title: Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG
    Evaluation Prompts
summary: Evaluating natural language generation (NLG) systems is challenging due to
  the diversity of valid outputs. While human evaluation is the gold standard, it
  suffers from inconsistencies, lack of standardisation, and demographic biases,
  limiting reproducibility. LLM-based evaluation offers a scalable alternative
  but is highly sensitive to prompt design, where small variations can lead to
  significant discrepancies. In this work, we propose an inversion learning
  method that learns effective reverse mappings from model outputs back to their
  input instructions, enabling the automatic generation of highly effective,
  model-specific evaluation prompts. Our method requires only a single evaluation
  sample and eliminates the need for time-consuming manual prompt engineering,
  thereby improving both efficiency and robustness. Our work contributes toward a
  new direction for more robust and efficient LLM-based evaluation.
paper_authors._id: 681889891bcdcf80a49d9ef8
paper_authors.name: Hanhua Hong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T14:07:10.803Z
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paper_authors.fullname: Hanhua Hong
paper_authors.type: user
paper_authors.user: kou199024
paper_authors._id: 681889891bcdcf80a49d9ef9
paper_authors.name: Chenghao Xiao
paper_authors.status: claimed_verified
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paper_authors._id: 63108cc834c7d77420b0fd68
paper_authors.avatarUrl: /avatars/2721e573a417a8ec0b81ee048c4b42ba.svg
paper_authors.fullname: chenghao xiao
paper_authors.type: user
paper_authors.user: gowitheflow
paper_authors._id: 681889891bcdcf80a49d9efa
paper_authors.name: Yang Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-05T15:18:25.522Z
paper_authors._id: 60f313f4adf471cbdf8bb66a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60f313f4adf471cbdf8bb66a/5NJFqnldE_0fdE_mEvz9V.jpeg
paper_authors.fullname: Yang Wang
paper_authors.type: user
paper_authors.user: yangwang825
paper_authors._id: 681889891bcdcf80a49d9efb
paper_authors.name: Yiqi Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T14:07:07.758Z
paper_authors._id: 6399fecbdfc6c5bb8f164073
paper_authors.avatarUrl: /avatars/e63ac1a4de53d1fe3e039d0e9dff5c25.svg
paper_authors.fullname: Yiqi Liu
paper_authors.type: user
paper_authors.user: Yliu566
paper_authors._id: 681889891bcdcf80a49d9efc
paper_authors.name: Wenge Rong
paper_authors._id: 681889891bcdcf80a49d9efd
paper_authors.name: Chenghua Lin
paper_title: Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG
    Evaluation Prompts
paper_summary: Evaluating natural language generation (NLG) systems is challenging due to
  the diversity of valid outputs. While human evaluation is the gold standard, it
  suffers from inconsistencies, lack of standardisation, and demographic biases,
  limiting reproducibility. LLM-based evaluation offers a scalable alternative
  but is highly sensitive to prompt design, where small variations can lead to
  significant discrepancies. In this work, we propose an inversion learning
  method that learns effective reverse mappings from model outputs back to their
  input instructions, enabling the automatic generation of highly effective,
  model-specific evaluation prompts. Our method requires only a single evaluation
  sample and eliminates the need for time-consuming manual prompt engineering,
  thereby improving both efficiency and robustness. Our work contributes toward a
  new direction for more robust and efficient LLM-based evaluation.
paper_ai_summary: Inversion learning automates the generation of effective evaluation prompts for language models, improving robustness and efficiency over manual processes.
paper_ai_keywords: natural language generation NLG human evaluation inconsistencies standardisation demographic biases LLM-based evaluation prompt design inversion learning reverse mappings input instructions single evaluation sample