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Found 4 papers, 2 papers with code
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
First, we involve a shared retrieval knowledge pool in the debate process to solve the problem of limited and different knowledge backgrounds.
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.
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