User satisfaction serves as a key indicator of search experience in Generative Information Retrieval (GenIR) systems, and its accurate estimation is essential for system optimization. Extensive research in traditional IR has established interaction signals (e.g., dwell time, clicks, query reformulations) as reliable indicators of user satisfaction. However, prevailing approaches for satisfaction estimation in GenIR (e.g., LLM-as-a-judge) primarily rely on textual content and fail to account for user interactions during the search process. In this work, empirical analysis of real-world data shows that user satisfaction correlates negatively with interaction signals reflecting interaction cost, and positively with response quality. Building on these findings, we propose the Gain-aware Cost-sensitive Satisfaction estimator (G-CoS), an interpretable gain-cost framework for user satisfaction estimation in GenIR. G-CoS models user satisfaction as a dynamic trade-off between Response Quality and multidimensional Interaction Cost. Experimental results demonstrate that G-CoS outperforms LLM-as-a-judge methods, interaction sequence models, as well as machine learning models using the same gain and cost features. Moreover, the learned parameters reveal interpretable associations between gain-cost dynamics and user satisfaction. This work contributes an interpretable framework for user satisfaction estimation and offers insights for GenIR system optimization.

The rapid spread of misinformation on social media has underscored the importance of automatic fact-checking. Existing fact-checking pipelines typically rely on multi-stage frameworks involving evidence retrieval and claim verification. However, these methods face two major challenges: (1) the retrieval process often introduces noisy evidence, which compromises the reliability of the final veracity prediction; and (2) the verification models may overlook critical factual details, resulting in hallucinated conclusions. To address these issues, we propose a fact-checking framework SLED with Self-supervised denoising evidence retrieval and LLM-Enhanced Debate-based verification. In the retrieval stage, SLED leverage trained verifier to assess credibility and necessity of retrieved evidence, enabling the elimination of noisy evidence. In the verification stage, SLED prompts the LLM to generate dual-perspective reasoning and simulates a multi-agent debate, followed by distillation into a lightweight model for final veracity prediction. Experiments on CHEF and HOVER datasets demonstrate that SLED achieves the state-of-the-art results in complex fact verification scenarios.

The development of LLM has facilitated the emergence of generative information retrieval (IR) systems, such as ''Bing Chat''. Generative IR systems return generated text with citations rather than a list of ranked search results. User studies on IR systems are essential for understanding users' interaction patterns, evaluating and optimizing systems, and improving search experience, particularly in the context of generative IR systems with novel conversational interfaces and responses. However, systematic investigations into user behavior and search experience on generative IR systems are notably lacking. To address this gap, we conducted a user study using Bing Chat to explore user behavior and feedback on generative IR systems. The participants were required to accomplish three types of tasks using Bing Chat. During the search process, we collected their various behavior (e.g., click, query reformulation) and explicit feedback (e.g., satisfaction, credibility, and success). Additionally, the same study was conducted on traditional IR systems Bing for comparison. Analyses of these data show that Bing Chat can reduce the user's search effort and lead to a better search experience without any decrease in credibility compared with Bing. We believe that this work provides valuable insight into the design and evaluation of generative information retrieval systems.

User behavior and experience are important for improving information retrieval (IR) systems. While much research has focused on traditional IR systems, few studies have systematically examined user behavior and search experience with emerging generative IR systems. A key reason for this gap is the lack of publicly available toolkits to record user behavior and feedback in generative IR systems. We developed a comprehensive platform to collect user behavior and feedback on the generative IR system. This platform consists of: 1) a generative IR system that supports both API-based and customized retrieval-augmented generation (RAG) methods, 2) a user interface that logs various user behavior, including prompts, clicks, mouse movements, and scrolling, and 3) an annotation website that allows users to provide feedback. We believe the proposed platform has the potential to streamline data collection for user studies on generative IR systems, paving the way for future research on how users engage with and interact with these systems.

Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments. While recent advances in neural retrieval methods have significantly improved the performance of open-domain retrieval tasks (e.g., Web search), their advantages haven’t been observed in legal case retrieval due to their thirst for annotated data. As annotating large-scale training data in legal domains is prohibitive due to the need for domain expertise, traditional search techniques based on lexical matching such as TF-IDF, BM25, and Query Likelihood are still prevalent in legal case retrieval systems. While previous studies have designed several pre-training methods for IR models in open-domain tasks, these methods are usually suboptimal in legal case retrieval because they cannot understand and capture the key knowledge and data structures in the legal corpus. To this end, we propose a novel pre-training framework named Caseformer that enables the pre-trained models to learn legal knowledge and domain-specific relevance-matching patterns in legal case retrieval without any human-labeled data. This framework is designed to support both dense retrieval models and neural re-ranking models. Through three unsupervised learning tasks, Caseformer is able to capture the special language, document structure, and relevance-matching patterns of legal case documents, making it a strong backbone for downstream legal case retrieval tasks. Experimental results show that our model has achieved state-of-the-art performance in both zero-shot and fine-tuning settings. Also, experiments on both Chinese and English legal datasets demonstrate that the effectiveness of Caseformer is language-independent in legal case retrieval.

Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR). However, the responses generated by off-the-shelf LLMs tend to be generic, i.e., cannot capture the distinctiveness of each document with similar content. This limits the performance of LLMs in IR because finding and distinguishing relevant documents from substantial similar documents is a typical problem in many IR tasks. To address this issue, we propose an unsupervised alignment method, namely Reinforcement Learning from Contrastive Feedback (RLCF), empowering LLMs to generate both high-quality and context-specific responses. Our approach constructs unsupervised contrastive feedback signals based on similar document groups, and adopts a reward function, named group-wise reciprocal rank, to optimize LLMs. We conduct extensive experiments to evaluate the effectiveness of RLCF.

The Dialogue-level Aspect-based Sentiment Quadruple analysis (DiaASQ) task has recently received attention in the Aspect-Based Sentiment Analysis (ABSA) field. It aims to extract(target, aspect, opinion, sentiment) quadruples from multi-turn and multi-party dialogues. Compared to previous ABSA tasks focusing on text such as sentences, the DiaASQ task involves more complex contextual information and corresponding relations between terms, as well as longer sequences. These characteristics challenge existing methods that struggle to model explicit span-level interactions or have high computational costs. In this paper, we propose a span-pair interaction and tagging method to solve these issues, which includes a novel Span-pair Tagging Scheme (STS) and a simple and efficient Multi-level Representation Model (MRM). STS simplifies the DiaASQ task to a span-pair tagging task and explicitly captures complete span-level semantics by tagging span pairs. MRM efficiently models the dialogue structure information and span-level interactions by constructing multi-level contextual representation. Besides, we train a span ranker to improve the running efficiency of MRM. Extensive experiments on multilingual datasets demonstrate that our method outperforms existing state-of-the-art methods.

With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the relevant POI based on query-POI matching. However, user behavior in POI search, i.e., how users examine the search engine result page (SERP), is mostly underexplored. A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users' eye movements and their implicit feedback on the SERP are collected. Based on the collected data, we analyze (1) query-level user behavior patterns in POI search, i.e., examination and interactions on SERP; (2) session-level user behavior patterns in POI search, i.e., query reformulation, termination of search, etc. Our work sheds light on user behavior in POI search and could potentially benefit future studies on related research topics.

Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues. To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines. Expert annotators are recruited to provide 4-level graded relevance scores (fine-grained) for query-passage pairs instead of binary relevance judgments (coarse-grained). To ease the false negative issues, more passages with higher diversities are considered when performing relevance annotations, especially in the test set, to ensure a more accurate evaluation. Apart from the textual query and passage data, other auxiliary resources are also provided, such as query types and XML files of documents which passages are generated from, to facilitate further studies. To evaluate the dataset, commonly used ranking models are implemented and tested on T2Ranking as baselines. The experimental results show that T2Ranking is challenging and there is still scope for improvement. The full data and all codes are available at https://github.com/THUIR/T2Ranking/.

