As search engines increasingly adopt answer-centric interfaces, AI-generated summaries are often consumed as final answers. These summaries are typically well-structured and citation-rich, creating a strong appearance of reliability that can encourage user trust, even when evidential grounding is uncertain. Motivated by this gap between appearance and evidence, we analyze 14,175 real-world queries from MS MARCO by examining Google Search AI summaries and their cited sources. We find that reliable-looking summaries mask substantial evidential failures. Despite their credible appearance, 32.31% of summaries are incorrect, and 56.16% of these errors arise even when supporting evidence exists in the cited sources. Moreover, 31.08% of summaries exhibit citation inconsistencies, with conflict risk increasing as more sources are cited and as citations appear in more prominent positions. Our findings reveal systematic, user-facing risks in answer-centric search, highlighting the need for evaluation and design practices beyond surface-level reliability signals. https://github.com/icip-cas/AISummary.

The widespread adoption of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems is reshaping the landscape of information retrieval. However, the long-term effects of LLM-generated texts on retrieval systems remain underexplored, creating challenges for mitigating their impact. The effects are examined in this study, with a particular focus on the “Spiral of Silence” phenomenon, which refers to the marginalization of diverse information as certain types of content dominate, leading to a homogenized information ecosystem. To investigate this, a simulation pipeline is constructed to model the iterative introduction of LLM-generated texts into retrieval systems. Experimental results across multiple iterations reveal that as the presence of LLM-generated texts within the system grows, retrieval systems exhibit a stronger tendency to retrieve these texts. This trend, in turn, reduces the visibility of human-generated content, diminishes diversity, propagates errors, and results in a notable decline in retrieval performance. To address these challenges, we propose a Utility-Driven Multi-Objective Optimization (UMO) framework to effectively mitigate the “Spiral of Silence.” This framework employs a two-phase approach: an optimization phase, leveraging the NSGA-II algorithm to derive optimal preference weights for multiple objectives, and a memorization phase, which directly integrates these weights into the retrieval vector space without requiring additional model retraining. Experimental results demonstrate that this framework maintains stable retrieval effectiveness, improves the retrieval proportion of human-generated content, reduces the excessive influence of LLM-generated texts, and preserves information diversity, effectively mitigating the “Spiral of Silence.”

Entity Resolution (ER) identifies records from different data sources that refer to the same real-world entity. Conventional ER approaches usually employ a structure matching mechanism, where attributes are aligned, compared and aggregated for ER decision. The structure matching approaches, unfortunately, often suffer from heterogeneous and dirty ER problems. That is, entities from different data sources are described using different schemas, and attribute values may be misplaced, missing, or noisy. In this paper, we propose a deep sequence-to-sequence entity matching model, denoted Seq2SeqMatcher, which can effectively solve the heterogeneous and dirty problems by modeling ER as a token-level sequence-to-sequence matching task. Specifically, we propose an align-compare-aggregate neural network for Seq2Seq entity matching, which can learn the representations of tokens, capture the semantic relevance between tokens, and aggregate matching evidence for accurate ER decisions in an end-to-end manner. Experimental results show that, by comparing entity records in token level and learning all components in an end-to-end manner, our Seq2Seq entity matching model can achieve remarkable performance improvements on 9 standard entity resolution benchmarks.

To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement.

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy.

Effective organization of web search results can greatly improve the utility of search engine and enhance the quality of search results. However, the organization of search results is difficult because the sub-topics of a query are usually not explicitly given. In this paper, we propose a novel topic-driven search result organization method, which can first detect the sub-topics of a query by finding the coherent Wikipedia concept groups from its search results; then organize these results using a topic-driven clustering algorithm; in the end we score and rank the topics using the support vector regression model. Empirical results show that our method can achieve competitive performance.

Name ambiguity problem has raised an urgent demand for efficient, high-quality named entity disambiguation methods. The key problem of named entity disambiguation is to measure the similarity between occurrences of names. The traditional methods measure the similarity using the bag of words (BOW) model. The BOW, however, ignores all the semantic relations such as social relatedness between named entities, associative relatedness between concepts, polysemy and synonymy between key terms. So the BOW cannot reflect the actual similarity. Some research has investigated social networks as background knowledge for disambiguation. Social networks, however, can only capture the social relatedness between named entities, and often suffer the limited coverage problem.