Copertina

PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xue, Tianci, Wang, Ziqi, Li, Yixia, Chen, Yun, Chen, Guanhua
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning
di: Wang, Hanqing, et al.
Pubblicazione: (2024)

SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
di: Li, Yixia, et al.
Pubblicazione: (2024)

Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning
di: Ruan, Zhiwen, et al.
Pubblicazione: (2025)

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
di: Ruan, Zhiwen, et al.
Pubblicazione: (2026)

ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs
di: Yang, Yan, et al.
Pubblicazione: (2025)

LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy
di: Ruan, Zhiwen, et al.
Pubblicazione: (2025)

Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection
di: Huang, Hsiu-Yuan, et al.
Pubblicazione: (2024)

In-Context Examples Matter: Improving Emotion Recognition in Conversation with Instruction Tuning
di: Ma, Hui, et al.
Pubblicazione: (2025)

G2: Guided Generation for Enhanced Output Diversity in LLMs
di: Ruan, Zhiwen, et al.
Pubblicazione: (2025)

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
di: Zhu, Yutao, et al.
Pubblicazione: (2024)

Document Reconstruction Unlocks Scalable Long-Context RLVR
di: Xiao, Yao, et al.
Pubblicazione: (2026)

MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
di: Chen, Tao, et al.
Pubblicazione: (2023)

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning
di: Zhang, Jiajun, et al.
Pubblicazione: (2026)

Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis
di: Zheng, Guangmin, et al.
Pubblicazione: (2024)

TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
di: Zhu, He, et al.
Pubblicazione: (2025)

Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
di: Chen, Pinzhen, et al.
Pubblicazione: (2023)

Otter: A Multi-Modal Model with In-Context Instruction Tuning
di: Li, Bo, et al.
Pubblicazione: (2023)

GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks
di: Gupta, Shivanshu, et al.
Pubblicazione: (2023)

From Word to World: Can Large Language Models be Implicit Text-based World Models?
di: Li, Yixia, et al.
Pubblicazione: (2025)

MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning
di: Bai, Yi, et al.
Pubblicazione: (2026)

Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
di: Chen, Guanhua, et al.
Pubblicazione: (2024)

XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
di: Ding, Yifeng, et al.
Pubblicazione: (2024)

Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
di: Liu, Tianci, et al.
Pubblicazione: (2025)

Hint Tuning: Less Data Makes Better Reasoners
di: Fan, Siqi, et al.
Pubblicazione: (2026)

Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
di: Chen, Jiaao, et al.
Pubblicazione: (2023)

InstructDiff: Domain-Adaptive Data Selection via Differential Entropy for Efficient LLM Fine-Tuning
di: Su, Junyou, et al.
Pubblicazione: (2026)

Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
di: Zhu, Wenhao, et al.
Pubblicazione: (2025)

Towards Better Understanding Table Instruction Tuning: Decoupling the Effects from Data versus Models
di: Deng, Naihao, et al.
Pubblicazione: (2025)

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms
di: Xiao, Zeguan, et al.
Pubblicazione: (2025)

Towards Fair and Comprehensive Evaluation of Routers in Collaborative LLM Systems
di: Wu, Wanxing, et al.
Pubblicazione: (2026)

Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction
di: Chakma, Aunabil, et al.
Pubblicazione: (2026)

PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation
di: Xue, Yongfu
Pubblicazione: (2025)

Enhancing Uncertainty Estimation in LLMs with Expectation of Aggregated Internal Belief
di: Xiao, Zeguan, et al.
Pubblicazione: (2025)

JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning
di: Gao, Chang, et al.
Pubblicazione: (2023)

Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning
di: Yang, Zhe, et al.
Pubblicazione: (2023)

A Unified Causal View of Instruction Tuning
di: Chen, Lu, et al.
Pubblicazione: (2024)

Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
di: Liu, Shang, et al.
Pubblicazione: (2024)

Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs
di: Le, Chenqian, et al.
Pubblicazione: (2025)

Unveiling Over-Memorization in Finetuning LLMs for Reasoning Tasks
di: Ruan, Zhiwen, et al.
Pubblicazione: (2025)

ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
di: He, Xingwei, et al.
Pubblicazione: (2025)