Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.g. FastText) is widely used for efficient online inference. However, the representation ability of the FastText model is insufficient, resulting in poor classification performance, especially on some low-frequency queries and tailed categories. Using a deeper and more complex model (e.g. BERT) is an effective solution, but it will cause a higher online inference latency and more expensive computing costs. Thus, how to juggle both inference efficiency and classification performance is obviously of great practical importance. To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints. Specifically, we propose to train an offline BERT model to retrieve more potentially relevant data. Benefiting from its powerful semantic representation, more relevant labels not exposed in the historical data will be added into the training set for better FastText model training. Moreover, a novel distribution-diverse multi-expert learning strategy is proposed to further improve the mining ability of relevant data. By training multiple BERT models from different data distributions, it can respectively perform better at high, middle, and low-frequency search queries. The model ensemble from multi-distribution makes its retrieval ability more powerful. We have deployed two versions of this framework in JD search, and both offline experiments and online A/B testing from multiple datasets have validated the effectiveness of the proposed approach.

Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since it needs to capture semantic information from short and ambiguous queries and often requires massive task-specific labeled data. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks because they can extract general semantic information from large-scale corpora. Therefore, there are unprecedented opportunities to adopt PLMs for query understanding. However, there is a gap between the goal of query understanding and existing pre-training strategies -- the goal of query understanding is to boost search performance while existing strategies rarely consider this goal. Thus, directly applying them to query understanding is sub-optimal. On the other hand, search logs contain user clicks between queries and urls that provide rich users' search behavioral information on queries beyond their content. Therefore, in this paper, we aim to fill this gap by exploring search logs. In particular, to incorporate search logs into pre-training, we first construct a query graph where nodes are queries and two queries are connected if they lead to clicks on the same urls. Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph. In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries. Extensive experiments on various query understanding tasks have demonstrated the effectiveness of the proposed framework.

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The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formalize under the unified framework of Generative Engines (GEs), has the potential to generate accurate and personalized responses, and is rapidly replacing traditional search engines like Google and Bing. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them with the help of LLMs. While this shift significantly improves \textit{user} utility and \textit{generative search engine} traffic, it results in a huge challenge for the third stakeholder -- website and content creators. Given the black-box and fast-moving nature of Generative Engines, content creators have little to no control over when and how their content is displayed. With generative engines here to stay, the right tools should be provided to ensure that creator economy is not severely disadvantaged. To address this, we introduce Generative Engine Optimization (GEO), a novel paradigm to aid content creators in improving the visibility of their content in Generative Engine responses through a black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries. Through rigorous evaluation, we show that GEO can boost visibility by up to 40\% in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific methods. Our work opens a new frontier in the field of information discovery systems, with profound implications for generative engines and content creators.

Benchmarking the performance of information retrieval (IR) methods are mostly conducted with a fixed set of documents (static corpora); in realistic scenarios, this is rarely the case and the document to be retrieved are constantly updated and added. In this paper, we focus on conducting a comprehensive comparison between two categories of contemporary retrieval systems, Dual Encoders (DE) and Generative Retrievals (GR), in a dynamic scenario where the corpora to be retrieved is updated. We also conduct an extensive evaluation of computational and memory efficiency, crucial factors for IR systems for real-world deployment. Our results demonstrate that GR is more adaptable to evolving knowledge (+13-18% on the StreamingQA Benchmark), robust in handling data with temporal information (x 10 times), and efficient in terms of memory (x 4 times), indexing time (x 6 times), and inference flops (x 10 times). Our paper highlights GR's potential for future use in practical IR systems.

Query expansion has been proved to be effective in improving recall and precision of first-stage retrievers, and yet its influence on a complicated, state-of-the-art cross-encoder ranker remains under-explored. We first show that directly applying the expansion techniques in the current literature to state-of-the-art neural rankers can result in deteriorated zero-shot performance. To this end, we propose GFF, a pipeline that includes a large language model and a neural ranker, to Generate, Filter, and Fuse query expansions more effectively in order to improve the zero-shot ranking metrics such as nDCG@10. Specifically, GFF first calls an instruction-following language model to generate query-related keywords through a reasoning chain. Leveraging self-consistency and reciprocal rank weighting, GFF further filters and combines the ranking results of each expanded query dynamically. By utilizing this pipeline, we show that GFF can improve the zero-shot nDCG@10 on BEIR and TREC DL 2019/2020. We also analyze different modelling choices in the GFF pipeline and shed light on the future directions in query expansion for zero-shot neural rankers.

