:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Shi, Wenhang, Bian, Shuqing, Chen, Yiren, Zhang, Xinyi, Zhao, Zhe, Hu, Pengfei, Lu, Wei, Du, Xiaoyong
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2510.16686
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Joint Knowledge Editing for Information Enrichment and Probability Promotion
by: Shi, Wenhang, et al.
Published: (2024)

No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
by: Shi, Wenhang, et al.
Published: (2025)

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
by: Shi, Wenhang, et al.
Published: (2026)

Scaling Agentic Capabilities via Grounded Interaction Synthesis
by: Shi, Wenhang, et al.
Published: (2026)

Arch: An AI-Native Hardware Description Language for Register-Transfer Clocked Hardware Design
by: Zhao, Shuqing
Published: (2026)

LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding
by: Zhao, Haoyu, et al.
Published: (2024)

Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals
by: Wang, Yupei, et al.
Published: (2024)

MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction
by: Jiang, Han, et al.
Published: (2024)

How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty
by: Kim, Hazel H.
Published: (2024)

Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
by: Hu, Zhe, et al.
Published: (2024)

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
by: Zhu, Chiwei, et al.
Published: (2025)

Everything is Plausible: Investigating the Impact of LLM Rationales on Human Notions of Plausibility
by: Palta, Shramay, et al.
Published: (2025)

Large Language Model for Table Processing: A Survey
by: Lu, Weizheng, et al.
Published: (2024)

LQER: Low-Rank Quantization Error Reconstruction for LLMs
by: Zhang, Cheng, et al.
Published: (2024)

AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework
by: Fan, Meihao, et al.
Published: (2024)

PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
by: Wang, Jinyu, et al.
Published: (2025)

An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
by: Hu, Dou, et al.
Published: (2025)

DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models
by: Li, Zihao, et al.
Published: (2025)

Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models?
by: Yang, Zhihui, et al.
Published: (2025)

Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
by: Wang, Bing, et al.
Published: (2026)

Rationale Behind Essay Scores: Enhancing S-LLM's Multi-Trait Essay Scoring with Rationale Generated by LLMs
by: Chu, SeongYeub, et al.
Published: (2024)

NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging
by: Zhang, Weiming, et al.
Published: (2025)

Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding
by: Hariharan, Mohanakrishnan
Published: (2025)

RORA: Robust Free-Text Rationale Evaluation
by: Jiang, Zhengping, et al.
Published: (2024)

Understanding and Mitigating Language Confusion in LLMs
by: Marchisio, Kelly, et al.
Published: (2024)

Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis
by: Yuan, Lin, et al.
Published: (2025)

NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations
by: Chen, Junkai, et al.
Published: (2024)

A Two-Phase Recall-and-Select Framework for Fast Model Selection
by: Cui, Jianwei, et al.
Published: (2024)

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
by: Liao, Kuo, et al.
Published: (2024)

Efficient End-to-End Visual Document Understanding with Rationale Distillation
by: Zhu, Wang, et al.
Published: (2023)

Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding
by: Xiao, Yunpeng, et al.
Published: (2025)

From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs
by: Guo, Xiaoyong, et al.
Published: (2026)

DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
by: Zhang, Shaolei, et al.
Published: (2025)

Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation
by: Jia, Pengyue, et al.
Published: (2024)

Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
by: Li, Yun, et al.
Published: (2025)

SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
by: Xu, Tianyang, et al.
Published: (2024)

The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models
by: Chen, Xinyi, et al.
Published: (2023)

Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
by: Zheng, Xin, et al.
Published: (2024)

On-the-fly Denoising for Data Augmentation in Natural Language Understanding
by: Fang, Tianqing, et al.
Published: (2022)

InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
by: Wei, Zhepei, et al.
Published: (2024)