Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals

• URL: http://arxiv.org/abs/2509.08809v1
• Date: Wed, 10 Sep 2025 17:42:41 GMT
• Title: Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
• Authors: Cheng Chen, Haiyan Yin, Ivor Tsang,
• Abstract summary: Large Language Models (LLMs), when paired with prompt-based tasks, have significantly reduced data annotation costs and reliance on human annotators.<n>We propose a novel agentic annotation paradigm, where a student model collaborates with a noisy teacher to assess and refine annotation quality without relying on oracle feedback.<n>We introduce the Consistent and Inconsistent (CAI) Ratio, a novel unsupervised evaluation metric.
• Score: 9.145863861037862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs), when paired with prompt-based tasks, have significantly reduced data annotation costs and reliance on human annotators. However, evaluating the quality of their annotations remains challenging in dynamic, unsupervised environments where oracle feedback is scarce and conventional methods fail. To address this challenge, we propose a novel agentic annotation paradigm, where a student model collaborates with a noisy teacher (the LLM) to assess and refine annotation quality without relying on oracle feedback. The student model, acting as an unsupervised feedback mechanism, employs a user preference-based majority voting strategy to evaluate the consistency of the LLM outputs. To systematically measure the reliability of LLM-generated annotations, we introduce the Consistent and Inconsistent (CAI) Ratio, a novel unsupervised evaluation metric. The CAI Ratio not only quantifies the annotation quality of the noisy teacher under limited user preferences but also plays a critical role in model selection, enabling the identification of robust LLMs in dynamic, unsupervised environments. Applied to ten open-domain NLP datasets across four LLMs, the CAI Ratio demonstrates a strong positive correlation with LLM accuracy, establishing it as an essential tool for unsupervised evaluation and model selection in real-world settings.

Related papers

• On Evaluating LLM Alignment by Evaluating LLMs as Judges [68.15541137648721]
  evaluating large language models' (LLMs) alignment requires them to be helpful, honest, safe, and to precisely follow human instructions.<n>We examine the relationship between LLMs' generation and evaluation capabilities in aligning with human preferences.<n>We propose a benchmark that assesses alignment without directly evaluating model outputs.
  arXiv  Detail & Related papers  (2025-11-25T18:33:24Z)
• Self-Evolving Critique Abilities in Large Language Models [59.861013614500024]
  This paper explores enhancing critique abilities of Large Language Models (LLMs)<n>We introduce SCRIT, a framework that trains LLMs with self-generated data to evolve their critique abilities.<n>Our analysis reveals that SCRIT's performance scales positively with data and model size.
  arXiv  Detail & Related papers  (2025-01-10T05:51:52Z)
• Towards Understanding the Robustness of LLM-based Evaluations under Perturbations [9.944512689015998]
  Large Language Models (LLMs) can serve as automatic evaluators for non-standardized metrics in summarization and dialog-based tasks.<n>We conduct experiments across multiple prompting strategies to examine how LLMs fare as quality evaluators when compared with human judgments.
  arXiv  Detail & Related papers  (2024-12-12T13:31:58Z)
• Your Weak LLM is Secretly a Strong Teacher for Alignment [19.33906256866585]
  Existing alignment frameworks present constraints either in the form of expensive human effort or high computational costs.<n>This paper explores a promising middle ground, where we employ a weak LLM that is significantly less resource-intensive than top-tier models.<n>We show that weak LLMs can provide feedback that rivals or even exceeds that of fully human-annotated data.
  arXiv  Detail & Related papers  (2024-09-13T13:24:52Z)
• Large Language Models are Inconsistent and Biased Evaluators [2.136983452580014]
  We show that Large Language Models (LLMs) are biased evaluators as they exhibit familiarity bias and show skewed distributions of ratings. We also found that LLMs are inconsistent evaluators, showing low "inter-sample" agreement and sensitivity to prompt differences that are insignificant to human understanding of text quality.
  arXiv  Detail & Related papers  (2024-05-02T20:42:28Z)
• FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom [19.104850413126066]
  Federated Learning (FL) has emerged as a promising solution for collaborative training of large language models (LLMs) Traditional evaluation methods that rely on labeled test sets and similarity-based metrics cover only a subset of the acceptable answers. We propose FedEval-LLM that provides reliable performance measurements of LLMs on downstream tasks without the reliance on labeled test sets and external tools.
  arXiv  Detail & Related papers  (2024-04-18T15:46:26Z)
• An In-depth Evaluation of Large Language Models in Sentence Simplification with Error-based Human Assessment [9.156064716689833]
  This study provides in-depth insights into LLMs' performance while ensuring the reliability of the evaluation.<n>We select both closed-source and open-source LLMs, including GPT-4, Qwen2.5-72B, and Llama-3.2-3B.<n>Results show that LLMs generally generate fewer erroneous simplification outputs compared to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2024-03-08T00:19:24Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models [52.15056231665816]
  We propose a framework for self-supervised evaluation of Large Language Models (LLMs) We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence. We find strong correlations between self-supervised and human-supervised evaluations.
  arXiv  Detail & Related papers  (2023-06-23T17:59:09Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)
• Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization [66.08074487429477]
  We investigate the stability and reliability of large language models (LLMs) as automatic evaluators for abstractive summarization. We find that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements.
  arXiv  Detail & Related papers  (2023-05-22T14:58:13Z)

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.