Challenge: Using Large Language Models, code generation capabilities have transformed programming practices.
Approach: They analyze 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025 . they identify measurable trends in the evolution of coding style that align with LLM-generated code .
Outcome: The proposed study examines 20,000 GitHub repositories linked to arXiv papers . it finds that LLMs influence code style, and that they can be observed in real-world code .

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Adaptation of Large Language Models (2025.naacl-tutorial)

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Challenge: a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities.
Approach: This tutorial will provide an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques.
Outcome: This tutorial will outline dynamic, domain-specific, and task-adaptive LLM adaptation techniques.
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

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Challenge: Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
Approach: They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge.
Outcome: The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses.
A Study of LLMs’ Preferences for Libraries and Programming Languages (2026.findings-acl)

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Challenge: Existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.
Approach: They conduct the first systematic study of LLMs’ preferences for libraries and programming languages when generating code, covering eight different LLM.
Outcome: The proposed benchmarks show that LLMs prioritize familiarity and popularity over suitability and task-specific optimality.
CODEMENV: Benchmarking Large Language Models on Code Migration (2025.findings-acl)

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Challenge: Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored.
Approach: They propose a benchmark to evaluate large language models’ performance in handling code migration tasks.
Outcome: The proposed benchmark comprises 922 data points across 19 Python and Java packages and offers three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments.
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)

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Challenge: Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance.
Approach: They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process.
Outcome: Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models.
Can Language Models Replace Programmers for Coding? REPOCOD Says ‘Not Yet’ (2025.acl-long)

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Challenge: Existing benchmarks for code generation use short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.
Approach: They propose a Python code-generation benchmark that contains 980 whole-function generation tasks with realistic dependencies from 11 popular projects.
Outcome: The proposed benchmarks are short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

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Challenge: Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach: They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome: The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)

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Challenge: Large language models (LLMs) have shown impressive performance in code understanding and generation.
Approach: They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks.
Outcome: The proposed models have shown to be highly effective in coding tasks.
The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)

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Challenge: Recent development of large language models (LLMs) for code shows promise in achieving code intelligence.
Approach: They explore the ability of large language models to generate automated test cases . they show +11.77% and +4.22% higher code pass rates on HumanEval+ .
Outcome: The proposed models show higher pass rates on humanEval+ compared with the current state-of-the-art models.
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers (2024.naacl-long)

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Challenge: Recent advances in language modeling have caused disruptive shifts throughout AI research, spurring discussion about how the field is changing and how it should change.
Approach: They analyze a dataset of 16,979 LLM-related arXiv papers and examine industry and academic publishing trends.
Outcome: The authors examine the impact of large language models on AI research in 2023 and 2022.

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