Copertina

100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pacchiardi, Lorenzo, Cheke, Lucy G., Hernández-Orallo, José
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computation and Language Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers
di: Pacchiardi, Lorenzo, et al.
Pubblicazione: (2024)

Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
di: Testini, Irene, et al.
Pubblicazione: (2025)

PredictaBoard: Benchmarking LLM Score Predictability
di: Pacchiardi, Lorenzo, et al.
Pubblicazione: (2025)

Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture
di: Burden, John, et al.
Pubblicazione: (2025)

Collaboration is all you need: LLM Assisted Safe Code Translation
di: Karanjai, Rabimba, et al.
Pubblicazione: (2025)

Large Language Models aren't all that you need
di: Holla, Kiran Voderhobli, et al.
Pubblicazione: (2024)

Inferring Capabilities from Task Performance with Bayesian Triangulation
di: Burden, John, et al.
Pubblicazione: (2023)

Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs
di: Zhang, Weixing, et al.
Pubblicazione: (2025)

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following
di: Ren, Qingyu, et al.
Pubblicazione: (2025)

Cognitive Science-Inspired Evaluation of Core Capabilities for Object Understanding in AI
di: Rutar, Danaja, et al.
Pubblicazione: (2025)

Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
di: Lindo, Mariana, et al.
Pubblicazione: (2023)

Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores
di: Balkır, Esma, et al.
Pubblicazione: (2026)

Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
di: Chang, Kaiyan, et al.
Pubblicazione: (2024)

No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes
di: Cencerrado, Iván Vicente Moreno, et al.
Pubblicazione: (2025)

Visuospatial Perspective Taking in Multimodal Language Models
di: Prunty, Jonathan, et al.
Pubblicazione: (2026)

Conversational Complexity for Assessing Risk in Large Language Models
di: Burden, John, et al.
Pubblicazione: (2024)

Effects of structure on reasoning in instance-level Self-Discover
di: Gunasekara, Sachith, et al.
Pubblicazione: (2025)

Propaganda is all you need
di: Kronlund-Drouault, Paul
Pubblicazione: (2024)

Psychometric Personality Shaping Modulates Capabilities and Safety in Language Models
di: Fitz, Stephen, et al.
Pubblicazione: (2025)

REL: Working out is all you need
di: Simonds, Toby, et al.
Pubblicazione: (2024)

Bringing Comparative Cognition To Computers
di: Voudouris, Konstantinos, et al.
Pubblicazione: (2025)

Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances
di: Verel, Sébastien, et al.
Pubblicazione: (2024)

Compression is all you need: Modeling Mathematics
di: Aksenov, Vitaly, et al.
Pubblicazione: (2026)

Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning
di: Wang, Xiaoyu, et al.
Pubblicazione: (2023)

Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators
di: Cao, Yang Trista, et al.
Pubblicazione: (2023)

CTDGSI: A comprehensive exploitation of instance selection methods for automatic text classification. VII Concurso de Teses, Dissertações e Trabalhos de Graduação em SI -- XXI Simpósio Brasileiro de Sistemas de Informação
di: Cunha, Washington, et al.
Pubblicazione: (2025)

General Scales Unlock AI Evaluation with Explanatory and Predictive Power
di: Zhou, Lexin, et al.
Pubblicazione: (2025)

Tabular Data: Is Deep Learning all you need?
di: Zabërgja, Guri, et al.
Pubblicazione: (2024)

A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
di: Artelt, André, et al.
Pubblicazione: (2024)

Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!
di: Fang, Xiangxin, et al.
Pubblicazione: (2024)

Multi-agent AI systems outperform human teams in creativity
di: Hu, Tiancheng, et al.
Pubblicazione: (2026)

Seven simple steps for log analysis in AI systems
di: Dubois, Magda, et al.
Pubblicazione: (2026)

ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation
di: Brach, William, et al.
Pubblicazione: (2026)

Let your LLM generate a few tokens and you will reduce the need for retrieval
di: Déjean, Hervé
Pubblicazione: (2024)

Prompting Science Report 3: I'll pay you or I'll kill you -- but will you care?
di: Meincke, Lennart, et al.
Pubblicazione: (2025)

Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models
di: Perez-Herrera, Laura Valeria, et al.
Pubblicazione: (2026)

Layered LA-MAPF: a decomposition of large agent MAPF instance to accelerate solving without compromising solvability
di: Yao, Zhuo
Pubblicazione: (2024)

Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
di: Viswanath, Hrishikesh, et al.
Pubblicazione: (2023)

How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions
di: Bašaragin, Bojana, et al.
Pubblicazione: (2024)

How new data permeates LLM knowledge and how to dilute it
di: Sun, Chen, et al.
Pubblicazione: (2025)