TALES-Tax: Evaluating Cultural Misrepresentations

• TALES-Tax is a systematic evaluation taxonomy that defines and categorizes cultural misrepresentations in LLM-generated narratives through community-grounded criteria.
• It utilizes mixed methods including focus groups and surveys to quantitatively benchmark narrative errors across 540 stories from diverse Indian regions.
• The framework is designed for model auditing and quality control, identifying errors such as linguistic inaccuracies, clichés, and factual mistakes to guide improvements.

TALES-Tax is a systematically developed evaluation taxonomy for categorizing and analyzing cultural misrepresentations in LLM-generated narratives, particularly focusing on Indian cultural identities and their representation in open-ended text generation tasks. Originating from participatory research with community experts, TALES-Tax provides a granular, multi-criteria framework to quantify, benchmark, and interpret failures in cultural fidelity and nuance in model-generated stories (Bhagat et al., 26 Nov 2025). It supports both qualitative insight and large-scale, statistically grounded annotation campaigns, serving as a foundation for model auditing, benchmarking, and tool-building.

1. Motivations, Goals, and Elicitation of TALES-Tax

TALES-Tax was designed to address the lack of rigorous, culturally situated evaluation tools for LLM narrative outputs, recognizing the complexity of Indian cultural contexts in terms of language, geography, ritual, and social hierarchy. The primary objectives were threefold:

• Establish a systematic, community-grounded framework to identify and classify modes of cultural misrepresentation in model outputs,
• Supplement the “correctness” paradigm in text evaluation with a multi-dimensional approach rooted in the lived experiences of affected communities,
• Enable both insight into specific narrative errors (qualitative) and quantitative measurement of occurrence and distribution across models, languages, and scenarios (Bhagat et al., 26 Nov 2025).

Construction of TALES-Tax proceeded via a two-tiered elicitation:

• Focus groups (N=9) covering diverse Indian regions,
• Individual surveys (N=15) with regionally and linguistically representative participants. Respondents annotated LLM-generated stories for culturally problematic spans, supplemented by rationales. Open coding and thematic synthesis allowed researchers to distill recurring patterns into distinct conceptual categories, with refinement continuing until thematic saturation.

2. Structure and Category Definitions

The taxonomy comprises seven non-exclusive categories, each precisely delineated and, when appropriate, subdivided for scope clarity. The set of misrepresentation categories is

$C = \{c_1, c_2, \ldots, c_7\}$

with formal definitions as follows:

Code	Category	Definition (summary)
c₁	Cultural Inaccuracy	Contradiction of community norms in depicting objects/rituals
c₂	Unlikely Scenarios	Contextually implausible but logically possible events
c₃	Clichés	Reliance on stereotypes or hackneyed portrayals
c₄	Oversimplification	Erosion of regional specificity; use of generic pan-Indian labels
c₅	Factual Error	Verifiable mistakes in geography, history, or socioeconomic fact
c₆	Linguistic Inaccuracy	Errors in spelling, grammar, or culturally specific language use
c₇	Logical Error	Narrative or cultural procedural inconsistencies

For each, TALES-Tax supplies operationalized criteria and common sub-types. For example:

• c₁: Object misplacement (e.g., misassigned jewelry), ritual misattribution (wrong religion/region);
• c₂: Misplaced practices, exaggerated drama;
• c₄: Use of umbrella terms (“rangoli” in place of region-specific “alpana”).

Representative annotated examples clarify boundaries between adjacent categories and demonstrate application breadth (Bhagat et al., 26 Nov 2025).

3. Annotation Campaigns and Quantitative Findings

Using TALES-Tax, a multi-annotator, large-scale paper comprised 2,925 annotations on 540 LLM-generated stories. Annotators possessed lived cultural experience across 71 Indian regions and 14 languages. Main findings include:

• 88% of stories contained at least one misrepresentation, with a mean of 5.42 errors per story (approximately one per five sentences),
• Linguistic inaccuracies (c₆) were most frequent, followed by factual errors (c₅), cultural inaccuracies (c₁), and logical errors (c₇).

Category	Mean Errors per Story
Cultural Inaccuracy	0.8
Unlikely Scenarios	0.7
Clichés	0.6
Oversimplification	0.5
Factual Error	0.9
Linguistic Inaccuracy	1.2
Logical Error	0.8

Prevalence of errors escalated in mid- and low-resource languages (median +2 to +4.5 errors over English, p<.001), and stories set in tier-3 (non-metropolitan) regions showed a substantive increase in cultural inaccuracies and factual errors compared to metropolitan (tier-1) settings. The relatability of stories, scored on a 1–5 scale, correlated negatively with frequency of misrepresentations ($r_s \approx -0.30$, $p<.01$).

4. Methodological and Theoretical Significance

TALES-Tax operationalizes community reflexivity in LLM evaluation, advancing beyond accuracy-oriented or single-dimension error taxonomies. The use of focus group and regionally stratified survey design ensures misrepresentation categories are not simply formal abstractions or externally imposed, but instead reflect the hierarchies and sensitivities salient to local audiences. The distinction among categories such as “Cultural Inaccuracy”, “Clichés”, and “Oversimplification” allows for nuanced analysis of both overt and subtle failures in modeling socio-cultural context. This suggests the value of TALES-Tax in identifying not only misinformation but also representational flattening and stereotype perpetuation.

5. Applications and Extensions

TALES-Tax is positioned as both an analytical lens and a practical annotation schema. Major applications include:

• Model Auditing: Systematic error logging and evaluation across new or updated LLMs, with involvement of cultural experts for interpretive depth,
• Automated Quality Control: Training classifiers or incorporating RLHF signals to penalize taxonomized misrepresentations during model fine-tuning,
• Benchmarking: Longitudinal tracking of LLM progress in cultural competence by aggregating taxonomy-conditioned error counts over time,
• Human-in-the-loop Aids: Integration into writing tools or educational platforms to prompt review of culturally salient details pre-publication.

Cross-cultural adaptation is explicit: TALES-Tax serves as a template, with category boundaries and examples modifiable for other cultural-linguistic ecosystems. Annotations can be transformed into task banks (e.g., TALES-QA) for probing LLMs’ encoded cultural knowledge as distinct from their generative performance (Bhagat et al., 26 Nov 2025).

6. Limitations and Interpreted Findings

While TALES-Tax enables detailed and statistically robust audits of LLM-generated text, its region- and group-specific categories may require modification for deployment outside the annotated population. A plausible implication is that culturally grounded error taxonomies must be continually updated as community standards, linguistic usage, and practices evolve. The finding that LLMs often “possess the requisite cultural knowledge despite generating stories rife with cultural misrepresentations” underscores the disconnect between knowledge representation and narrative composition tasks in LLMs, and motivates model-level or training-architecture interventions targeting representational alignment.

7. Broader Context and Related Work

TALES-Tax addresses gaps left by prior taxonomies focused on narrow semantic or grammatical errors, offering a tool for high-dimensional, context-specific model evaluations. Its participatory construction aligns with contemporary moves towards reflexive, user-centered AI evaluation methodologies. The TALES-Tax framework contributes an auditable, portable reference model to support researchers and practitioners in systematically measuring, comparing, and ultimately improving the cultural fidelity of LLMs (Bhagat et al., 26 Nov 2025).