mlsanityData to 7 Oct 2026

Creative Beam Search: LLM-as-a-Judge For Improving Response Generation

arXiv 2405.00099 ↗First posted 30 Apr 2024cs.AI cs.CL cs.HCcited by 13

Not on the map. Why?

Abstract (arXiv)

Large language models are revolutionizing several areas, including artificial creativity. However, the process of generation in machines profoundly diverges from that observed in humans. In particular, machine generation is characterized by a lack of intentionality and an underlying creative process. We propose a method called Creative Beam Search that uses Diverse Beam Search and LLM-as-a-Judge to perform response generation and response validation. The results of a qualitative experiment show how our approach can provide better output than standard sampling techniques. We also show that the response validation step is a necessary complement to the response generation step.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

17 of 31 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2017Deep reinforcement learning from human preferencescited by 3,846
  5. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  7. 2023Large Language Models are not Fair Evaluatorscited by 1,050
  8. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 729
  9. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  10. 2017Lexically Constrained Decoding for Sequence Generation Using Grid Beam Searchcited by 234
  11. 2023Self-Evaluation Guided Beam Search for Reasoningcited by 195
  12. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 176
5 more
  1. 2018Analyzing Uncertainty in Neural Machine Translationcited by 126
  2. 2018Language GANs Falling Shortcited by 97
  3. 2022Putting GPT-3's Creativity to the (Alternative Uses) Testcited by 62
  4. 2023Bits of Grass: Does GPT already know how to write like Whitman?cited by 13
  5. 2023Is AI Art Another Industrial Revolution in the Making?cited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 6.

13 papers.

Sort:

By area:

  1. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
  2. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 129
  3. 2025Creative Preference Optimizationcited by 24
  4. 2025Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Needcited by 12
  5. 2024Creativity in AI: Progresses and Challengescited by 7
  6. 2024Do LLMs Agree on the Creativity Evaluation of Alternative Uses?cited by 5
  7. 2024A Transformer Based Generative Chemical Language AI Model for Structural Elucidation of Organic Compoundscited by 4
  8. 2026Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplerscited by 2
  9. 2026Typologically-Informed Candidate Reranking for LLM-based Translation into Low-Resource Languagescited by 0
  10. 2026Playing with Words, Improving with Rewards: Training Language Models for Creative Associationcited by 0
  11. 2026Anchorless Diversification for Parallel LLM Ideationcited by 0
  12. 2026Coherence-Oriented Dream Scene Visualisationcited by 0
1 more
  1. 2026Recipes for Creativity: Iterative Generation and Evaluation in Large Language Modelscited by 0