Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference.Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines.However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve-entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models.In this work, we propose s3, a lightweight, modelagnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG.s3 requires only 2.4k training samples to outperform baselines trained on over 70× more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks. 1

While significant progress has been made with dual-and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case often overlooked yet important in downstream applications.In this paper, we introduce LOGICOL, a logically informed contrastive learning objective for dense retrievers.LOGICOL builds upon in-batch supervised contrastive learning and learns dense retrievers to respect the subset and mutually exclusive set relation between query results.We evaluated the effectiveness of LOGICOL in the entity retrieval task, where the model is expected to retrieve a set of Wikipedia entities that satisfy the implicit logical constraints of the query.We show that models trained with LOGICOL show improvements both in terms of retrieval performance and logical consistency in the results.We provide detailed analysis and insights to uncover why queries with logical connectives are challenging for dense retrievers and why LOGI-COL is effective.Our codes and data are available at https://github.com/yanzhen4/LogiCoL. A not B: Species of orchids inMalaysia but not Thailand.

Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications such as web content analysis and semantic indexing. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly and time-consuming to acquire. To alleviate human efforts, in this paper, we work on hierarchical text classification with a minimal amount of supervision: using the sole class name of each node as the only supervision. Recently, large language models (LLM) have shown competitive performance on various tasks through zero-shot prompting, but this method performs poorly in the hierarchical setting because it is ineffective to include the large and structured label space in a prompt. On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features. To tackle the above challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced weakly-supervised hierarchical text Classification, which combines the general knowledge of LLMs and task-specific features mined from an unlabeled corpus. TELEClass automatically enriches the raw taxonomy with class-indicative features for better label space understanding and utilizes novel LLM-based data annotation and generation methods specifically tailored for the hierarchical setting. Experiments show that TELEClass can significantly outperform previous baselines while achieving comparable performance to zero-shot prompting of LLMs with drastically less inference cost.