Marc Najork
Marc Najork is a Distinguished Research Scientist at Google DeepMind. Previously, he was a Senior Director of Research Engineering at Google Research. Before joining Google in 2014, Marc was a Principal Researcher at Microsoft Research (2001-2014) and prior to that a Researcher at the DEC/Compaq Systems Researcher Center (1993-2001). Marc earned a Ph.D. in Computer Science from the University of Illinois. He is an AAAS Fellow, an ACM Fellow, an IEEE Fellow and a member of the SIGIR Academy. His service activities include Editor-in-Chief of the ACM Transactions on the Web (2011-2014), news board co-chair of the Communications of the ACM (2008-2014), member of the ACM Publications Board (since 2020), conference chair of WSDM 2008, and program co-chair of WWW 2004, WWW 2021 and WSDM 2025.
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Knowledge Distillation with Perturbed Loss: From a Vanilla Teacher to a Proxy Teacher
Rongzhi Zhang
Chao Zhang
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), ACM, pp. 4278 - 4289
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Knowledge distillation is a popular technique to transfer knowledge from a large teacher model to a small student model. Typically, the student learns to imitate the teacher by minimizing the KL divergence of its output distribution with the teacher's output distribution. In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution. Therefore, forcing the student to blindly imitate the unreliable teacher output distribution leads to inferior performance. To this end, we propose a novel knowledge distillation objective PTLoss by first representing the vanilla KL-based distillation loss function via a Maclaurin series and then perturbing the leading-order terms in this series. This perturbed loss implicitly transforms the original teacher into a proxy teacher with a distribution closer to the ground truth distribution. We establish the theoretical connection between this "distribution closeness'' and the student model generalizability, which enables us to select the PTLoss's perturbation coefficients in a principled way. Extensive experiments on six public benchmark datasets demonstrate the effectiveness of PTLoss with teachers of different scales.
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End-to-End Query Term Weighting
Karan Samel
Swaraj Khadanga
Wensong Xu
Xingyu Wang
Kashyap Kolipaka
Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '23) (2023)
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Bag-of-words based lexical retrieval systems are still the most commonly used methods for real-world search applications. Recently deep learning methods have shown promising results to improve this retrieval performance but are expensive to run in an online fashion, non-trivial to integrate into existing production systems, and might not generalize well in out-of-domain retrieval scenarios. Instead, we build on top of lexical retrievers by proposing a Term Weighting BERT (TW-BERT) model. TW-BERT learns to predict the weight for individual n-gram (e.g., uni-grams and bi-grams) query input terms. These inferred weights and terms can be used directly by a retrieval system to perform a query search. To optimize these term weights, TW-BERT incorporates the scoring function used by the search engine, such as BM25, to score query-document pairs. Given sample query-document pairs we can compute a ranking loss over these matching scores, optimizing the learned query term weights in an end-to-end fashion. Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing production applications, whereas existing deep learning based search methods would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion. We show that TW-BERT improves retrieval performance over strong term weighting baselines within MSMARCO and in out-of-domain retrieval on TREC datasets.
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Historically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer.
More recently, the coming-of-age of deep neural networks has dramatically improved the capabilities of large language models (LLMs). Trained on a large corpus of documents, these models not only memorize the vocabulary, morphology and syntax of human languages, but have shown to be able to memorize facts and relations [2]. Generative language models, when provided with a prompt, will extend the prompt with likely completions – an ability that can be used to extract answers to questions from the model. Two years ago, Metzler et al. argued that this ability of LLMs will allow us to rethink the search paradigm: to answer information needs directly rather that directing users to responsive primary sources [1]. Their vision was not without controversy; the following year Shaw and Bender argued that such a system is neither feasible nor desirable [3]. Nonetheless, the past year has seen the emergence of such systems, with offerings from established search engines and multiple new entrants to the industry.
The keynote will summarize the short history of these generative information retrieval systems, and focus on the many open challenges in this emerging field: ensuring that answers are grounded, attributing answer passages to a primary source, providing nuanced answers to non-factoid-seeking questions, avoiding bias, and going beyond simple regurgitation of memorized facts. It will also touch on the changing nature of the content ecosystem. LLMs are starting to be used to generate web content. Should search engines treat such derived content equal to human-authored content? Is it possible to distinguish generated from original content? How should we view hybrid authorship where humans contribute ideas and LLMs shape these ideas into prose? And how will this parallel technical evolution of search engines and content ecosystems affect their respective business models?
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Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Krishna Srinivasan
SIGIR (2023)
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Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models which are finetuned using large collections of labeled data. However, in specialized domains such as e-commerce and healthcare, the viability of this approach is limited by the dearth of large in-domain data. To address this paucity, recent methods leverage these powerful models to generate high-quality task and domain-specific synthetic data. Prior work has largely explored synthetic data generation or query generation (QGen) for Question-Answering (QA) and binary (yes/no) relevance prediction, where for instance, the QGen models are given a document, and trained to generate a query relevant to that document. However in many problems, we have a more fine-grained notion of relevance than a simple yes/no label. Thus, in this work, we conduct
a detailed study into how QGen approaches can be leveraged for nuanced relevance prediction. We demonstrate that – contrary to claims from prior works – current QGen approaches fall short of the more conventional cross-domain transfer-learning approaches. Via empirical studies spanning three public e-commerce benchmarks, we identify new shortcomings of existing QGen approaches – including their inability to distinguish between different grades of
relevance. To address this, we introduce label-conditioned QGen models which incorporates knowledge about the different relevance. While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label.