We are investigating the broader concept of using AI-based generative music systems to generate training data for Music Information Retrieval (MIR) tasks. To kick off this line of work, we ran an initial experiment in which we trained a genre classifier on a fully artificial music dataset created with MusicGen. We constructed over 50 000 genre- conditioned textual descriptions and generated a collection of music excerpts that covers five musical genres. Our preliminary results show that the proposed model can learn genre-specific characteristics from artificial music tracks that generalise well to real-world music recordings.

Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a new approach for end-to-end document retrieval that directly generates document identifiers given an input query. Techniques for designing effective, high-quality document IDs remain largely unexplored. We introduce ACID, in which each document's ID is composed of abstractive keyphrases generated by a large language model, rather than an integer ID sequence as done in past work. We compare our method with the current state-of-the-art technique for ID generation, which produces IDs through hierarchical clustering of document embeddings. We also examine simpler methods to generate natural-language document IDs, including the naive approach of using the first k words of each document as its ID or words with high BM25 scores in that document. We show that using ACID improves top-10 and top-20 accuracy by 15.6% and 14.4% (relative) respectively versus the state-of-the-art baseline on the MSMARCO 100k retrieval task, and 4.4% and 4.0% respectively on the Natural Questions 100k retrieval task. Our results demonstrate the effectiveness of human-readable, natural-language IDs in generative retrieval with LMs. The code for reproducing our results and the keyword-augmented datasets will be released on formal publication.

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. Knowledge distillation offers an opportunity to compress a large language model to a small one, in order to reach a reasonable latency-performance tradeoff. However, for scenarios where the number of requests (e.g., queries submitted to a search engine) is highly variant, the static tradeoff attained by the compressed language model might not always fit. Once a model is assigned with a static tradeoff, it could be inadequate in that the latency is too high when the number of requests is large or the performance is too low when the number of requests is small. To this end, we propose an elastic language model (ElasticLM) that elastically adjusts the tradeoff according to the request stream. The basic idea is to introduce a compute elasticity to the compressed language model, so that the tradeoff could vary on-the-fly along scalable and controllable compute. Specifically, we impose an elastic structure to enable ElasticLM with compute elasticity and design an elastic optimization to learn ElasticLM under compute elasticity. To serve ElasticLM, we apply an elastic schedule. Considering the specificity of information retrieval, we adapt ElasticLM to dense retrieval and reranking and present ElasticDenser and ElasticRanker respectively. Offline evaluation is conducted on a language understanding benchmark GLUE; and several information retrieval tasks including Natural Question, Trivia QA, and MS MARCO. The results show that ElasticLM along with ElasticDenser and ElasticRanker can perform correctly and competitively compared with an array of static baselines. Furthermore, online simulation with concurrency is also carried out. The results demonstrate that ElasticLM can provide elastic tradeoffs with respect to varying request stream.

Query expansion is a commonly-used technique in many search systems to better represent users' information needs with additional query terms. Existing studies for this task usually propose to expand a query with retrieved or generated contextual documents. However, both types of methods have clear limitations. For retrieval-based methods, the documents retrieved with the original query might not be accurate enough to reveal the search intent, especially when the query is brief or ambiguous. For generation-based methods, existing models can hardly be trained or aligned on a particular corpus, due to the lack of corpus-specific labeled data. In this paper, we propose a novel Large Language Model (LLM) based mutual verification framework for query expansion, which alleviates the aforementioned limitations. Specifically, we first design a query-query-document generation pipeline, which can effectively leverage the contextual knowledge encoded in LLMs to generate sub-queries and corresponding documents from multiple perspectives. Next, we employ a mutual verification method for both generated and retrieved contextual documents, where 1) retrieved documents are filtered with the external contextual knowledge in generated documents, and 2) generated documents are filtered with the corpus-specific knowledge in retrieved documents. Overall, the proposed method allows retrieved and generated documents to complement each other to finalize a better query expansion. We conduct extensive experiments on three information retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO. The results demonstrate that our method outperforms other baselines significantly.