose a search-augmented instruction-following large language model with 7b parameters. we construct the first search-augmented instruction-tuning corpus consisting of human-generated instructions, gpt-4 generated responses, and search results generated by a bm25 retriever based on wikipedia and a commercial search engine. we then finetuned the llama-7b language model with the constructed training corpus on academic computational resources. experiments on instruction-following, question answering, and fact/fairness checking show that the search-augmented language model can distill trustworthy and helpful information from all search results and generate high-quality responses, improving both the performance and transparency of instruction-following large language models.