Methods, Applications, and Directions of Learning-to-Rank in NLP Research (2024)

Abstract: Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web search and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.

## introduction ranking, i.e., ordering according to some property, is a central problem for many natural language processing (nlp) and information retrieval (ir) tasks such as search, question answering, document summarization, and machine translation. while nlp and ir tasks overlap, generally speaking in ir ranking problems are query-based (e.g. search, qa), while this is not necessarily true for nlp tasks. learning-to-rank (ltr) is the process of applying machine learning methods to the task of ranking, i.e., to learn how to order elements in a sample from a data distribution. this is in contrast to performing the ranking using non-learning approaches, e.g. rule-based heuristics. ltr is commonly treated as a supervised learning problem, although research on unsupervised methods and reinforcement learning for ltr also exists @xcite . in this paper we focus on the formal background of ltr and the most widely-used supervised methods. we also discuss the increasing use of large language models (llms) for this task, and what we expect for the future of ltr in nlp and machine learning more broadly. an nlp problem can be framed as a ranking problem when multiple candidate solutions are present and the top k options are considered to get the final solution. this general definition fits a wide number of scenarios. for example: (1) in classification, one may set @xmath0. when the number of classes is large, or in multi-label classification scenarios, a ranking would sometimes be more suitable than choosing the most likely class. (2) in machine translation the best possible translation(s) may be chosen from a list of generated translations. discussion around ltr typically focuses on ir (e.g., web search) tasks, but many other use cases exist within nlp, as these examples show. in information retrieval tasks, ltr is generally applied to relevance ranking, where there is a query, and a list of instances related the query which need to be retrieved and ranked. however, in ltr for many nlp tasks the query is optional, and the core problem is to learn to rank a list of instances with respect to some property of the list items (e.g., ranking a set of essays based on text quality), rather than the (properties of) the query as in relevance ranking. further, ltr is also sometimes used as an intermediate step in several nlp tasks (e.g., in sequence tagging, to rank the possible tags for a given token). three book-length works on ltr exist, to our knowledge @xcite @xcite . while the first two focused on the defining the problem and described commonly used methods, @xcite is about how recent neural network architectures can be applied for ltr. all three books primarily focused on the methods themselves and not on specific use cases within nlp. additionally, there is little discussion on evaluation and almost none on statistical significance testing for ltr in these three books. this paper addresses this gap, and provides some guidelines on: for a given task to be framed as a ranking problem, the data is often partitioned into groupings, usually corresponding to a query. so, each grouping contains a query and a set of items to rank related to the query. in the absence of an explicit query, groupings still exist and are used during training and evaluation to designate appropriate aggregations for metric calculation (e.g. averaging over groupings) and for splitting into appropriate train-test-validation sets. in this section, we will discuss some of the commonly used loss functions, supervised learning methods, and evaluation measures. ## overview of ltr applications in nlp in previous surveys, ranking approaches have been separated as ranking creation (ordering according to a criteria, with or without a query) vs. ranking aggregation (combining previously-computed rankings) @xcite , or re-ranking (of a previously computed ranking) vs. direct ranking, sometimes called dense ranking or dense retrieval @xcite . in this paper, we distinguish the applications based on whether the ranking is done when there is a query/reference and a set of items to be ranked in terms of relevance to the query, i.e. ranking with a query (section 3.1) vs. when there is no explicit query, only a list of items to be ranked , i.e. ranking without a query(section 3.2) foot_9 . aligning with the growing efforts in the nlp community on studying and expanding multilingual support for nlp applications, we note the multilingual coverage of ltr use cases within nlp where possible. ## significance testing as this survey aims to guide nlp researchers and practitioners, we consider it important to discuss not only how to implement and evaluate ranking, but also how to reliably compare different methods. therefore, in this section, we present an overview of significance testing methods, before analyzing the actual usage of such methods in the papers we surveyed and providing recommendations. ## conclusions and future directions this survey shows a snapshot of ltr methods and current practices in the use of ltr in nlp, and provides guidance and resource pointers for significance testing, an under-practiced element of evaluation. our key insights so far are summarized below: future directions: there has been growing interest in using llms as zero-shot rankers (see section 2.4 for a discussion), following the current trend of using large language models as natural language apis. this strand of research has been primarily focused on (re-)ranking for information retrieval and question answering. we expect this trend to continue, and hyphothesize that new use cases within nlp could emerge for ranking and learning-to-rank. the information retrieval community has been working on increasing the diversity of rankings @xcite , which could be relevant for nlp problems that rely on sampling techniques and diverse text generation (e.g., machine translation, keyphrase generation). there is also emerging research on how neural ranking models can benefit from traditional ir or ltr methods @xcite , and equivalent ideas on the relevance of traditional nlp to ranking may emerge. we hope to see more research applying significance testing across ltr in nlp and more multilingual ltr use cases in future.