Conversational Retrieval-Augmented Generation (RAG) systems enhance user interactions by integrating large language models (LLMs) with external knowledge retrieval. However, multi-turn conversations present significant challenges, including implicit user intent and noisy context, which hinder accurate retrieval and response generation. Existing approaches often struggle with the unstructured conversational context and fail to model explicit relations among conversational turns. Moreover, they do not leverage historically relevant passages effectively. To overcome these limitations, we propose EvoRAG, a novel framework that maintains an evolving knowledge graph aligned with the unstructured conversational context. This graph explicitly captures relations among user queries, system responses, and relevant passages across conversational turns, serving as a structured representation of the context. EvoRAG includes three key components: (1) a dual-path retrieval module for context denoising, (2) a unified knowledge integration module for query rewriting and summarization, and (3) a graph-enhanced RAG module for accurate retrieval and response generation. Experiments on four public conversational RAG datasets show that EvoRAG significantly outperforms strong baselines, particularly in handling topic shifts and long dialogue contexts.

Knowledge-intensive tasks often require complex reasoning and contextual understanding over long contexts. However, the learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we propose that the short LLMs have great potentiality for solving knowledge-intensive tasks that have long context, i.e. they can be solved by purely working with oracle short-contexts within the input long-context. On top of this argument, we propose a framework called DCISO DynamiC knowledge-Intensive task S>Olver), which enables a short-LLM to address the knowledge-intensive tasks with long context via dynamic context browsing. In our framework, the short-LLM prompts itself to reason for two critical decisions: 1) how to access to the appropriate part of context within the input, 2) how to make effective use of the accessed context. By adaptively accessing and utilizing the context based on the presented tasks, DCISO can serve as a general framework to handle diversified knowledge-intensive long-context problems. We comprehensively evaluate different types of tasks from popular long-context benchmarks, where DCISO is able to achieve a substantially improved performance. Our codes will be released at this repository.

Processing long contexts presents a significant challenge for large language models (LLMs). While recent advancements allow LLMs to handle much longer contexts than before (e.g., 32K or 128K tokens), it is computationally expensive and can still be insufficient for many applications. Retrieval-Augmented Generation (RAG) is considered a promising strategy to address this problem. However, conventional RAG methods face inherent limitations because of two underlying requirements: 1) explicitly stated queries, and 2) well-structured knowledge. These conditions, however, do not hold in general long-context processing tasks.

As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) technologies, particularly large language models (LLMs), search engines have evolved to support more intuitive and intelligent interactions between users and systems. Conversational search, an emerging paradigm for next-generation search engines, leverages natural language dialogue to facilitate complex and precise information retrieval, thus attracting significant attention. Unlike traditional keyword-based search engines, conversational search systems enhance user experience by supporting intricate queries, maintaining context over multi-turn interactions, and providing robust information integration and processing capabilities. Key components such as query reformulation, search clarification, conversational retrieval, and response generation work in unison to enable these sophisticated interactions. In this survey, we explore the recent advancements and potential future directions in conversational search, examining the critical modules that constitute a conversational search system. We highlight the integration of LLMs in enhancing these systems and discuss the challenges and opportunities that lie ahead in this dynamic field. Additionally, we provide insights into real-world applications and robust evaluations of current conversational search systems, aiming to guide future research and development in conversational search.

Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.

Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.

Conversational search is a crucial and promising branch in information retrieval. In this paper, we reveal that not all historical conversational turns are necessary for understanding the intent of the current query. The redundant noisy turns in the context largely hinder the improvement of search performance. However, enhancing the context denoising ability for conversational search is quite challenging due to data scarcity and the steep difficulty for simultaneously learning conversational query encoding and context denoising. To address these issues, in this paper, we present a novel Curriculum cOntrastive conTExt Denoising framework, COTED, towards few-shot conversational dense retrieval. Under a curriculum training order, we progressively endow the model with the capability of context denoising via contrastive learning between noised samples and denoised samples generated by a new conversation data augmentation strategy. Three curriculums tailored to conversational search are exploited in our framework. Extensive experiments on two few-shot conversational search datasets, i.e., CAsT-19 and CAsT-20, validate the effectiveness and superiority of our method compared with the state-of-the-art baselines.

In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. Many methods have been proposed to assign a personality to dialogue chatbots, but most of them utilize explicit user profiles, including several persona descriptions or key-value-based personal information. In a practical scenario, however, users might be reluctant to write detailed persona descriptions, and obtaining a large number of explicit user profiles requires tremendous manual labour. To tackle the problem, we present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. We conduct comprehensive experiments on two large datasets, and the results show that our method outperforms all baseline models.

atural language dialogue systems raise great attention recently. As many dialogue models are data-driven, high-quality datasets are essential to these systems. In this paper, we introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively. To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization, deduplication, segmentation, and filtering. The scale of Pchatbot is significantly larger than existing Chinese datasets, which might benefit the data-driven models. Besides, current dialogue datasets for personalized chatbot usually contain several persona sentences or attributes. Different from existing datasets, Pchatbot provides anonymized user IDs and timestamps for both posts and responses. This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history. Our preliminary experimental study benchmarks several state-of-the-art dialogue models to provide a comparison for future work. The dataset can be publicly accessed at Github: https://github.com/qhjqhj00/Pchatbot.