Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Fuente:
arXiv
Saved in:
| Main Authors: | Cook, Jonathan, Sapora, Silvia, Ahmadian, Arash, Khan, Akbir, Rocktaschel, Tim, Foerster, Jakob, Ruis, Laura |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Debating with More Persuasive LLMs Leads to More Truthful Answers
by: Khan, Akbir, et al.
Published: (2024)
by: Khan, Akbir, et al.
Published: (2024)
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation
by: Cook, Jonathan, et al.
Published: (2024)
by: Cook, Jonathan, et al.
Published: (2024)
Investigating Non-Transitivity in LLM-as-a-Judge
by: Xu, Yi, et al.
Published: (2025)
by: Xu, Yi, et al.
Published: (2025)
Scaling Opponent Shaping to High Dimensional Games
by: Khan, Akbir, et al.
Published: (2023)
by: Khan, Akbir, et al.
Published: (2023)
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
by: Franzmeyer, Tim, et al.
Published: (2025)
by: Franzmeyer, Tim, et al.
Published: (2025)
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
by: Paglieri, Davide, et al.
Published: (2025)
by: Paglieri, Davide, et al.
Published: (2025)
LLM-First Search: Self-Guided Exploration of the Solution Space
by: Herr, Nathan, et al.
Published: (2025)
by: Herr, Nathan, et al.
Published: (2025)
Towards Reliable Evaluation of Behavior Steering Interventions in LLMs
by: Pres, Itamar, et al.
Published: (2024)
by: Pres, Itamar, et al.
Published: (2024)
Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs
by: Paglieri, Davide, et al.
Published: (2024)
by: Paglieri, Davide, et al.
Published: (2024)
Is Programming by Example solved by LLMs?
by: Li, Wen-Ding, et al.
Published: (2024)
by: Li, Wen-Ding, et al.
Published: (2024)
Meta-Learning Objectives for Preference Optimization
by: Alfano, Carlo, et al.
Published: (2024)
by: Alfano, Carlo, et al.
Published: (2024)
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs
by: Hu, Zichao, et al.
Published: (2024)
by: Hu, Zichao, et al.
Published: (2024)
The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning
by: Xu, Yi, et al.
Published: (2026)
by: Xu, Yi, et al.
Published: (2026)
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
by: Dang, John, et al.
Published: (2024)
by: Dang, John, et al.
Published: (2024)
DéjàQ: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems
by: Röpke, Willem, et al.
Published: (2026)
by: Röpke, Willem, et al.
Published: (2026)
Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training
by: Nguyen, Luong N.
Published: (2026)
by: Nguyen, Luong N.
Published: (2026)
On Instruction-Finetuning Neural Machine Translation Models
by: Raunak, Vikas, et al.
Published: (2024)
by: Raunak, Vikas, et al.
Published: (2024)
Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions
by: Rosser, J, et al.
Published: (2026)
by: Rosser, J, et al.
Published: (2026)
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
by: Kang, Katie, et al.
Published: (2024)
by: Kang, Katie, et al.
Published: (2024)
Evaluation of Finetuned LLMs in AMR Parsing
by: Ho, Shu Han
Published: (2025)
by: Ho, Shu Han
Published: (2025)
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
by: Paglieri, Davide, et al.
Published: (2024)
by: Paglieri, Davide, et al.
Published: (2024)
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
by: Samvelyan, Mikayel, et al.
Published: (2024)
by: Samvelyan, Mikayel, et al.
Published: (2024)
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition
by: Zhang, Ziyang, et al.
Published: (2024)
by: Zhang, Ziyang, et al.
Published: (2024)
Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion
by: Liu, Yang, et al.
Published: (2024)
by: Liu, Yang, et al.
Published: (2024)
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs
by: Arabelly, Abhinav, et al.
Published: (2025)
by: Arabelly, Abhinav, et al.
Published: (2025)
Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability
by: Raimondi, Bianca, et al.
Published: (2025)
by: Raimondi, Bianca, et al.
Published: (2025)
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
by: Li, Renhao, et al.
Published: (2024)
by: Li, Renhao, et al.
Published: (2024)
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
by: Zhao, Chenyang, et al.
Published: (2024)
by: Zhao, Chenyang, et al.
Published: (2024)
Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models
by: McDaniel, Emma, et al.
Published: (2024)
by: McDaniel, Emma, et al.
Published: (2024)
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
by: Lupu, Andrei, et al.
Published: (2025)
by: Lupu, Andrei, et al.
Published: (2025)
Chatting with Logs: An exploratory study on Finetuning LLMs for LogQL
by: Seshagiri, Vishwanath, et al.
Published: (2024)
by: Seshagiri, Vishwanath, et al.
Published: (2024)
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
by: Indurthi, Sathish Reddy, et al.
Published: (2024)
by: Indurthi, Sathish Reddy, et al.
Published: (2024)
FLUX: Data Worth Training On
by: Gowtham, et al.
Published: (2026)
by: Gowtham, et al.
Published: (2026)
Is Peer-Reviewing Worth the Effort?
by: Church, Kenneth, et al.
Published: (2024)
by: Church, Kenneth, et al.
Published: (2024)
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
by: Ye, Jiasheng, et al.
Published: (2023)
by: Ye, Jiasheng, et al.
Published: (2023)
Factorio Learning Environment
by: Hopkins, Jack, et al.
Published: (2025)
by: Hopkins, Jack, et al.
Published: (2025)
An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing
by: Chai, Ziwei, et al.
Published: (2024)
by: Chai, Ziwei, et al.
Published: (2024)
mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection
by: Macko, Dominik
Published: (2026)
by: Macko, Dominik
Published: (2026)
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
by: Ruis, Laura, et al.
Published: (2024)
by: Ruis, Laura, et al.
Published: (2024)
Analyzing Finetuning Representation Shift for Multimodal LLMs Steering
by: Khayatan, Pegah, et al.
Published: (2025)
by: Khayatan, Pegah, et al.
Published: (2025)
Similar Items
-
Debating with More Persuasive LLMs Leads to More Truthful Answers
by: Khan, Akbir, et al.
Published: (2024) -
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation
by: Cook, Jonathan, et al.
Published: (2024) -
Investigating Non-Transitivity in LLM-as-a-Judge
by: Xu, Yi, et al.
Published: (2025) -
Scaling Opponent Shaping to High Dimensional Games
by: Khan, Akbir, et al.
Published: (2023) -
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
by: Franzmeyer, Tim, et al.
Published: (2025)