Papers Long Form Question Answering
“Long Form Question Answering” 태그가 달린 논문 61편 · 필터 해제
GenerationPrograms: Fine-grained Attribution with Executable Programs
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their outputs, undermining verifiability and trus…
Document SummarizationLong Form Question AnsweringMulti-Document SummarizationQuestion Answering+1Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, suc…
Fact CheckingHallucinationLong Form Question AnsweringMedical Question Answering+2LaMP-QA: A Benchmark for Personalized Long-form Question Answering
Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has been relatively underexplored. This is mainly due to lack of resources f…
Answer GenerationFormLong Form Question AnsweringQuestion AnsweringAtomic Consistency Preference Optimization for Long-Form Question Answering
Large Language Models (LLMs) frequently produce factoid hallucinations - plausible yet incorrect answers. A common mitigation strategy is model alignment, which improves factual accuracy by training on curated factual an…
FormLong Form Question AnsweringQuestion AnsweringAn Empirical Study of Evaluating Long-form Question Answering
\Ac{LFQA} aims to generate lengthy answers to complex questions. This scenario presents great flexibility as well as significant challenges for evaluation. Most evaluations rely on deterministic metrics that depend on st…
FormInformativenessLarge Language ModelLong Form Question Answering+1MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend multi-agent multi-model reasoning to gen…
Long Form Question AnsweringQuestion AnsweringRerankingGenerate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution
Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in long-form question answering (LFQA) task…
counterfactualDomain AdaptationLong Form Question AnsweringQuestion Answering+2On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
Retrieval-augmented generation (RAG) has emerged as an approach to augment large language models (LLMs) by reducing their reliance on static knowledge and improving answer factuality. RAG retrieves relevant context snipp…
Long Form Question AnsweringQuestion AnsweringRAGRetrieval+1How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the Wild
In the age of misinformation, hallucination -- the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses -- represents the main risk for their global utility. Despite LLMs becoming incr…
ArticlesHallucinationLong Form Question AnsweringMachine Translation+2SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses. Instead of only relying on costly…
Long Form Question AnsweringQuestion AnsweringSentenceImproving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization
Ensuring contextual faithfulness in retrieval-augmented large language models (LLMs) is crucial for building trustworthy information-seeking systems, particularly in long-form question-answering (LFQA) scenarios. In this…
Long Form Question AnsweringQuestion AnsweringRetrievalTo Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation
Retrieval-Augmented Generation equips large language models with the capability to retrieve external knowledge, thereby mitigating hallucinations by incorporating information beyond the model's intrinsic abilities. Howev…
Long Form Question AnsweringQuestion AnsweringRetrievalRetrieval-augmented GenerationA Claim Decomposition Benchmark for Long-form Answer Verification
The advancement of LLMs has significantly boosted the performance of complex long-form question answering tasks. However, one prominent issue of LLMs is the generated "hallucination" responses that are not factual. Conse…
FormHallucinationLong Form Question AnsweringQuestion AnsweringRetrieving Contextual Information for Long-Form Question Answering using Weak Supervision
Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers are typically optimized towards informa…
FormLong Form Question AnsweringQuestion AnsweringRetrievalCALF: Benchmarking Evaluation of LFQA Using Chinese Examinations
Long-Form Question Answering (LFQA) refers to generating in-depth, paragraph-level responses to open-ended questions. Although lots of LFQA methods are developed, evaluating LFQA effectively and efficiently remains chall…
BenchmarkingLong Form Question AnsweringQuestion AnsweringAncient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy
LLMs have revolutionized the landscape of information retrieval and knowledge dissemination. However, their application in specialized areas is often hindered by factual inaccuracies and hallucinations, especially in lon…
Information RetrievalLong Form Question AnsweringPhilosophyQuestion Answering+3Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
Large Language Models (LLMs) have demonstrated significant capabilities, particularly in the domain of question answering (QA). However, their effectiveness in QA is often undermined by the vagueness of user questions. T…
Long Form Question AnsweringQuestion AnsweringLocalizing and Mitigating Errors in Long-form Question Answering
Long-form question answering (LFQA) aims to provide thorough and in-depth answers to complex questions, enhancing comprehension. However, such detailed responses are prone to hallucinations and factual inconsistencies, c…
FormHallucinationLong Form Question AnsweringQuestion AnsweringANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigation datasets are limited in domains and s…
HallucinationLong Form Question AnsweringNatural Language InferenceQuestion AnsweringGround Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. Recently, Attributed Text Generation (ATG) has attracted growing attention, which provide…
Fact CheckingLong Form Question AnsweringQuestion AnsweringRAG+4