Question Answering

ReaRAG enhances factuality in large reasoning models (LRMs) by integrating knowledge-guided reasoning with iterative retrieval augmented generation.

Open Deep Search (ODS): Democratizing Search with Open-source Reasoning Agents.

MCTS-RAG: Combines Monte Carlo Tree Search with Retrieval-Augmented Generation to enhance small LMs’ reasoning on complex tasks.

Typed-RAG enhances non-factoid QA by type-aware decomposition, refining retrieval and generation for nuanced, user-aligned answers.

R1-Searcher: RL enhances LLMs by incentivizing autonomous search, outperforming RAG methods, even GPT-4o-mini!

More documents can hurt RAG performance, even with same length!

SAGE: Precise RAG via semantic segmentation, adaptive chunking, and LLM feedback, boosting QA accuracy & cost-efficiency.

SemViQA: A new approach to boost Vietnamese fact-checking with semantic understanding and efficient evidence retrieval.

MoR: Adaptive knowledge retrieval by fusing structural and textual data for better question answering.

Rewriting method is critical to conversational assistant effectiveness.

CTM: A new benchmark for assessing temporal reasoning in LLMs across Chinese dynastic history.

Test-time scaling + confidence = better QA!

SAFE-SQL boosts Text-to-SQL accuracy by intelligently generating and filtering self-augmented examples for in-context learning, surpassing existing methods in challenging scenarios.

SQUARE, a novel prompting technique, enhances LLM reasoning by prompting self-interrogation through sequential question answering, significantly outperforming traditional methods.

Fact-checkers need explainable AI: This study reveals how AI tools can better support fact-checkers by providing explanations tailored to their workflows, addressing unmet needs, and improving the eff…

FailSafeQA benchmark rigorously evaluates LLMs’ resilience against diverse human-interaction variations, revealing critical weaknesses in even high-performing models, particularly regarding hallucinat…

ARR: A novel zero-shot prompting method significantly boosts LLM performance on diverse question-answering tasks by explicitly incorporating question analysis, information retrieval, and step-by-step …

DeepRAG enhances LLM reasoning by strategically integrating retrieval, modeled as an MDP, improving accuracy by 21.99% and retrieval efficiency.

ChartCitor: A multi-agent LLM framework combats LLM hallucination in ChartQA by providing fine-grained visual citations, enhancing user trust and productivity.

CoRAG, a novel Chain-of-Retrieval Augmented Generation model, dynamically refines queries for improved accuracy in multi-hop question answering, achieving state-of-the-art performance.