Cover Image

Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Raja, Rahul, Vats, Arpita
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RADAR: Mechanistic Pathways for Detecting Data Contamination in LLM Evaluation
by: Kattamuri, Ashish, et al.
Published: (2025)

Counterfactual Risk Minimization with IPS-Weighted BPR and Self-Normalized Evaluation in Recommender Systems
by: Raja, Rahul, et al.
Published: (2025)

Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis
by: Fartale, Harshwardhan, et al.
Published: (2025)

Beyond Nearest Neighbors: Semantic Compression and Graph-Augmented Retrieval for Enhanced Vector Search
by: Raja, Rahul, et al.
Published: (2025)

Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review
by: Raja, Rahul, et al.
Published: (2025)

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment
by: Peng, Fred Zhangzhi, et al.
Published: (2026)

Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
by: Thulke, David, et al.
Published: (2024)

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
by: Casademunt, Helena, et al.
Published: (2025)

DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
by: Chen, Qin, et al.
Published: (2025)

Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures
by: Raja, Rahul, et al.
Published: (2025)

A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
by: Raja, Rahul, et al.
Published: (2025)

Instruction Fine-Tuning: Does Prompt Loss Matter?
by: Huerta-Enochian, Mathew, et al.
Published: (2024)

Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
by: Ayala, Orlando Marquez, et al.
Published: (2025)

PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
by: Kim, Gyeongman, et al.
Published: (2024)

ToPT: Task-Oriented Prompt Tuning for Urban Region Representation Learning
by: Guo, Zitao, et al.
Published: (2026)

Prompt Optimization Meets Subspace Representation Learning for Few-shot Out-of-Distribution Detection
by: Sayem, Faizul Rakib, et al.
Published: (2025)

Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
by: Ajwani, Rohan Deepak, et al.
Published: (2024)

Understanding Prompt Tuning and In-Context Learning via Meta-Learning
by: Genewein, Tim, et al.
Published: (2025)

Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
by: Wang, Xinyu, et al.
Published: (2026)

A Graph Prompt Fine-Tuning Method for WSN Spatio-Temporal Correlation Anomaly Detection
by: Ye, Miao, et al.
Published: (2026)

Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
by: Brouwer, Eric, et al.
Published: (2024)

DynaPrompt: Dynamic Test-Time Prompt Tuning
by: Xiao, Zehao, et al.
Published: (2025)

Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
by: Hu, Shengchao, et al.
Published: (2024)

Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
by: Jain, Abhinav, et al.
Published: (2024)

Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations
by: Yao, Yue, et al.
Published: (2024)

Edge Prompt Tuning for Graph Neural Networks
by: Fu, Xingbo, et al.
Published: (2025)

Universal Prompt Tuning for Graph Neural Networks
by: Fang, Taoran, et al.
Published: (2022)

Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
by: Soylu, Dilara, et al.
Published: (2024)

Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
by: Lan, Pengxiang, et al.
Published: (2024)

Assessing the Impact of Prompting Methods on ChatGPT's Mathematical Capabilities
by: Chen, Yuhao, et al.
Published: (2023)

Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution
by: Dubanowska, Zuzanna, et al.
Published: (2025)

Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
by: Anil, Gautham Govind, et al.
Published: (2025)

SGPT: Few-Shot Prompt Tuning for Signed Graphs
by: Zhai, Zian, et al.
Published: (2024)

Learning from Generalization Patterns: An Evaluation-Driven Approach to Enhanced Data Augmentation for Fine-Tuning Small Language Models
by: Song, Huan, et al.
Published: (2025)

ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
by: Bai, Yichen, et al.
Published: (2023)

Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024)

Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
by: Choi, Yunseon, et al.
Published: (2024)

Subgraph-level Universal Prompt Tuning
by: Lee, Junhyun, et al.
Published: (2024)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
by: Weng, Pei-Yau, et al.
Published: (2025)

Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs
by: Zhao, Yushang, et al.
Published: (2025)