Pith. sign in

REVIEW 2 cited by

Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Values

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.07071 v3 pith:CFPH6RZJ submitted 2025-01-13 cs.AI

classification cs.AI
keywords llmsvaluesvalueevaluationbenchmarksevaluationsalignmentbecome
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify the underlying values of LLMs precisely and comprehensively, while current evaluations focus narrowly on safety risks such as bias and toxicity. (2) Evaluation Validity: Existing static, open-source benchmarks are prone to data contamination and quickly become obsolete as LLMs evolve. Additionally, these discriminative evaluations uncover LLMs' knowledge about values, rather than valid assessments of LLMs' behavioral conformity to values. (3) Value Pluralism: The pluralistic nature of human values across individuals and cultures is largely ignored in measuring LLMs value alignment. To address these challenges, we presents the Value Compass Benchmarks, with three correspondingly designed modules. It (i) grounds the evaluation on motivationally distinct \textit{basic values to clarify LLMs' underlying values from a holistic view; (ii) applies a \textit{generative evolving evaluation framework with adaptive test items for evolving LLMs and direct value recognition from behaviors in realistic scenarios; (iii) propose a metric that quantifies LLMs alignment with a specific value as a weighted sum over multiple dimensions, with weights determined by pluralistic values.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey proposes a macro-meso-micro value framework for agentic AI alignment and maps applications, methods, and benchmarks onto it.

  2. Domain Specific Benchmarks for Evaluating Multimodal Large Language Models

    cs.LG 2025-06 conditional novelty 3.0 of 10

    A review paper that organizes domain-specific MLLM benchmarks into an eight-discipline taxonomy, with summary tables and performance highlights.

Pith tools