ECLIPTICA-30k

ECLIPTICA holds the user prompt fixed and varies only the alignment instruction, so any behavioral difference between two rows with the same prompt_id is attributable to the instruction. ECLIPTICA-30k crosses the same 300 prompts as ECLIPTICA-3k with 100 instruction types (10 axes x 10 settings) = 30,000 cases.

What has been evaluated. All experiments in the companion paper use the original 10 types, i.e. the eval_3k config (300 x 10 = 3,000 cases, byte-identical to ECLIPTICA-3k). The 90 additional types are released to widen the instruction space for further research; they are hand-designed as a taxonomy (no judge models), they have not been scored by any model yet, and they carry no evaluation claims.

Configs

config	rows	content
full (default)	30,000	300 prompts x 100 types; adds axis, axis_kind, origin columns
eval_3k	3,000	the evaluated subset, identical to ECLIPTICA-3k (6 columns)

Axes and settings

kind = policy axes change what the model commits to (stance, boundary, epistemics, role, region); kind = surface axes change how it says it (style, length, audience, format). Each type has three imperative phrasings; one is sampled per row with a fixed seed.

axis	kind	settings	types
stance	policy	10	neutral, conservative, liberal, utilitarian, deontological, libertarian, communitarian, precautionary, technocratic, egalitarian
constraint_posture	policy	10	regulatory_aware, safety_first, privacy_first, harm_reduction, child_safe, legal_conservative, permissive_informational, medical_caution, financial_caution, zero_speculation
delivery_style	surface	10	empathetic, educational, concise, professional, creative, casual, humorous, socratic, narrative, diplomatic
epistemic_stance	policy	10	hedged_uncertainty, confident_assertion, evidence_cited, consensus_only, skeptical, speculative_labeled, quantitative, probabilistic_calibrated, devils_advocate, first_principles
verbosity_structure	surface	10	one_sentence, three_bullets, numbered_steps, executive_summary, exhaustive, tldr_first, faq, outline_headings, comparison_table, hundred_words
audience	surface	10	young_child, teenager, undergraduate, domain_expert, policymaker, executive, older_adult_nontechnical, plain_english_esl, journalist, software_developer
persona_role	policy	10	support_agent, legal_advisor, clinician, teacher, financial_advisor, compliance_officer, research_scientist, product_manager, community_moderator, counselor
action_orientation	policy	10	recommend_one, options_only, risk_assessment, clarify_first, step_by_step_plan, tradeoff_matrix, stakeholder_map, historical_context, future_scenarios, counterargument_first
regional_lens	policy	10	global_south, india, european_union, united_states, east_asia, rural, urban, small_business, public_sector, nonprofit
format_constraints	surface	10	markdown_headings, json_output, plain_text, inline_citations, glossary, worked_examples, counterexamples, closing_summary, definition_first, quantitative_estimates

The full taxonomy with descriptions, phrasings and expected characteristics is in taxonomy.yaml.

Fields

• prompt_id: identifier shared by the 100 rows of one prompt
• prompt: the user query (held fixed across instruction types)
• instruction_type: one of the 100 types above
• instruction: the alignment-instruction text used for this row
• expected_characteristics: behavioral traits the response should show under this instruction
• source: topical category of the prompt (12 categories x 25 prompts)
• axis, axis_kind, origin (full config only): taxonomy axis, policy/surface, and whether the type belongs to the evaluated 10

Usage

from datasets import load_dataset
full = load_dataset("anonymousML123/ECLIPTICA-30k", "full", split="train")       # 30,000 rows
eval_3k = load_dataset("anonymousML123/ECLIPTICA-30k", "eval_3k", split="train") # the evaluated 3,000 rows

Citation

@misc{wanaskar2026ecliptica,
  title         = {ECLIPTICA: A Framework for Switchable LLM Alignment via SwiPO, Switchable Preference Optimization},
  author        = {Wanaskar, Kapil and Jena, Gaytri and Jain, Vinija and Chadha, Aman and Das, Amitava},
  year          = {2026},
  eprint        = {2601.06157},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2601.06157}
}

License

CC-BY-4.0.