Papers with value systems
A Game-Theoretica Negotiation Framework for Cross-Cultural Consensus (2026.acl-long)
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| Challenge: | Large language models exhibit pronounced WEIRD cultural bias, marginalizing diverse viewpoints and posing challenges for reconciling diverse populations with varying cultural backgrounds and value systems. |
| Approach: | They propose a framework for cross-cultural fairness using a Nash Equilibrium . they propose equilibriums that iteratively propose and refine natural-language guidelines . |
| Outcome: | The proposed framework generates higher-quality and more balanced consensus . it finetunes diverse LLM architectures with negotiation data, reducing cultural distances by 95.53%. |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie
| Challenge: | Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores. |
| Approach: | They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs. |
| Outcome: | The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values. |
Value Alignment from Unstructured Text (2024.emnlp-industry)
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Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush Varshney
| Challenge: | Currently, alignment of large language models to value systems relies on the availability of supervised and preference data. |
| Approach: | They propose a systematic approach for aligning large language models to values in unstructured text data using synthetic data generation techniques. |
| Outcome: | The proposed approach shows improved performance on the Mistral-7B-Instruct model compared to other approaches, as quantified through the use of automatic metrics and win rates. |
The Relevance of Value Systems for Offensive Language Detection (2026.eacl-long)
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| Challenge: | Recent research in perspectivism has departed from the assumption that offensiveness can be defined through a universal perspective. |
| Approach: | They propose to use a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems to identify offensiveness patterns. |
| Outcome: | The proposed dataset consists of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. |
Evaluating the Prompt Steerability of Large Language Models (2025.naacl-long)
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Erik Miehling, Michael Desmond, Karthikeyan Natesan Ramamurthy, Elizabeth M. Daly, Kush R. Varshney, Eitan Farchi, Pierre Dognin, Jesus Rios, Djallel Bouneffouf, Miao Liu, Prasanna Sattigeri
| Challenge: | a primary question underlying alignment research is: whose views are we aligning to? |
| Approach: | They propose to evaluate the steerability of model personas as a function of prompting by defining a benchmark and inspecting how these indices change as if steering effort is a factor. |
| Outcome: | The proposed benchmark reveals that the steerability of many current models is limited due to skew in baseline behavior and an asymmetry in their steerability across many persona dimensions. |
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)
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| Challenge: | Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good. |
| Approach: | They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity. |
| Outcome: | The proposed system could change people's opinions and actions for social good. |
Generative Psycho-Lexical Approach for Constructing Value Systems in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have raised concerns regarding their intrinsic values. |
| Approach: | They propose a psychologically grounded five-factor value system for Large Language Models that integrates psychological principles with cutting-edge AI priorities. |
| Outcome: | The proposed value system meets standard psychological criteria, improves LLM safety prediction, and enhances Llm alignment, when compared to the canonical Schwartz’s values. |
ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)
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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong
| Challenge: | Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems. |
| Approach: | They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation. |
| Outcome: | Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods. |
Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow and misaligned with human reasoning. |
| Approach: | They propose a value-grounded framework for evaluating and distilling structured moral reasoning in large language models. |
| Outcome: | The proposed framework evaluates 12 open-source models across four moral datasets. |