Multi-hop question answering needs evidence that is scattered across many sentences and ordered along a reasoning chain. Existing RAG systems struggle with this setting because flat chunk retrieval can miss bridge facts, binary graph structures lose n-ary information, and unordered top-K contexts leave evidence composition to the answer model. We introduce HyperPathsRAG, a hypergraph-based RAG framework that aligns query decomposition with the corpus index. It decomposes questions into n-ary sub-questions aligned with hypergraph facts, jointly matches all sub-questions to retrieve reasoning paths, scores paths by chain completeness, and presents the retrieved evidence in reasoning order. We evaluate in the candidate-passage setting on the 1k-question hard subsets of HotpotQA, 2Wiki, and MuSiQue, with three answering LLMs. HyperPathsRAG attains the best average Exact Match on every backbone (up to 42.2 EM / 56.2 F1 with Llama-3.3-70B-Instruct), and the best average F1 on two of three backbones; ablations attribute the bulk of the gain to the hypergraph index and the multi-target retriever. Our code is available at https://anonymous.4open.science/r/HyperPathsRAG-AE05/.

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations—where LLMs direct the discourse and steer the conversation’s objectives—remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings.

Recent image captioning models have made much progress for exploring the multi-modal interaction, such as attention mechanisms. Though these mechanisms can boost the interaction, there are still two gaps between the visual and language domains: (1) the gap between the visual features and textual semantics, (2) the gap between the disordering of visual features and the ordering of texts. To bridge the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit order planning. We integrate the planning mechanism to the attention based caption model and propose the High-level Semantic PLanning based Attention Network (HS-PLAN). First, an attention based reconstruction module is designed to reconstruct the visual features with high-level semantic information. Then we apply a pointer network to serialize the features and obtain the explicit order plan to guide the generation. Experiments conducted on MS COCO show that our model outperforms previous methods and achieves the state-of-the-art performance of 133.4% CIDEr-D score.