Cover Image

Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Liyan, Li, Jiangnan, Yu, Mo, Zhou, Jie
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings
by: Xu, Liyan, et al.
Published: (2025)

SIG: Speaker Identification in Literature via Prompt-Based Generation
by: Su, Zhenlin, et al.
Published: (2023)

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
by: Wang, Juyuan, et al.
Published: (2025)

Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
by: Xu, Liyan, et al.
Published: (2024)

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
by: Zhou, Chulun, et al.
Published: (2025)

STS: Efficient Sparse Attention with Speculative Token Sparsity
by: Xu, Ceyu, et al.
Published: (2026)

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning
by: Li, Chengye, et al.
Published: (2025)

Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions
by: Tsai, Chen Feng, et al.
Published: (2023)

Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
by: Yang, Jiuding, et al.
Published: (2024)

In-Context Fine-Tuning for Time-Series Foundation Models
by: Das, Abhimanyu, et al.
Published: (2024)

Synthetic Context Generation for Question Generation
by: Liu, Naiming, et al.
Published: (2024)

The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding
by: Yu, Mo, et al.
Published: (2025)

SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension
by: Wu, Junjie, et al.
Published: (2025)

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs
by: Huang, Yuxiang, et al.
Published: (2025)

VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
by: Tan, Yang, et al.
Published: (2025)

On the Shelf Life of Fine-Tuned LLM-Judges: Future-Proofing, Backward-Compatibility, and Question Generalization
by: Singh, Janvijay, et al.
Published: (2025)

Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
by: Li, Mo, et al.
Published: (2025)

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
by: Weng, Zixuan, et al.
Published: (2026)

Revealing Fine-Grained Values and Opinions in Large Language Models
by: Wright, Dustin, et al.
Published: (2024)

SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering
by: Yufan, Zhu, et al.
Published: (2024)

FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
by: Sun, Chaojie, et al.
Published: (2026)

MiA-Signature: Approximating Global Activation for Long-Context Understanding
by: Li, Yuqing, et al.
Published: (2026)

NarrationDep: Narratives on Social Media For Automatic Depression Detection
by: Zogan, Hamad, et al.
Published: (2024)

SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
by: Lu, Xudong, et al.
Published: (2024)

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
by: Lee, Chungpa, et al.
Published: (2026)

On Large Language Models' Hallucination with Regard to Known Facts
by: Jiang, Che, et al.
Published: (2024)

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning
by: Li, Ziheng, et al.
Published: (2026)

Parameter-Efficient Fine-Tuning via Circular Convolution
by: Chen, Aochuan, et al.
Published: (2024)

Beyond Imitation: Recovering Dense Rewards from Demonstrations
by: Li, Jiangnan, et al.
Published: (2025)

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
by: She, Jingyuan Selena, et al.
Published: (2023)

Narrative Consolidation: Formulating a New Task for Unifying Multi-Perspective Accounts
by: Finger, Roger A., et al.
Published: (2025)

In-Context Learning and Fine-Tuning GPT for Argument Mining
by: Cabessa, Jérémie, et al.
Published: (2024)

On the Loss of Context-awareness in General Instruction Fine-tuning
by: Wang, Yihan, et al.
Published: (2024)

From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
by: Fan, Shuxian, et al.
Published: (2024)

Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge
by: Yang, Zhuoyi, et al.
Published: (2026)

Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
by: Xiao, Wenyi, et al.
Published: (2024)

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
by: Ghosh, Bishwamittra, et al.
Published: (2026)

Latent Context Compilation: Distilling Long Context into Compact Portable Memory
by: Li, Zeju, et al.
Published: (2026)

Scalable Prompt Routing via Fine-Grained Latent Task Discovery
by: Zhang, Yunyi, et al.
Published: (2026)

A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text
by: Qamar, Faiza, et al.
Published: (2024)