Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps | Paper Citation Record

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This paper cites Automatic semantic augmentation of language model prompts (for code summarization).

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Automatic semantic augmentation of language model prompts (for code summarization)

Reference 1

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This paper cites Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency

Reference 2

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Statically contextualizing large language models with typed holes

Reference 3

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This paper cites Correctness Assessment of Code Generated by Large Language Models Using Internal Representations.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Correctness Assessment of Code Generated by Large Language Models Using Internal Representations

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This paper cites Empirical evaluation of generalizable automated program repair with large language models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Empirical evaluation of generalizable automated program repair with large language models

Reference 5

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This paper cites Polyver: A compositional approach for polyglot system modeling and verification.arXiv preprint arXiv:2503.03207, 2025.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Polyver: A compositional approach for polyglot system modeling and verification.arXiv preprint arXiv:2503.03207, 2025

Reference 6

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This paper cites Verifying LLM-Generated Code in the Context of Software Verification with Ada/SPARK.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Verifying LLM-Generated Code in the Context of Software Verification with Ada/SPARK

Reference 7

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Observation 3098640c-b7b6-4d8c-b6fd-ed0e696e4511 · outbound

This paper cites Search- based llms for code optimization.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Search- based llms for code optimization

Reference 8

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Unresolved cited work

Reference 9

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This paper cites Two sides of the same coin: Exploiting the impact of identifiers in neural code comprehension.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Two sides of the same coin: Exploiting the impact of identifiers in neural code comprehension

Reference 10

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Unresolved cited work

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Observation 1bb36545-051f-46c3-8626-3f115dd663ba · outbound

This paper cites AST-T5: Structure-Aware Pretraining for Code Generation and Understanding.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps AST-T5: Structure-Aware Pretraining for Code Generation and Understanding

Reference 12

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

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 13

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Observation d39f1e12-fb07-426f-8f90-128b74e92af2 · outbound

This paper cites Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

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This paper cites Causality-aided evaluation and explanation of large language model- based code generation.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Causality-aided evaluation and explanation of large language model- based code generation

Reference 15

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This paper cites Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs

Reference 16

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Observation a3a90a15-9809-41cc-8e7a-0cad555d4be0 · outbound

This paper cites Can Large Language Models Understand Intermediate Representations in Compilers?.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Can Large Language Models Understand Intermediate Representations in Compilers?

Reference 17

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Observation 4ee7b8ce-d645-4885-9f5f-75d8168f942c · outbound

This paper cites Codecrash: Stress testing llm reasoning under structural and semantic perturbations.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Codecrash: Stress testing llm reasoning under structural and semantic perturbations

Reference 18

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This paper cites Nova: Generative language models for assem- bly code with hierarchical attention and con-trastive learning.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Nova: Generative language models for assem- bly code with hierarchical attention and con-trastive learning

Reference 19

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Observation 73b62a28-ecd5-498b-a597-f2bd8ae68ff2 · outbound

This paper cites Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges

Reference 20

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Observation 5c9d1f36-597c-4cd7-824c-e8a46ec163fd · outbound

This paper cites The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs

Reference 21

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This paper cites Large language model powered symbolic execution.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Large language model powered symbolic execution

Reference 22

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This paper cites Unleashing the power of compiler intermedi- ate representation to enhance neural program embeddings.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Unleashing the power of compiler intermedi- ate representation to enhance neural program embeddings

Reference 23

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This paper cites An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios

Reference 24

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps On the accuracy and robustness of large language models in chinese industrial scenarios

Reference 25

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This paper cites CCTEST: testing and repairing code completion systems.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps CCTEST: testing and repairing code completion systems

Reference 26

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This paper cites On the feasibility of specialized ability stealing for large language code models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps On the feasibility of specialized ability stealing for large language code models

Reference 27

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Split and merge: Aligning position biases in LLM-based evaluators

Reference 28

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Protect- ing intellectual property of large language model-based code genera- tion apis via watermarks

Reference 29

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models

Reference 30

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Api-guided dataset synthesis to finetune large code models

Reference 31

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Differentiation-based extraction of proprietary data from fine-tuned llms

Reference 32

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Large language models can be guided to evade ai-generated text de- tection

Reference 33

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This paper cites Safe delta: Consistently preserving safety when fine-tuning LLMs on diverse datasets.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Safe delta: Consistently preserving safety when fine-tuning LLMs on diverse datasets

Reference 34

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This paper cites Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend

Reference 35

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs

Reference 36

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Type-Constrained Code Generation with Language Models

Reference 37

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Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Static inference meets deep learning: a hybrid type inference approach for python

Reference 38

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This paper cites Do code llms do static analysis? arXiv preprint arXiv:2505.12118, 2025.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Do code llms do static analysis? arXiv preprint arXiv:2505.12118, 2025

