Value Quotient: Evaluating LLMs' Societal Impact

• VQ is a multidimensional framework that quantifies the real-world utility, risks, and broader impacts of LLMs across economic, social, ethical, and environmental axes.
• It aggregates detailed sub-criteria such as cost–benefit ratios, user satisfaction, fairness, and ecological footprints to compute normalized scores.
• The composite VQ score aids stakeholders in highlighting tradeoffs, guiding improvements, and ensuring LLM deployments are socially and ethically beneficial.

Value Quotient (VQ) is a multidimensional framework devised to quantify the real-world utility, risks, and broader impacts of LLMs across economic, social, ethical, and environmental axes. VQ stands alongside Intelligence Quotient (IQ), Professional Quotient (PQ), and Emotional Quotient (EQ) in a four-pillar taxonomy for LLM evaluation, addressing vital dimensions absent from traditional benchmark-driven assessments. By systematically aggregating evidence from cost–benefit metrics, welfare improvements, normative alignment, and ecological considerations, VQ reframes the evaluative question from mere technical feasibility to societal worth and acceptability (Wang et al., 26 Aug 2025).

1. Position in the Evaluation Taxonomy

VQ operationalizes the inquiry: “What real-world value does this model deliver?” in direct complement to:

• IQ (“foundational capacity”): assessing general reasoning and world knowledge,
• PQ (“professional expertise”): domain- and task-specific skill,
• EQ (“alignment ability”): human-value alignment and preference matching.

While IQ, PQ, and EQ interrogate model prowess, skill, and human compatibility, VQ evaluates whether LLM deployment confers net benefit, is ethically justified, and minimizes negative externalities. This shift extends the standard evaluation paradigm by capturing factors such as economic viability, social uplift, ethical soundness, and environmental sustainability, which are underrepresented by technical performance measures alone (Wang et al., 26 Aug 2025).

2. Core Structure and Dimensionalization

VQ decomposes into four dimensions, each scored on the normalized interval $[0,1]$:

Dimension	Abbreviation	Focus
Economic Viability	$S_{\rm Econ}$	Cost-benefit and productivity
Social Impact	$S_{\rm Soc}$	Welfare, user satisfaction, public good
Ethical Alignment	$S_{\rm Eth}$	Fairness, transparency, privacy, bias
Environmental Sustainability	$S_{\rm Env}$	Energy, carbon footprint, life-cycle effects

Each dimension is further subdivided into quantitative or ordinal sub-criteria, which are aggregated into a single dimension score using simple means and normalization procedures. This structure enables transparent decomposition of overall value, identifies tradeoffs, and supports stakeholders in prioritizing what dimensions matter most to them.

3. Mathematical Formulation

3.1 Economic Viability ($S_{\rm Econ}$)

This dimension quantifies cost-effectiveness and practical adoption potential, aggregating:

• Cost–Benefit Ratio (CBR): $\frac{\text{Annual Benefit}}{\text{Annual Cost}}$
• Return on Investment (ROI): $$\frac{\text{Gain} - \text{Investment}}{\text{Investment}}$$
• Productivity Improvement (PI): Percent reduction in manual effort or time
• Market Acceptance (MA): Adoption rate or customer satisfaction index

The dimension score is computed as

$$S_{\rm Econ} = \frac{1}{4}\Big( \mathrm{norm}(\mathrm{CBR}) + \mathrm{norm}(\mathrm{ROI}) + \mathrm{norm}(\mathrm{PI}) + \mathrm{norm}(\mathrm{MA}) \Big)$$

where $\mathrm{norm}(m) = \min\{m / m_{\max}, 1\}$.

