Do Code Models Suffer from the Dunning-Kruger Effect?
Fuente:
arXiv
Saved in:
| Main Authors: | Singh, Mukul, Chatterjee, Somya, Radhakrishna, Arjun, Gulwani, Sumit |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Diffusion is a code repair operator and generator
by: Singh, Mukul, et al.
Published: (2025)
by: Singh, Mukul, et al.
Published: (2025)
Semantically Aligned Question and Code Generation for Automated Insight Generation
by: Singha, Ananya, et al.
Published: (2024)
by: Singha, Ananya, et al.
Published: (2024)
Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
by: Chopra, Bhavya, et al.
Published: (2024)
by: Chopra, Bhavya, et al.
Published: (2024)
Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
by: Singh, Mukul, et al.
Published: (2025)
by: Singh, Mukul, et al.
Published: (2025)
Tabularis Formatus: Predictive Formatting for Tables
by: Singh, Mukul, et al.
Published: (2025)
by: Singh, Mukul, et al.
Published: (2025)
LLM-Guided Compositional Program Synthesis
by: Khan, Ruhma, et al.
Published: (2025)
by: Khan, Ruhma, et al.
Published: (2025)
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
by: Liang, Jenny T., et al.
Published: (2025)
by: Liang, Jenny T., et al.
Published: (2025)
Testing the Effect of Code Documentation on Large Language Model Code Understanding
by: Macke, William, et al.
Published: (2024)
by: Macke, William, et al.
Published: (2024)
Scaling Competence, Shrinking Reasoning: Cognitive Signatures in Language Model Learning
by: Singh, Mukul, et al.
Published: (2025)
by: Singh, Mukul, et al.
Published: (2025)
A Critical Study of What Code-LLMs (Do Not) Learn
by: Anand, Abhinav, et al.
Published: (2024)
by: Anand, Abhinav, et al.
Published: (2024)
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
by: Mishra, Mayank, et al.
Published: (2024)
by: Mishra, Mayank, et al.
Published: (2024)
A Code Comprehension Benchmark for Large Language Models for Code
by: Havare, Jayant, et al.
Published: (2025)
by: Havare, Jayant, et al.
Published: (2025)
CodeMirage: Hallucinations in Code Generated by Large Language Models
by: Agarwal, Vibhor, et al.
Published: (2024)
by: Agarwal, Vibhor, et al.
Published: (2024)
CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking
by: Su, Zian, et al.
Published: (2024)
by: Su, Zian, et al.
Published: (2024)
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
by: Han, Hojae, et al.
Published: (2024)
by: Han, Hojae, et al.
Published: (2024)
ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages
by: Kammakomati, Mehant, et al.
Published: (2024)
by: Kammakomati, Mehant, et al.
Published: (2024)
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases
by: Liu, Xiangyan, et al.
Published: (2024)
by: Liu, Xiangyan, et al.
Published: (2024)
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
by: Lee, Seonghyeon, et al.
Published: (2024)
by: Lee, Seonghyeon, et al.
Published: (2024)
Advancing Language Models for Code-related Tasks
by: Tian, Zhao
Published: (2026)
by: Tian, Zhao
Published: (2026)
TEN: Table Explicitization, Neurosymbolically
by: Mehrotra, Nikita, et al.
Published: (2025)
by: Mehrotra, Nikita, et al.
Published: (2025)
A Survey on Large Language Models for Code Generation
by: Jiang, Juyong, et al.
Published: (2024)
by: Jiang, Juyong, et al.
Published: (2024)
Scaling Granite Code Models to 128K Context
by: Stallone, Matt, et al.
Published: (2024)
by: Stallone, Matt, et al.
Published: (2024)
Analyzing the Performance of Large Language Models on Code Summarization
by: Haldar, Rajarshi, et al.
Published: (2024)
by: Haldar, Rajarshi, et al.
Published: (2024)
Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
by: Takerngsaksiri, Wannita, et al.
Published: (2025)
by: Takerngsaksiri, Wannita, et al.
Published: (2025)
Mitigating Gender Bias in Code Large Language Models via Model Editing
by: Qin, Zhanyue, et al.
Published: (2024)
by: Qin, Zhanyue, et al.
Published: (2024)
CodeMind: Evaluating Large Language Models for Code Reasoning
by: Liu, Changshu, et al.
Published: (2024)
by: Liu, Changshu, et al.
Published: (2024)
ICE-Score: Instructing Large Language Models to Evaluate Code
by: Zhuo, Terry Yue
Published: (2023)
by: Zhuo, Terry Yue
Published: (2023)
Structure-aware Fine-tuning for Code Pre-trained Models
by: Wu, Jiayi, et al.
Published: (2024)
by: Wu, Jiayi, et al.
Published: (2024)
Code Sharing In Prediction Model Research: A Scoping Review
by: Sounack, Thomas, et al.
Published: (2026)
by: Sounack, Thomas, et al.
Published: (2026)
Exploring Language Model's Code Generation Ability with Auxiliary Functions
by: Lee, Seonghyeon, et al.
Published: (2024)
by: Lee, Seonghyeon, et al.
Published: (2024)
Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions
by: Sajadi, Amirali, et al.
Published: (2025)
by: Sajadi, Amirali, et al.
Published: (2025)
EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
by: Li, Jia, et al.
Published: (2024)
by: Li, Jia, et al.
Published: (2024)
IndustryCode: A Benchmark for Industry Code Generation
by: Zeng, Puyu, et al.
Published: (2026)
by: Zeng, Puyu, et al.
Published: (2026)
Rethinking Code Refinement: Learning to Judge Code Efficiency
by: Seo, Minju, et al.
Published: (2024)
by: Seo, Minju, et al.
Published: (2024)
Large Language Model Critics for Execution-Free Evaluation of Code Changes
by: Yadavally, Aashish, et al.
Published: (2025)
by: Yadavally, Aashish, et al.
Published: (2025)
CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
by: Wang, Chenlong, et al.
Published: (2025)
by: Wang, Chenlong, et al.
Published: (2025)
Exploring Data-Efficient Adaptation of Large Language Models for Code Generation
by: Jiang, Xue, et al.
Published: (2024)
by: Jiang, Xue, et al.
Published: (2024)
Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
by: Su, Zian, et al.
Published: (2024)
by: Su, Zian, et al.
Published: (2024)
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
by: Zhuo, Terry Yue, et al.
Published: (2024)
by: Zhuo, Terry Yue, et al.
Published: (2024)
Self-Explained Keywords Empower Large Language Models for Code Generation
by: Fan, Lishui, et al.
Published: (2024)
by: Fan, Lishui, et al.
Published: (2024)
Similar Items
-
Diffusion is a code repair operator and generator
by: Singh, Mukul, et al.
Published: (2025) -
Semantically Aligned Question and Code Generation for Automated Insight Generation
by: Singha, Ananya, et al.
Published: (2024) -
Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
by: Chopra, Bhavya, et al.
Published: (2024) -
Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
by: Singh, Mukul, et al.
Published: (2025) -
Tabularis Formatus: Predictive Formatting for Tables
by: Singh, Mukul, et al.
Published: (2025)