Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation | Paper Citation Record

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This paper cites https://deepmind.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation https://deepmind

Reference 1

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Observation e420d59f-78dc-4941-9f59-94a7e5a1999c · outbound

This paper cites https://platform.openai.com/docs/models/o1/.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation https://platform.openai.com/docs/models/o1/

Reference 2

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This paper cites Let the llms talk: Simu- lating human-to-human conversational qa via zero-shot llm-to-llm interactions.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Let the llms talk: Simu- lating human-to-human conversational qa via zero-shot llm-to-llm interactions

Reference 3

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This paper cites Chain-of-Thought Reasoning In The Wild Is Not Always Faithful.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Reference 4

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This paper cites Is github’s copilot as bad as humans at introducing vulnerabilities in code? Empirical Software Engineering , 28(6):129, 2023.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Is github’s copilot as bad as humans at introducing vulnerabilities in code? Empirical Software Engineering , 28(6):129, 2023

Reference 5

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Observation 05f384a1-10a6-4683-be98-6259d406079d · outbound

This paper cites Language Models are Few-Shot Learners.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Language Models are Few-Shot Learners

Reference 6

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Observation 045be678-fa73-44e7-a757-e3b6f3933bdc · outbound

This paper cites CodeT: Code Generation with Generated Tests.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeT: Code Generation with Generated Tests

Reference 7

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This paper cites Evaluating Large Language Models Trained on Code.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Evaluating Large Language Models Trained on Code

Reference 8

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This paper cites LocAgent: Graph-Guided LLM Agents for Code Localization.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation LocAgent: Graph-Guided LLM Agents for Code Localization

Reference 9

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Observation 80cf230c-d9ba-41d1-90f5-b62e0adc921f · outbound

This paper cites TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration

Reference 10

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Observation 32968c5c-115a-42f3-b597-5eced2799e7b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

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This paper cites Reasoning with Language Model is Planning with World Model.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Reasoning with Language Model is Planning with World Model

Reference 12

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Observation 0051cb81-9ce3-476a-8660-fb9c4255a904 · outbound

This paper cites CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation

Reference 13

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This paper cites Adaptive mixtures of local experts.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Adaptive mixtures of local experts

Reference 14

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This paper cites Devanbu, and Emily Morgan.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Devanbu, and Emily Morgan

Reference 15

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This paper cites Self- planning code generation with large language models,.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Self- planning code generation with large language models,

Reference 16

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Observation c72dfe49-e09c-4735-9f4c-533e027ca233 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 17

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This paper cites Grace: Discriminator- guided chain-of-thought reasoning.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Grace: Discriminator- guided chain-of-thought reasoning

Reference 18

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Observation cc63a596-dd09-4fde-9c68-dca2c4fcba1e · outbound

This paper cites Large language models are zero-shot reasoners.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Large language models are zero-shot reasoners

Reference 19

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Observation c3beeed5-0e39-4cf6-b8c5-fd66db7ff908 · outbound

This paper cites CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 20

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This paper cites CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules

Reference 21

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Observation 0695e282-24b3-438e-9c2b-e81d33b5cdbe · outbound

This paper cites Structured chain-of-thought prompting for code generation.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Structured chain-of-thought prompting for code generation

Reference 22

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Observation 882c4230-c241-4e77-bd71-ce8f3d35ecb4 · outbound

This paper cites CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models

Reference 23

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This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 24

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This paper cites Fastfixer: An efficient and effective approach for repair- ing programming assignments.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Fastfixer: An efficient and effective approach for repair- ing programming assignments

Reference 25

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This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023

Reference 26

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Observation 8e83878d-5eec-411e-9108-24a62f1728de · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

Reference 27

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Observation b52eba2d-4f23-4672-88a9-095b01315893 · outbound

This paper cites Refining chatgpt-generated code: Characterizing and mitigating code quality issues.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Refining chatgpt-generated code: Characterizing and mitigating code quality issues

Reference 28

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Observation 1257cabc-3675-4503-869c-657aec8b956a · outbound

This paper cites No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT

Reference 29

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation

Reference 30

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Clarifygpt: A framework for enhancing llm-based code generation via requirements clarification

Reference 31

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 32

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers

Reference 33

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Code Llama: Open Foundation Models for Code

Reference 34

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Amazon codewhisperer, 2024

Reference 35

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Calibration and Correctness of Language Models for Code

Reference 36

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 37

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Bugs in Large Language Models Generated Code: An Empirical Study

Reference 38

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Code repair with llms gives an exploration-exploitation tradeoff

Reference 39

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 40

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting

Reference 41

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Expectation vs

Reference 42

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This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Chain-of-thought prompting elicits reasoning in large language models

Reference 43

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This paper cites Using github copilot to solve simple programming problems.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Using github copilot to solve simple programming problems

Reference 44

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This paper cites Agentless: Demystifying LLM-based Software Engineering Agents.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Agentless: Demystifying LLM-based Software Engineering Agents

Reference 45

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This paper cites Demystifying llm-based software en- gineering agents.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Demystifying llm-based software en- gineering agents

Reference 46

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This paper cites Self- evaluation guided beam search for reasoning.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Self- evaluation guided beam search for reasoning

Reference 47

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Observation 6216f725-8dfc-4c42-a584-667df91f637a · outbound

This paper cites SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Reference 48

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Tree of thoughts: Deliberate problem solving with large language models

Reference 49

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This paper cites Framework for evalu- ating code generation ability of large language models.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Framework for evalu- ating code generation ability of large language models

Reference 50

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Observation 8dbffacd-d2e2-48b2-93b9-8ecc1bef1d57 · outbound

This paper cites Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks

Reference 51

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This paper cites Learning-based widget matching for migrating gui test cases.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Learning-based widget matching for migrating gui test cases

Reference 52

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This paper cites ToolChain: Efficient Action Space Navigation in Large Language Models with A Search.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

Reference 53

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Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Unresolved cited work

Reference 2024

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