The field of natural language processing is witnessing significant advancements in retrieval-augmented generation and reasoning. Recent developments have focused on improving the efficiency and effectiveness of large language models (LLMs) in searching and retrieving relevant information to generate more accurate and informative responses. Notable trends include the integration of reinforcement learning, self-supervised learning, and multi-agent frameworks to enhance the search and reasoning capabilities of LLMs. These advancements have led to state-of-the-art performances in various benchmarks and datasets, demonstrating the potential of retrieval-augmented generation and reasoning in real-world applications. Some noteworthy papers in this regard include 'Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs', which proposes a novel framework for autonomous retrieval-augmented reasoning, and 's3: You Don't Need That Much Data to Train a Search Agent via RL', which introduces a lightweight framework for training search agents using reinforcement learning.
Advancements in Retrieval-Augmented Generation and Reasoning
Sources
Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate
Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents
JIR-Arena: The First Benchmark Dataset for Just-in-time Information Recommendation
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
s3: You Don't Need That Much Data to Train a Search Agent via RL
RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection
Transductively Informed Inductive Program Synthesis
Self-GIVE: Associative Thinking from Limited Structured Knowledge for Enhanced Large Language Model Reasoning
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization
ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation
FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning
URLs Help, Topics Guide: Understanding Metadata Utility in LLM Training
O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning