In the past decades, recommender systems have attracted much attention in
both research and industry communities, and a large number of studies have been
devoted to developing effective recommendation models. Basically speaking,
these models mainly learn the underlying user preference from historical
behavior data, and then estimate the user-item matching relationships for
recommendations. Inspired by the recent progress on large language models
(LLMs), we take a different approach to developing the recommendation models,
considering recommendation as instruction following by LLMs. The key idea is
that the preferences or needs of a user can be expressed in natural language
descriptions (called instructions), so that LLMs can understand and further
execute the instruction for fulfilling the recommendation task. Instead of
using public APIs of LLMs, we instruction tune an open-source LLM (3B
Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this
purpose, we first design a general instruction format for describing the
preference, intention, task form and context of a user in natural language.
Then we manually design 39 instruction templates and automatically generate a
large amount of user-personalized instruction data (252K instructions) with
varying types of preferences and intentions. To demonstrate the effectiveness
of our approach, we instantiate the instruction templates into several
widely-studied recommendation (or search) tasks, and conduct extensive
experiments on these tasks with real-world datasets. Experiment results show
that the proposed approach can outperform several competitive baselines,
including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds
light on developing more user-friendly recommender systems, in which users can
freely communicate with the system and obtain more accurate recommendations via
natural language instructions.

该论文提出了一种以自然语言为指令的方法来开发推荐模型，这种方法可以更好地适应用户需求，实现个性化推荐，并在真实数据集上比多个竞争基线模型表现更优。

推荐作为指令跟随： 一个拥有大型语言模型的推荐方法

Recommendation as Instruction Following: A Large Language Model  Empowered Recommendation Approach

Recently, prompt-based methods have achieved significant performance in
few-shot learning scenarios by bridging the gap between language model
pre-training and fine-tuning for downstream tasks. However, existing prompt
templates are mostly designed for sentence-level tasks and are inappropriate
for sequence labeling objectives. To address the above issue, we propose a
multi-task instruction-based generative framework, named InstructionNER, for
low-resource named entity recognition. Specifically, we reformulate the NER
task as a generation problem, which enriches source sentences with
task-specific instructions and answer options, then inferences the entities and
types in natural language. We further propose two auxiliary tasks, including
entity extraction and entity typing, which enable the model to capture more
boundary information of entities and deepen the understanding of entity type
semantics, respectively. Experimental results show that our method consistently
outperforms other baselines on five datasets in few-shot settings.

本文提出了一种基于多任务指令的生成式框架 InstructionNER，用于低资源的命名实体识别。实验结果表明，我们的方法在少样本任务中始终优于其他基线模型。