Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters

• URL: http://arxiv.org/abs/2510.25860v1
• Date: Wed, 29 Oct 2025 18:03:44 GMT
• Title: Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters
• Authors: Xingjian Zhang, Tianhong Gao, Suliang Jin, Tianhao Wang, Teng Ye, Eytan Adar, Qiaozhu Mei,
• Abstract summary: Thinking traces are highly informative but challenging to collect and curate.<n>We present a human-LLM collaborative framework to infer thinking traces from label-only annotations.
• Score: 16.692860590587184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly used as raters for evaluation tasks. However, their reliability is often limited for subjective tasks, when human judgments involve subtle reasoning beyond annotation labels. Thinking traces, the reasoning behind a judgment, are highly informative but challenging to collect and curate. We present a human-LLM collaborative framework to infer thinking traces from label-only annotations. The proposed framework uses a simple and effective rejection sampling method to reconstruct these traces at scale. These inferred thinking traces are applied to two complementary tasks: (1) fine-tuning open LLM raters; and (2) synthesizing clearer annotation guidelines for proprietary LLM raters. Across multiple datasets, our methods lead to significantly improved LLM-human agreement. Additionally, the refined annotation guidelines increase agreement among different LLM models. These results suggest that LLMs can serve as practical proxies for otherwise unrevealed human thinking traces, enabling label-only corpora to be extended into thinking-trace-augmented resources that enhance the reliability of LLM raters.

Related papers

• Next Generation Active Learning: Mixture of LLMs in the Loop [7.786330678327967]
  We propose a novel active learning framework, replacing human annotators with labels generated through a Mixture-of-LLMs-based annotation model.<n>Our framework is built on lightweight LLMs, enabling it to operate fully on local machines in real-world applications.
  arXiv  Detail & Related papers  (2026-01-22T09:01:42Z)
• Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads [104.9566359759396]
  We propose a lightweight alternative for step-level reasoning verification based on data-driven uncertainty scores.<n>Our findings suggest that the internal states of LLMs encode their uncertainty and can serve as reliable signals for reasoning verification.
  arXiv  Detail & Related papers  (2025-11-09T03:38:29Z)
• LLM-Specific Utility: A New Perspective for Retrieval-Augmented Generation [110.610512800947]
  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge.<n>Existing studies often treat utility as a generic attribute, ignoring the fact that different LLMs may benefit differently from the same passage.
  arXiv  Detail & Related papers  (2025-10-13T12:57:45Z)
• Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks [18.695435335031355]
  In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution.<n>We conducted a pre-registered experiment with 410 unique annotators and over 7,000 annotations.<n>We find that presenting crowdworkers with LLM-generated annotation suggestions did not make them faster, but did improve their self-reported confidence in the task.
  arXiv  Detail & Related papers  (2025-07-21T17:29:21Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• Reinforcing Thinking through Reasoning-Enhanced Reward Models [6.636512424910708]
  Large Language Models (LLMs) exhibit great potential in complex multi-step reasoning through inference-time thinking.<n>LLMs struggle with deciding when to stop thinking due to limited self-awareness about their knowledge boundaries.<n>This work addresses these challenges by distilling the LLM's own reasoning processes into synthetic behavioral data.
  arXiv  Detail & Related papers  (2024-12-31T04:50:15Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators.<n>The question of how reliable these evaluators are has emerged as a crucial research question.<n>We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• 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)
• Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation [23.182787000804407]
  Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR) We propose a Reflective Reinforcement Large Language Model (Re2LLM) for SBR, guiding LLMs to focus on specialized knowledge essential for more accurate recommendations.
  arXiv  Detail & Related papers  (2024-03-25T05:12:18Z)
• 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)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)
• 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)

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.