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Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Yunan Zhang
29TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2023)
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Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose two methods to mitigate the negative confounding effects by better disentangling relevance and bias. Offline empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches. We conduct a live experiment on a popular web store for four weeks, and find a significant improvement in user clicks over the baseline, which ignores the negative confounding effect.
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Job Type Extraction for Service Businesses
Yaping Qi
Hayk Zakaryan
Yonghua Wu
Companion Proceedings of the ACM Web Conference 2023
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Google My Business (GMB) is a platform that allows business owners to manage their business profiles, which will be displayed when a user issues a relevant query on Google Search or Maps. Many GMB businesses provide diverse services from home cleaning and plumbing to legal services and education. However the exact service content, which we call job types, is often missing in their profiles. This leaves the burden of finding such content to the users, either by the tedious work of scanning through business websites or time-consuming calling of the owners. In the present paper, we describe how we build a pipeline to automatically extract the job types from websites of business owners and how we solve scalability issues for deployment. Rather than focusing on developing novel and sophisticated machine learning models, we share various challenges we have faced and practical experiences of building such a pipeline, including the cold start problem of dataset collection with limited human annotation resource, scalability, reaching a launch bar of high precision, and building a general pipeline with reasonable coverage of any free-text web pages without relying on the Document Object Model (DOM) structure. With these challenges, standard approaches for information extraction do not directly apply or are not scalable to be served. In this paper, we show how we address these challenges in different stages of the extraction pipeline, including: (1) utilizing structured content like tables and lists to tackle the cold start problem of dataset collection; (2) exploitation of various context information to improve model performance without hurting scalability; and (3) formulating the extraction problem as a retrieval task to improve generalizability, efficiency as well as coverage. The pipeline has been successfully deployed, and is scalable enough to be refreshed every few days to extract the latest online information. The extracted job types are serving millions of users of Google Search and Google Maps with at least three use cases: (1) job types of a place are directly displayed on mobile devices; (2) job types provide explanation as to why a place shows up given a query; (3) job types are used as a signal to rank business places. According to a user survey, the displayed job types has greatly enhanced the probability of a user hiring a service provider.
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Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Pratyush Kar
Bing-Rong Lin
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (2023)
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As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.
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Comparative decisions, such as picking between two cars or deciding between two hiking trails, require the users to visit multiple webpages and contrast the choices along relevant aspects. Given the impressive capabilities of pre-trained large language models, we ask whether they can help automate such analysis. We refer to this task as extractive aspect-based contrastive summarization which involves constructing a structured summary that compares the choices along relevant aspects. In this paper, we propose a novel method called STRUM for this task that can generalize across domains without requiring any human-written summaries or fixed aspect list as supervision. Given a set of relevant input webpages, STRUM solves this problem using two pre-trained T5-based large language models: first one fine-tuned for aspect and value extraction, and second one fine-tuned for natural language inference. We showcase the abilities of our method across different domains, identify shortcomings, and discuss questions that we believe will be critical in this new line of research.
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Generative Information Retrieval (abstract)
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023), pp. 1
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Historically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer.
More recently, the coming-of-age of deep neural networks has dramatically improved the capabilities of large language models (LLMs). Trained on a large corpus of documents, these models not only memorize the vocabulary, morphology and syntax of human languages, but have shown to be able to memorize facts and relations~\cite{HowMuchKnowledge}. Generative language models, when provided with a prompt, will extend the prompt with likely completions -- an ability that can be used to extract answers to questions from the model. Two years ago, Metzler et al. argued that this ability of LLMs will allow us to rethink the search paradigm: to answer information needs directly rather that directing users to responsive primary sources~\cite{RethinkingSearch}. Their vision was not without controversy; the following year Shaw and Bender argued that such a system is neither feasible nor desirable~\cite{SituatingSearch}. Nonetheless, the past year has seen the emergence of such systems, with offerings from established search engines and multiple new entrants to the industry.
The keynote will briefly the short history of these generative information retrieval systems, and focus on the many open challenges in this emerging field: ensuring that answers are grounded, attributing answer passages to a primary source, providing nuanced answers to non-factoid-seeking questions, avoiding bias, and going beyond simple regurgitation of memorized facts. It will also touch on the changing nature of the content ecosystem. LLMs are starting to be used to generate web content. Should search engines treat such derived content equal to human-authored content? Is it possible to distinguish generated from original content? How should we view hybrid authorship where humans contribute ideas and LLMs shape these ideas into prose? And how will this parallel technical evolution of search engines and content ecosystems affect their respective business models?
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DSI++: Updating Transformer Memory with New Documents
Yi Tay
Jinfeng Rao
Emma Strubell
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
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Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents.
Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting significantly. Concretely, it improves the average Hits@10 by +21.1% over competitive baselines for NQ and requires 6 times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.
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