Reading is a vital and complex cognitive activity during users’ information-seeking process. Several studies have focused on understanding users’ reading behavior in desktop search. Their findings greatly contribute to the design of information retrieval models. However, little is known about how users read a result in mobile search, although search currently happens more frequently in mobile scenarios. In this paper, we conduct a lab-based user study to investigate users’ fine-grained reading behavior patterns in mobile search. We find that users’ reading attention allocation is strongly affected by several behavior biases, such as position and selection biases. Inspired by these findings, we propose a probabilistic generative model, the Passage-level Reading behavior Model (PRM), to model users’ reading behavior in mobile search. The PRM utilizes observable passage-level exposure and viewport duration events to infer users’ unobserved skimming event, reading event, and satisfaction perception during the reading process. Besides fitting the passage-level reading behavior, we utilize the fitted parameters of PRM to estimate the passage-level and document-level relevance. Experimental results show that PRM outperforms existing unsupervised relevance estimation models. PRM has strong interpretability and provides valuable insights into the understanding of how users seek and perceive useful information in mobile search.

The major challenge in the task of entity alignment (EA) lies in the heterogeneity of the knowledge graph. The traditional solution to EA is to first map entities to the same space via knowledge embedding and then calculate the similarity between entities from different knowledge graphs. However, these methods mainly rely on manually labeled seeds of EA, which limits their applicability. Some researchers have begun using pseudo-labels rather than seeds for unsupervised EA. However, directly using pseudo-labels causes new problems, such as noise in the pseudo-labels. In this article, we propose a model called the Semantically Driven Hybrid Network (SDHN) to reduce the impact of noise in the pseudo-labels on the performance of EA models. The SDHN consists of two modules: a Teacher–Student Network (TSN) and a Rotation and Penalty (RAP) module. The TSN module reduces the impact of noise in two ways: (1) The TSN’s teacher network guides its student network to construct pseudo-labels based on semantic information instead of directly creating pseudo-labels. (2) It adaptively fuses semantic information into student networks to improve the final representation of entity embedding. Finally, the TSN enhances the performance of models of entity alignment via the RAP module. The results of experiments on multiple benchmark datasets showed that the SDHN outperforms state-of-the-art models.

This paper presents a neural information retrieval pipeline that integrates cooperative learning of query reformulation and neural retrieval models. Our pipeline first exploits an automatic query reformulator to reformulate the user-issued query and then submits the reformulated query to the neural retrieval model. We simultaneously optimize the quality of reformulated queries and ranking performance with an alternate training strategy where query reformulator and neural retrieval model learn from the feedback of each other. Besides, we incorporate knowledge information into automatic query reformulation. The reformulated queries are further improved and contribute to a better ranking performance of the following neural retrieval model. We study two representative neural retrieval models KNRM and BERT in our pipeline. Experiments on two datasets show that our pipeline consistently improves the retrieval performance of the original neural retrieval models while only increases negligible time on automatic query reformulation.

To study the impact of providing direct answers in search results on user behavior, we conducted a controlled user study to analyze factors including reading time, eye-tracked attention, and the influence of the quality of answer module content. We also studied a more advanced answer interface, where multiple answers are shown on the search engine results page (SERP). Our results show that users focus more extensively than normal on the top items in the result list when answers are provided. The existence of the answer module helps to improve user engagement on SERPs, reduces user effort, and promotes user satisfaction during the search process. Furthermore, we investigate how the question type -- factoid or non-factoid -- affects user interaction patterns. This work provides insight into the design of SERPs that includes direct answers to queries, including when answers should be shown.

A better understanding of users' reading behavior helps improve many information retrieval (IR) tasks, such as relevance estimation and document ranking. Existing research has already leveraged eye movement information to investigate user's reading process during document-level relevance judgments and the findings were adopted to build more effective ranking models. Recently, fine-grained (e.g., passage or sentence level) relevance judgments have been paid much attention to with the requirements in conversational search and QA systems. However, there is still a lack of thorough investigation on user's reading behavior during these kinds of interaction processes. To shed light on this research question, we investigate how users allocate their attention to passages of a document during the relevance judgment process. With the eye-tracking data collected in a laboratory study, we show that users pay more attention to the "key" passages which contain key useful information. Users tend to revisit these key passages several times to accumulate and verify the gathered information. With both content and user behavior features, we find that key passages can be predicted with supervised learning. We believe that this work contributes to better understanding users' reading behavior and may provide more explainability for relevance estimation.

Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.

The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually estimate relevance based on document-level signals. Recent works consider a more fine-grain, passage-level relevance information, which can further enhance retrieval performance. However, it lacks a detailed analysis of how passage-level relevance signals determine or influence the relevance judgment of the whole document. To investigate the role of passage-level relevance in the document-level relevance judgment, we construct an ad-hoc retrieval dataset with both passage-level and document-level relevance labels. A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance. Based on the relationship between passage-level and document-level relevance, we also show that utilizing passage-level relevance signals can improve existing document ranking models. This study helps us better understand how users perceive relevance for a document and inspire the designing of novel ranking models leveraging fine-grain, passage-level relevance signals.

Understanding search intents behind queries is of vital importance for improving search performance or designing better evaluation metrics. Although there exist many efforts in Web search user intent taxonomies and investigating how users' interaction behaviors vary with the intent types, only a few of them have been made specifically for the image search scenario. Different from previous works which investigate image search user behavior and task characteristics based on either lab studies or large scale log analysis, we conducted a field study which lasts one month and involves 2,040 search queries from 555 search tasks. By this means, we collected relatively large amount of practical search behavior data with extensive first-tier annotation from users. With this data set, we investigate how various image search intents affect users' search behavior, and try to adopt different signals to predict search satisfaction under the certain intent. Meanwhile, external assessors were also employed to categorize each search task using four orthogonal intent taxonomies. Based on the hypothesis that behavior is dependent of task type, we analyze user search behavior on the field study data, examining characteristics of the session, click and mouse patterns. We also link the search satisfaction prediction to image search intent, which shows that different types of signals play different roles in satisfaction prediction as intent varies. Our findings indicate the importance of considering search intent in user behavior analysis and satisfaction prediction in image search.

Diversity has been taken into consideration by existing Web image search engines in ranking search results. However, there is no thorough investigation of how diversity affects user satisfaction in image search. In this article, we address the following questions: (1) How do different factors, such as content and visual presentations, affect users’ perception of diversity? (2) How does search result diversity affect user satisfaction with different search intents? To answer those questions, we conduct a set of laboratory user studies to collect users’ perceived diversity annotations and search satisfaction. We find that the existence of nearly duplicated image results has the largest impact on users’ perceived diversity, followed by the similarity in content and visual presentations. Besides these findings, we also investigate the relationship between diversity and satisfaction in image search. Specifically, we find that users’ preference for diversity varies across different search intents. When users want to collect information or save images for further usage (the Locate search tasks), more diversified result lists lead to higher satisfaction levels. The insights may help commercial image search engines to design better result ranking strategies and evaluation metrics.

Image search engines show results differently from general Web search engines in three key ways: (1) most Web-based image search engines adopt the two-dimensional result placement instead of the linear result list; (2) image searches show snapshots instead of snippets (query-dependent abstracts of landing pages) on search engine result pages (SERPs); and (3) pagination is usually not (explicitly) supported on image search SERPs, and users can view results without having to click on the "next page'' button. Compared with the extensive study of user behavior in general Web search scenarios, there exists no thorough investigation how the different interaction mechanism of image search engines affects users' examination behavior. To shed light on this research question, we conducted an eye-tracking study to investigate users' examination behavior in image searches. We focus on the impacts of factors in examination including position, visual saliency, edge density, the existence of textual information, and human faces in result images. Three interesting findings indicate users' behavior biases: (1) instead of the traditional "Golden Triangle'' phenomena in the user examination patterns of general Web search, we observe a middle-position bias, (2) besides the position factor, the content of image results (e.g., visual saliency) affects examination behavior, and (3) some popular behavior assumptions in general Web search (e.g., examination hypothesis) do not hold in image search scenarios. We predict users' examination behavior with different impact factors. Results show that combining position and visual content features can improve prediction in image searches.