There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions.However, existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning-where relevant information is embedded in subtle, syntactic, or semantically distant connections rather than explicit statements.In such cases, traditional retrieval methods fail to capture relevant context, and long-context modeling also becomes inefficient due to numerous complicated personarelated details.To address this gap, we introduce IMPLEXCONV, a large-scale long-term dataset with 2,500 examples, each containing approximately 100 conversation sessions, designed to study implicit reasoning in personalized dialogues.Additionally, we propose TAC-ITREE, a novel hierarchical tree framework that structures conversation history into multiple levels of summarization.Instead of brute-force searching all data, TACITREE enables an efficient, level-based retrieval process where models refine their search by progressively selecting relevant details.Our experiments demonstrate that TACITREE significantly improves the ability of LLMs to reason over long-term conversations with implicit contextual dependencies.I love playing sports like basketball and swimming.I'm going on a sports stadium tour!Aug 15, 2024 I broke my leg in a car accident...

We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without any labels or ground-truth label space.Similar single-label XWS settings have been explored recently, however, these methods cannot be easily adapted for multi-label.We observe that (1) most documents have a dominant class covering the majority of content and (2) long-tail labels would appear in some documents as a dominant class.Therefore, we first utilize the user description to prompt a large language model (LLM) for dominant keyphrases of a subset of raw documents, and then construct a (initial) label space via clustering.We further apply a zero-shot multi-label classifier to locate the documents with small top predicted scores, so we can revisit their dominant keyphrases for more long-tail labels.We iterate this process to discover a comprehensive label space and construct a multi-label classifier as a novel method, X-MLClass.X-MLClass exhibits a remarkable increase in ground-truth label space coverage on various datasets, for example, a 40% improvement on the AAPD dataset over topic modeling and keyword extraction methods.Moreover, X-MLClass achieves the best end-to-end multi-label classification accuracy. B Prompt Templates for Generating KeyphrasesCode 2 provides an example of the prompt used to generate keyphrases for a selected chunk of the Amazon-531 dataset.Users can help us define the objective with examples.For example, the coarse-grained objectives look like "games" and "animals", while the corresponding fine-grained objectives are "trading_card_games" and "reptiles".