Well-designed prompts can guide text-to-image models to generate amazing images.

The Corpus dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv.

Late interaction methods compute representations for the query and corpus graphs separately, and compare these representations using simple similarity functions at the last stage, leading to highly scalable systems. Early interaction methods combine information from both graphs right from the input stages, are usually considerablymoreaccurate,butslower.

Claim on SSL demonstrating the value of labeled data. This is an interesting perspective.

An intelligent conversational agent (a.k.a., chat-bot) could embrace conversational technologies to obtain user preferences online, to overcome inherent limitations of recommender systems trained over the offline historical user behaviors. In this paper, we propose CORE, a new offline-training and online-checking framework to plug a COnversational agent into REcommender systems. Unlike most prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, CORE bridges the conversational agent and recommender system through a unified uncertainty minimization framework, which can be easily applied to any existing recommendation approach. Concretely, CORE treats a recommender system as an offline estimator to produce an estimated relevance score for each item, while CORE regards a conversational agent as an online checker that checks these estimated scores in each online session. We define uncertainty as the sum of unchecked relevance scores. In this regard, the conversational agent acts to minimize uncertainty via querying either attributes or items. Towards uncertainty minimization, we derive the certainty gain of querying each attribute and item, and develop a novel online decision tree algorithm to decide what to query at each turn. Our theoretical analysis reveals the bound of the expected number of turns of CORE in a cold-start setting. Experimental results demonstrate that CORE can be seamlessly employed on a variety of recommendation approaches, and can consistently bring significant improvements in both hot-start and cold-start settings.

Large Language Model (LLM) agents have shown remarkable capabilities in reasoning and problem-solving when augmented with retrieval mechanisms [1, 2]. However, a critical challenge persists: ensuring that retrieved information maintains logical and structural consistency with the agent's current reasoning context. Traditional retrieval methods, such as vector similarity search, retrieve information based solely on semantic similarity, without considering structural relationships within knowledge bases. This limitation becomes particularly problematic in multi-hop reasoning scenarios, where an agent must traverse a knowledge graph to answer complex queries. When an agent is reasoning about a specific concept (the "anchor"), retrieving information from structurally disconnected parts of the knowledge graph can introduce inconsistencies and contradictions into the reasoning process. For example, if an agent is reasoning about "cloud computing architecture" starting from a specific node, retrieving information about unrelated topics that happen to be semantically similar can lead to incoherent reasoning chains due to lack of structural consistency. We propose Path-Constrained Retrieval (PCR), a retrieval method that enforces structural constraints by restricting the search space to nodes reachable from an anchor node in a knowledge graph.

The rapid evolution of the telecommunication industry necessitates efficient troubleshooting processes to maintain network reliability, software maintainability, and service quality. Trouble Reports (TRs), which document issues in Ericsson's production system, play a critical role in facilitating the timely resolution of software faults. However, the complexity and volume of TR data, along with the presence of diverse criteria that reflect different aspects of each fault, present challenges for retrieval systems. Building on prior work at Ericsson, which utilized a two-stage workflow, comprising Initial Retrieval (IR) and Re-Ranking (RR) stages, this study investigates different TR observation criteria and their impact on the performance of retrieval models. We propose \textbf{CREST} (\textbf{C}riteria-specific \textbf{R}etrieval via \textbf{E}nsemble of \textbf{S}pecialized \textbf{T}R models), a criterion-driven retrieval approach that leverages specialized models for different TR fields to improve both effectiveness and interpretability, thereby enabling quicker fault resolution and supporting software maintenance. CREST utilizes specialized models trained on specific TR criteria and aggregates their outputs to capture diverse and complementary signals. This approach leads to enhanced retrieval accuracy, better calibration of predicted scores, and improved interpretability by providing relevance scores for each criterion, helping users understand why specific TRs were retrieved. Using a subset of Ericsson's internal TRs, this research demonstrates that criterion-specific models significantly outperform a single model approach across key evaluation metrics. This highlights the importance of all targeted criteria used in this study for optimizing the performance of retrieval systems.