mlsanityData to 7 Oct 2026

DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning

arXiv 2402.09136 ↗First posted 14 Feb 2024cs.CL cs.AIcited by 11

Not on the map. Why?

Abstract (arXiv)

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks, demonstrating new insights for future code instruction tuning work. Our key findings are: (1) Augmenting more diverse responses with distinct reasoning paths increases the code capability of LLMs. (2) Improving one's ability to evaluate the correctness of code solutions also enhances their ability to create it.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 37 of its 75 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

37 of 75 references.

References · 37

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  7. 2020Scaling Laws for Neural Language Modelscited by 6,411
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  9. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  10. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  11. 2021Program Synthesis with Large Language Modelscited by 3,509
  12. 2023Code Llama: Open Foundation Models for Codecited by 2,105
25 more
  1. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  2. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  3. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  4. 2023LIMA: Less Is More for Alignmentcited by 1,080
  5. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  6. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  7. 2023StarCoder: may the source be with you!cited by 790
  8. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  9. 2023MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuningcited by 483
  10. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
  11. 2023How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resourcescited by 405
  12. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 369
  13. 2023What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuningcited by 354
  14. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  15. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  16. 2023How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Compositioncited by 222
  17. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  18. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  19. 2023PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedbackcited by 69
  20. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  21. 2021Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learningcited by 35
  22. 2023Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Taskcited by 23
  23. 2023Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPTcited by 16
  24. 2023DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Taskcited by 5
  25. 2023DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extractioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 1.

11 papers.

  1. 2024CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Masterycited by 30
  2. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  3. 2024Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoningcited by 15
  4. 2025Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferencescited by 10
  5. 2024SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code Generationcited by 9
  6. 2025How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Codecited by 9
  7. 2024How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Datacited by 8
  8. 2024Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codescited by 5
  9. 2025Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategiescited by 3
  10. 2025Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactualscited by 1
  11. 2026Self-Improving Code Generation via Semantic Entropy and Behavioral Consensuscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
  2. 2026CodeEval: A pedagogical approach for targeted evaluation of code-trained Large Language Models
  3. 2025PACIFIC: a framework for generating benchmarks to check Precise Automatically Checked Instruction Following In Code
  4. 2023L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
  5. 2024SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
  6. 2023Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code
  7. 2023Large Language Models for Compiler Optimization
  8. 2024SelfCodeAlign: Self-Alignment for Code Generation