Reference 39

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This paper cites ClassInvGen: Class Invariant Synthesis using Large Language Models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps ClassInvGen: Class Invariant Synthesis using Large Language Models

Reference 40

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This paper cites Beyond peft: Layer-wise optimization for more effective and efficient large code model tuning.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Beyond peft: Layer-wise optimization for more effective and efficient large code model tuning

Reference 41

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This paper cites A systematic evaluation of large code models in api suggestion: When, which, and how.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps A systematic evaluation of large code models in api suggestion: When, which, and how

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:16.967056Z

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Observation 4cd76a1e-a321-4dc6-8e37-b5db6af4359a · outbound

This paper cites Reef: A framework for collecting real-world vulnerabilities and fixes.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Reef: A framework for collecting real-world vulnerabilities and fixes

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:16.831682Z

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Observation 05347eba-95be-491b-808d-9dd930951f47 · outbound

This paper cites Enriching query semantics for code search with reinforcement learning.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Enriching query semantics for code search with reinforcement learning

Reference 44

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This paper cites Prompt tuning in code intelligence: An experimental evaluation.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Prompt tuning in code intelligence: An experimental evaluation

Reference 45

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Observation 05fbfc81-2de1-4897-b095-6659fc05c185 · outbound

This paper cites RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry

Reference 46

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Observation f155acf1-848c-410e-95ca-2df62dd551bf · outbound

This paper cites Llmdfa: Analyzing dataflow in code with large language models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Llmdfa: Analyzing dataflow in code with large language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:16.334249Z

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Observation e59aecd6-d868-442a-907c-d84efb3524cb · outbound

This paper cites Sanitizing large language models in bug detection with data- flow.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Sanitizing large language models in bug detection with data- flow

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:16.166849Z

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Observation 36e661c1-154b-414d-9ce0-bf21f9297c68 · outbound

This paper cites sem2vec: Semantics-aware assembly tracelet embedding.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps sem2vec: Semantics-aware assembly tracelet embedding

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:15.990057Z

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Observation 7ba10a8f-41dc-445b-a839-932a7d52d0fa · outbound

This paper cites Navrepair: Node-type aware c/c++ code vulnerability repair.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Navrepair: Node-type aware c/c++ code vulnerability repair

Reference 50

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:07:13.579493Z

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Observation bfe57d35-6010-4412-859f-85efe30fed51 · outbound

This paper cites Towards understanding the characteristics of code generation errors made by large language models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Towards understanding the characteristics of code generation errors made by large language models

Reference 51

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Observation 68b88d2d-fe9e-44b4-863d-4ec93b53e92a · outbound

This paper cites Zhang, and Qing Liao.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Zhang, and Qing Liao

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:15.846266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b120fb6-8323-42b3-93ed-7bfe96e8dce3 · outbound

This paper cites VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection

Reference 53

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Source-reported events for the cited work

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Observation 85a4fb0b-d822-469e-a6ed-68cb5c171ca9 · outbound

This paper cites Refining Decompiled C Code with Large Language Models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Refining Decompiled C Code with Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:12.671769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 626ee597-6526-4a41-ac5f-95d3d9a2b845 · outbound

This paper cites BinAug: Enhancing binary similarity analysis with low-cost input repairing.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps BinAug: Enhancing binary similarity analysis with low-cost input repairing

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:15.625325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6aefab1-75c1-44c2-9528-05cbc7695647 · outbound

This paper cites Decllm: Llm-augmented recompilable decompilation for enabling programmatic use of decom- piled code.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Decllm: Llm-augmented recompilable decompilation for enabling programmatic use of decom- piled code

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:15.322855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c1cf64ad-5fa3-4ef6-abe6-1dbbc5493176 · outbound

This paper cites Formal Mathematical Reasoning: A New Frontier in AI.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Formal Mathematical Reasoning: A New Frontier in AI

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:12.858438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3554da71-64e0-4298-94db-54b9212c0054 · outbound

This paper cites Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:12.908631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d213cb82-2dbe-478c-809e-2963fa48d993 · outbound

This paper cites Order matters: Semantic-aware neural networks for binary code similarity detection.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Order matters: Semantic-aware neural networks for binary code similarity detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:15.058094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df35b2bc-d5b2-44e2-929d-a44a29a9fa90 · outbound

This paper cites Low-Cost and Comprehensive Non-textual Input Fuzzing with LLM-Synthesized Input Generators.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Low-Cost and Comprehensive Non-textual Input Fuzzing with LLM-Synthesized Input Generators

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:13.053523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da9794cb-ebf7-43f7-82ac-2e96adba6eb8 · outbound

This paper cites An approach for API synthesis using large language models.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps An approach for API synthesis using large language models

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:07:13.214111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Reference resolution

Outbound references

Pith citing papers