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AdAEM: An Adaptively and Automated Extensible Evaluation Method of LLMs' Value Difference

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
LLM EvaluationValue EvaluationValue AlignmentDynamic Evaluation
Abstract:

Assessing Large Language Models (LLMs)' underlying value differences enables comprehensive comparison of their misalignment, cultural adaptability, and biases. Nevertheless, current value measurement methods face the informativeness challenge: with often outdated, contaminated, or generic test questions, they can only capture the orientations on comment safety values, e.g., HHH, shared among different LLMs, leading to indistinguishable and uninformative results. To address this problem, we introduce AdAEM, a novel, self-extensible evaluation algorithm for revealing LLMs' inclinations. Distinct from static benchmarks, AdAEM automatically and adaptively generates and extends its test questions. This is achieved by probing the internal value boundaries of a diverse set of LLMs developed across cultures and time periods in an in-context optimization manner. Such a process theoretically maximizes an information-theoretic objective to extract diverse controversial topics that can provide more distinguishable and informative insights about models' value differences. In this way, AdAEM is able to co-evolve with the development of LLMs, consistently tracking their value dynamics. We use AdAEM to generate novel questions and conduct an extensive analysis, demonstrating our method's validity and effectiveness, laying the groundwork for better interdisciplinary research on LLMs' values and alignment.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces AdAEM, a self-extensible algorithm for evaluating value differences across LLMs by dynamically generating test questions through in-context optimization. It resides in the 'Adaptive Value Evaluation Methods' leaf, which contains only three papers total, making this a relatively sparse research direction within the broader taxonomy. This leaf explicitly excludes static benchmarks with fixed question sets, positioning AdAEM as part of an emerging cluster focused on dynamic, context-sensitive value measurement rather than traditional psychometric approaches.

The taxonomy reveals that AdAEM's immediate neighbors include static 'Value Measurement Benchmarks' (e.g., Valuebench) and 'Heterogeneous Value Alignment' frameworks assessing multiple conflicting objectives. Nearby branches address reinforcement learning-based value optimization and behavioral consistency checks, but these focus on training-time alignment or action validation rather than adaptive diagnostic measurement. The scope notes clarify that AdAEM's dynamic question generation distinguishes it from fixed-item psychometric tools, while its focus on value orientation assessment separates it from optimization-focused RL methods.

Among 26 candidates examined, each of AdAEM's three contributions shows at least one refutable candidate. Contribution A (the core algorithm) examined 9 papers with 1 potential refutation; Contribution B (information-theoretic objective) examined 7 with 1 refutation; Contribution C (AdAEM Bench) examined 10 with 1 refutation. The statistics suggest that within this limited search scope, each contribution encounters some overlapping prior work, though the majority of examined candidates (23 of 26 total) do not clearly refute the claims. The sparse leaf structure and modest refutation counts indicate moderate novelty relative to the examined literature.

Based on top-26 semantic matches, AdAEM appears to occupy a less-crowded niche within value evaluation, though the limited search scope and presence of refutable candidates for all contributions suggest caution. The analysis captures adaptive value measurement methods but does not exhaustively cover static benchmarking or optimization-focused RL literature, which may contain additional relevant comparisons. The taxonomy structure confirms that dynamic, self-extensible evaluation remains an emerging area with fewer established precedents than static assessment frameworks.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
26
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: adaptive value evaluation of large language models. The field has grown into a rich landscape organized around several major branches. Value Alignment and Orientation Assessment focuses on measuring whether models reflect human values and cultural norms, often through benchmarking frameworks like Valuebench[6] and methods that assess semantic alignment or heterogeneous value orientations (Heterogeneous Value Alignment[1]). Reinforcement Learning and Value-Based Optimization explores how to train models using value functions and reward signals, including techniques like step-level Q-value estimation (Step-level Q-value[12]) and direct value optimization (Direct Value Optimization[25]). Adaptive Planning and Decision-Making Agents examines how models can dynamically adjust their reasoning strategies, as seen in works like Adaplanner[3]. Evaluation Frameworks and Benchmarking Methodologies provide systematic ways to measure model capabilities, while branches on Adaptive Model Optimization and Memory Management (e.g., PagedAttention[5]) address efficiency concerns. Adaptive Inference and Realignment Strategies, Domain-Specific Applications, and Specialized Techniques round out the taxonomy, covering context-dependent adjustments and targeted use cases. A particularly active line of work centers on developing fine-grained value evaluation methods that can adapt to different contexts or user populations. AdAEM Value Difference[0] sits squarely within this cluster, proposing adaptive mechanisms to measure value differences across diverse settings. It shares thematic ground with AdAEM Measurement[27], which also emphasizes adaptive evaluation, and with Clave[9], another work in the same branch that explores context-sensitive value assessment. These methods contrast with more static benchmarking approaches like Valuebench[6] or zero-shot evaluation schemes (Zero-shot Benchmarking[11]), which apply uniform criteria across all scenarios. Meanwhile, reinforcement learning branches pursue value estimation for optimization rather than pure assessment, highlighting a trade-off between diagnostic measurement and performance improvement. The original paper's focus on adaptive value difference measurement positions it as a bridge between alignment assessment and dynamic evaluation, addressing the challenge of capturing how model values shift in response to varying inputs or populations.

Claimed Contributions

AdAEM: A self-extensible dynamic value evaluation algorithm

The authors propose AdAEM, an automated framework that dynamically generates and extends test questions to evaluate LLMs' value orientations. Unlike static benchmarks, AdAEM probes value boundaries across diverse LLMs through in-context optimization, enabling it to co-evolve with LLM development and consistently track value dynamics.

9 retrieved papers
Can Refute
Information-theoretic optimization objective for maximizing value differences

The authors formalize an information-theoretic optimization objective that guides the generation of test questions to maximize distinguishability and disentanglement of value orientations across different LLMs. This objective addresses the informativeness challenge by extracting controversial topics that reveal genuine value differences rather than shared safety values.

7 retrieved papers
Can Refute
AdAEM Bench: A novel value evaluation benchmark

The authors construct AdAEM Bench, a benchmark dataset containing 12,310 value-evoking questions generated using their framework. This benchmark is grounded in Schwartz's Theory of Basic Values and demonstrates superior semantic diversity, novelty, and ability to elicit distinguishable value orientations compared to existing static benchmarks.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AdAEM: A self-extensible dynamic value evaluation algorithm

The authors propose AdAEM, an automated framework that dynamically generates and extends test questions to evaluate LLMs' value orientations. Unlike static benchmarks, AdAEM probes value boundaries across diverse LLMs through in-context optimization, enabling it to co-evolve with LLM development and consistently track value dynamics.

Contribution

Information-theoretic optimization objective for maximizing value differences

The authors formalize an information-theoretic optimization objective that guides the generation of test questions to maximize distinguishability and disentanglement of value orientations across different LLMs. This objective addresses the informativeness challenge by extracting controversial topics that reveal genuine value differences rather than shared safety values.

Contribution

AdAEM Bench: A novel value evaluation benchmark

The authors construct AdAEM Bench, a benchmark dataset containing 12,310 value-evoking questions generated using their framework. This benchmark is grounded in Schwartz's Theory of Basic Values and demonstrates superior semantic diversity, novelty, and ability to elicit distinguishable value orientations compared to existing static benchmarks.

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