arXiv:2606. 07936v1 Announce Type: cross Abstract: Human evaluation plays a critical role in assessing the quality of generated text.
arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.
arXiv:2609.24516v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these s...
arXiv:2606. 09809v1 Announce Type: new Abstract: AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs.
arXiv:2609.23264v1 Announce Type: new Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
arXiv:2510. 02027v2 Announce Type: replace Abstract: Scholarly publishing requires scalable scrutiny supported by auditable evidence.
The paper introduces behavioral correctness assumptions as a new framework for evaluating reference-based automatic evaluation methods in natural language generation. It defines a taxonomy of correctness-preserving and correctness-altering assumptions and operationalizes them through controlled response transformations to specify expected scoring behaviors. The authors evaluate a range of lexical, character-level, semantic, LLM-based, and hybrid evaluators, analyzing their behavior across multiple dimensions and finding that no evaluator satisfies all assumptions, revealing distinct behavioral trade-offs not evident from aggregate scores.
The paper critiques the conventional method of averaging human ratings to evaluate natural language generation (NLG) systems, arguing that it relies on assumptions about annotators that are often violated, especially when using Likert scales. These violations can even reverse true preferences, leading to inaccurate system rankings. The authors propose a more theoretically sound protocol and introduce a new system-level probabilistic assessment (SPA) for open-ended tasks like story generation, which successfully recovers expected model orderings where the standard protocol fails.
The paper examines whether existing automatic methods can reliably assess creativity in text produced by large language models (LLMs). By collecting human ratings on 11 creativity dimensions for both human and AI short stories, the authors compare these judgments with automated metrics and LLM-as-a-Judge evaluations. The results show a significant misalignment: automated metrics and LLM judges favor AI-generated stories and show near-zero correlation with human assessments, revealing fundamental limitations in current computational approaches to evaluating creative text.
arXiv:2608. 03659v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers align with both conference decisions and human reviewing priorities within the same controlled setting.
arXiv:2606. 13670v1 Announce Type: new Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess whether the published findings can be recovered.
arXiv:2609.00241v1 Announce Type: new Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particular...
The paper "What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks" analyzes 14,767 arXiv submissions from 2022 to 2026 that introduce or update evaluation resources for large language models. It systematically maps changes in target systems, domains, evaluation materials, conditions, and scoring mechanisms, revealing a growing emphasis on action, interaction, and professional applications. The study also notes uneven development in model participation, with LLM-based scoring increasing in both agent and non-agent groups, while model-generated materials do not show a comparable rise.