Papers Evidence Selection
“Evidence Selection” 태그가 달린 논문 22편 · 필터 해제
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-base…
Evidence SelectionRAGRerankingRetrieval+4Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering
Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents (text, images, tables) and performing cross-modal reasoning. Current document retrieval-augmented generation (D…
BenchmarkingEvidence SelectionQuestion AnsweringRetrieval+2Knowledge-Aware Iterative Retrieval for Multi-Agent Systems
We introduce a novel large language model (LLM)-driven agent framework, which iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge. A defining feature of the system is …
Evidence SelectionLarge Language ModelOpen-Domain Question AnsweringQuestion Answering+1Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address…
Evidence SelectionFormQuestion AnsweringRAGHalu-J: Critique-Based Hallucination Judge
Large language models (LLMs) frequently generate non-factual content, known as hallucinations. Existing retrieval-augmented-based hallucination detection approaches typically address this by framing it as a classificatio…
Evidence SelectionHallucinationRetrievalChain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Open-ended question answering requires models to find appropriate evidence to form wellreasoned, comprehensive and helpful answers. In practical applications, models also need to engage in extended discussions on potenti…
Evidence SelectionOpen-Ended Question AnsweringQuestion AnsweringRetrievalComparing Knowledge Sources for Open-Domain Scientific Claim Verification
The increasing rate at which scientific knowledge is discovered and health claims shared online has highlighted the importance of developing efficient fact-checking systems for scientific claims. The usual setting for th…
Claim VerificationEvidence SelectionFact CheckingInformation Retrieval+1Do We Need Language-Specific Fact-Checking Models? The Case of Chinese
This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese. We first demonstrate the limitations of translation-based methods and multilingual large language…
Evidence SelectionFact CheckingMisinformationSentence+2Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation …
BenchmarkingEvidence SelectionHallucinationLogical Reasoning+1SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data
This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence sel…
Evidence SelectionNatural Language InferenceRetrievalTask 2SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets have been proposed for developing docum…
Evidence SelectionQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network
Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure…
Evidence SelectionGraph AttentionQuestion AnsweringSentenceIn-the-Wild Video Question Answering
Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding d…
Evidence SelectionQuestion AnsweringVideo Question AnsweringVideo UnderstandingWildQA: In-the-Wild Video Question Answering
Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding d…
Evidence SelectionQuestion AnsweringVideo Question AnsweringVideo UnderstandingProduct Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices
It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semi-structured attributes, text descriptions, user-provided contents, etc. However, these…
Answer GenerationData AugmentationEvidence SelectionProduct Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices
It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semi-structured attributes, text descriptions, user-provided contents, etc. However, these…
Answer GenerationData AugmentationEvidence SelectionEvidence Selection as a Token-Level Prediction Task
In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence s…
Claim VerificationEvidence SelectionPredictionRetrieval+2A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, b…
Evidence SelectionQuestion AnsweringAmbiFC: Fact-Checking Ambiguous Claims with Evidence
Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute the claim and yield conflicting but valid …
Claim VerificationEvidence SelectionFact CheckingSentence+3Complementary Evidence Identification in Open-Domain Question Answering
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple asp…
DiversityEvidence SelectionOpen-Domain Question AnsweringQuestion Answering