Pith. sign in

REVIEW 8 cited by

Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2503.01245 v2 pith:2IYX42NE submitted 2025-03-03 cs.SE cs.LG

Nam Huynh , Beiyu Lin This is my paper
classification cs.SEcs.LG
keywords codellmsgenerationsurveytaskstechniquesapplicationschallenges
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code. We begin with understanding LLMs' limitations and challenges in automated code generation. Subsequently, we review various fine-tuning techniques designed to enhance both the performance and adaptability of LLMs in code generation tasks. We then review the existing metrics and benchmarks for evaluations to assess model performance based on fine-tuning techniques. Finally, we explore the applications of LLMs (e.g. CodeLlama, GitHub Copilot, ToolGen) in code generation tasks to illustrate their roles and functionalities. This survey provides a comprehensive overview of LLMs for code generation, helps researchers in diverse fields better understand the current state-of-the-art technologies, and offers the potential of effectively leveraging LLMs for code generation tasks.

Discussion (0). Sign in to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development

    cs.SE 2026-07 conditional novelty 6.0 of 10

    A traceability-graph-driven multi-agent framework improves LLM-based repository-level code generation from use cases, reaching 53-57% functional success on ETOUR/SMOS versus 13-25% for ChatDev and MetaGPT.

  2. Can Vibe Coding Beat Graduate CS Students? An LLM vs. Human Coding Tournament on Market-driven Strategic Planning

    cs.LG 2025-11 conditional novelty 6.0 of 10

    Student-coded agents from a 2020 logistics course beat 40 LLM-coded agents in a bidding-and-delivery tournament, with 33 of 40 AI agents losing to a simple baseline.

  3. Testing chatbots on the creation of encoders for audio conditioned image generation

    cs.SD 2025-09 conditional novelty 6.0 of 10

    All chatbot-designed audio encoders failed to align with CLIP text embeddings and produced incoherent images, while showing a surprising architectural similarity across chatbots.

  4. Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Across 10 LLMs and 5 prompting strategies, curated context yields the best syntactic quality, yet simulation fidelity stays near zero for nearly all configurations.

  5. RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation

    cs.SE 2026-02 conditional novelty 5.0 of 10

    In a 631-case enterprise benchmark, iterative retrieval (CoRAG) achieved 10.29% exact match vs 7.45% for standard RAG on combined SQL/API generation with hybrid documentation, while no-RAG gives 0% exact match.

  6. Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT

    cs.CY 2025-06 unverdicted novelty 5.0 of 10

    Four worked case studies show generative AI can improve claim cost models, automate annual report analysis, classify car damage, and migrate actuarial code, with notebooks on GitHub.

  7. Exp2VLA: Enabling Vision-Language-Action for Drone Navigation from Expert Demonstrations

    cs.RO 2026-07 conditional novelty 4.0 of 10

    Expert demonstrations distilled into LeRobot-format data let fine-tuned mid-scale VLAs navigate multi-object drone scenes from language, with partial zero-shot color-shape generalization in simulation and SITL.

  8. Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps

    cs.SE 2025-07 unverdicted novelty 2.0 of 10

    A position paper arguing that PL techniques, especially formal verification and structure-aware representations, should be deeply integrated into LLM code generation.

Pith tools