3.2 Social Impact ($S_{\rm Soc}$)

This encompasses welfare gains unaccounted for by market dynamics:

• User Satisfaction (US): Survey-based mean ($0$–$1$)
• Knowledge Dissemination Efficiency (KDE): Increase in information reach
• Public Service Improvement (PSI): Expert panel assessment
• Education Quality Improvement (EQI): Measured learning outcomes

Aggregated as

$$S_{\rm Soc} = \frac{1}{4}\bigl(\mathrm{US}+\mathrm{KDE}+\mathrm{PSI}+\mathrm{EQI}\bigr)$$

3.3 Ethical Alignment ($S_{\rm Eth}$)

Evaluates regulatory, normative, and fairness criteria:

• Fairness (F): Statistical parity (e.g., difference in positive rates)
• Transparency (T): Comprehension score of explanations
• Privacy Protection (PP): Pass rate on privacy audits
• Bias Detection (BD): Inverse demographic bias rate

Dimension score: $$S_{\rm Eth} = \frac{1}{4}\bigl(\mathrm{norm}(1 - \mathrm{parity\;gap}) + \mathrm{T} + \mathrm{PP} + (1 - \mathrm{BiasRate})\bigr)$$

3.4 Environmental Sustainability ($S_{\rm Env}$)

Captures net ecological impact:

• Energy Efficiency (EE): Tokens per kWh (normalized)
• Carbon Footprint (CF): CO$_2$e per query (inverted and normalized)
• Sustainability (S): Life-cycle assessment score

Aggregated by

$$S_{\rm Env} = \frac{1}{3}\bigl(\mathrm{norm}(\mathrm{EE}) + [1 - \mathrm{norm}(\mathrm{CF})] + \mathrm{S}\bigr)$$

The composite VQ is a weighted sum of the four dimension scores: $$\mathrm{VQ} = w_{\rm Econ}\,S_{\rm Econ} + w_{\rm Soc}\,S_{\rm Soc} + w_{\rm Eth}\,S_{\rm Eth} + w_{\rm Env}\,S_{\rm Env}$$ with stakeholder- or context-dependent weights ($w_i$), or defaulting to uniform weighting ($w_i = 0.25$).

4. Application Example

In a hypothetical customer-service LLM deployment, the framework produces the following normalized sub-scores and aggregates:

Dimension	Sub-scores (CBR, ROI, etc.)	Aggregated Score
Econ	0.80, 0.75, 0.60, 0.85	0.75
Soc	0.72, 0.68, 0.75, 0.65	0.70
Eth	0.80, 0.85, 0.70, 0.77	0.78
Env	0.50, 0.40, 0.55	0.55

Aggregating with uniform weights: $$\mathrm{VQ} = 0.25(0.75 + 0.70 + 0.78 + 0.55) \approx 0.70$$

An overall VQ near 0.70 signifies strongly positive economic and ethical results, substantial social benefit, but a moderate environmental outcome, indicating domains for targeted improvement (Wang et al., 26 Aug 2025).

5. Implementation Considerations

VQ’s modularity permits adaption to diverse deployment contexts. Scoring depends on the quality and objectivity of data acquisition, normalization baselines ($m_{\max}$), and sub-criteria weighting. Some sub-criteria—particularly those involving social welfare or public service—necessitate expert assessment or stakeholder surveys. Periodic reassessment is advised to accommodate temporal variability in operational costs, adoption rates, and emergent societal norms. The authors maintain a curated open repository (“Awesome-LLM-Eval”) to standardize benchmarking, measurement protocols, and support cross-model comparison.

6. Challenges and Outlook

The principal methodological challenges for VQ include:

• Data measurement: Certain sub-criteria (e.g., public-service impact) may lack automated instrumentation, relying on periodic or ad hoc expert feedback.
• Normalization: Selecting $m_{\max}$ for each metric is subjective; inter-model comparisons require calibration of normalization constants across evaluations.
• Weight selection: The relevance of each VQ dimension can be stakeholder-specific, necessitating elicitation of explicit preference distributions.
• Temporal tracking: VQ should be maintained as a dynamic, longitudinal score accommodating evolving deployments and societal expectations.
• Interpretability: High-level VQ scores may obscure latent weaknesses; dimension-level dashboards and breakdowns are essential for diagnosis and actionable insights.

A plausible implication is that community-wide adoption of open VQ benchmarks and continuous, transparent reporting will enable robust, comparative, and trustworthy assessment of LLM deployments, guiding development towards solutions that optimize not only technical proficiency but also responsible, beneficial, and sustainable outcomes (Wang et al., 26 Aug 2025).