Expert Preference-based Evaluation of Automated Related Work Generation

• URL: http://arxiv.org/abs/2508.07955v1
• Date: Mon, 11 Aug 2025 13:08:07 GMT
• Title: Expert Preference-based Evaluation of Automated Related Work Generation
• Authors: Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych,
• Abstract summary: We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences.<n>For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs.
• Score: 54.29459509574242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Expert domain writing, such as scientific writing, typically demands extensive domain knowledge. Recent advances in LLMs show promising potential in reducing the expert workload. However, evaluating the quality of automatically generated scientific writing is a crucial open issue, as it requires knowledge of domain-specific evaluation criteria and the ability to discern expert preferences. Conventional automatic metrics and LLM-as-a-judge systems are insufficient to grasp expert preferences and domain-specific quality standards. To address this gap and support human-AI collaborative writing, we focus on related work generation, one of the most challenging scientific tasks, as an exemplar. We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences. Instead of assigning a single score, our framework decomposes the evaluation into fine-grained dimensions. This localized evaluation approach is further augmented with contrastive few-shot examples to provide detailed contextual guidance for the evaluation dimensions. The design principles allow our framework to deliver cardinal assessment of quality, which can facilitate better post-training compared to ordinal preference data. For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs. Empirical investigation reveals that our framework is able to assess the quality of related work sections in a much more robust manner compared to standard LLM judges, reflects natural scenarios of scientific writing, and bears a strong correlation with the human expert assessment. We also observe that generations from state-of-the-art LLMs struggle to satisfy validation constraints of a suitable related work section. They (mostly) fail to improve based on feedback as well.

Related papers

• DeepQuali: Initial results of a study on the use of large language models for assessing the quality of user stories [0.40451653578314795]
  Generative artificial intelligence (GAI) is increasingly used in software engineering, mainly for coding tasks.<n>The current focus of using GAI for requirements is on eliciting, transforming, and classifying requirements, not on quality assessment.<n>We propose and evaluate the LLM-based (GPT-4o) approach "DeepQuali", for assessing and improving requirements quality in agile software development.
  arXiv  Detail & Related papers  (2026-02-09T16:49:54Z)
• Automated Benchmark Generation from Domain Guidelines Informed by Bloom's Taxonomy [28.293009223912602]
  Open-ended question answering (QA) evaluates a model's ability to perform contextualized reasoning beyond factual recall.<n>This challenge is especially acute in practice-based domains, where knowledge is procedural and grounded in professional judgment.<n>We introduce a framework for automated benchmark generation from expert-authored guidelines informed by Bloom's taxonomy.
  arXiv  Detail & Related papers  (2026-01-28T05:01:11Z)
• Reward Modeling for Scientific Writing Evaluation [50.33952894976367]
  It is critical to develop models that can be reliably deployed for evaluating diverse open-ended scientific writing tasks.<n>We propose cost-efficient, open-source reward models tailored for scientific writing evaluation.
  arXiv  Detail & Related papers  (2026-01-16T15:32:58Z)
• OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models [55.21589313404023]
  OpenReview is a continually evolving repository of research papers, peer reviews, author rebuttals, meta-reviews, and decision outcomes.<n>We highlight three promising areas in which OpenReview can uniquely contribute: enhancing the quality, scalability, and accountability of peer review processes; enabling meaningful, open-ended benchmarks rooted in genuine expert deliberation; and supporting alignment research through real-world interactions reflecting expert assessment, intentions, and scientific values.<n>We suggest the community collaboratively explore standardized benchmarks and usage guidelines around OpenReview, inviting broader dialogue on responsible data use, ethical considerations, and collective stewardship.
  arXiv  Detail & Related papers  (2025-05-24T09:07:13Z)
• Multi-Agent LLM Judge: automatic personalized LLM judge design for evaluating natural language generation applications [0.0]
  Large Language Models (LLMs) have demonstrated impressive performance across diverse domains, yet they still encounter challenges such as insufficient domain-specific knowledge, biases, and hallucinations.<n>Traditional evaluation methods, which rely on word overlap or text embeddings, are inadequate for capturing the nuanced semantic information necessary to evaluate dynamic, open-ended text generation.<n>We propose a novel dynamic multi-agent system that automatically designs personalized LLM judges for various natural language generation applications.
  arXiv  Detail & Related papers  (2025-04-01T09:36:56Z)
• DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation [11.010557279355885]
  This study empirically analyzes benchmark comments using a novel set of criteria informed by prior research and developer interviews.<n>Our evaluation framework, DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a comprehensive reassessment of current techniques.
  arXiv  Detail & Related papers  (2024-12-24T08:53:54Z)
• CARMO: Dynamic Criteria Generation for Context-Aware Reward Modelling [27.86204841898399]
  Reward modeling in large language models is susceptible to reward hacking.<n>We propose Context-Aware Reward Modeling (CARMO) to mitigate this problem.<n>We establish a new state-of-the-art performance in zero-shot settings for generative models, achieving a 2.1% improvement on Reward Bench.
  arXiv  Detail & Related papers  (2024-10-28T21:18:49Z)
• Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form Text [12.879551933541345]
  Large Language Models (LLMs) are capable of generating human-like conversations. Conventional metrics like BLEU and ROUGE are inadequate for capturing the subtle semantics and contextual richness of such generative outputs. We propose a reference-guided verdict method that automates the evaluation process by leveraging multiple LLMs-as-judges.
  arXiv  Detail & Related papers  (2024-08-17T16:01:45Z)
• HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition [92.17397504834825]
  HD-Eval is a framework that iteratively aligns large language models evaluators with human preference. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators.
  arXiv  Detail & Related papers  (2024-02-24T08:01:32Z)
• Leveraging Large Language Models for NLG Evaluation: Advances and Challenges [57.88520765782177]
  Large Language Models (LLMs) have opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
  arXiv  Detail & Related papers  (2024-01-13T15:59:09Z)
• Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems [0.0]
  The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies have begun to investigate fairness evaluations for LLMs within domains such as recommendations. Yet, the degree to which current fairness evaluation frameworks account for personalization remains unclear.
  arXiv  Detail & Related papers  (2024-01-08T17:57:29Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans.<n>This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning)<n>We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• Calibrating LLM-Based Evaluator [92.17397504834825]
  We propose AutoCalibrate, a multi-stage, gradient-free approach to calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration.
  arXiv  Detail & Related papers  (2023-09-23T08:46:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.