README.md

metadata

license: cc-by-4.0
language:
  - en
size_categories:
  - n<10K
task_categories:
  - text-classification
  - text-generation
tags:
  - automated-prompt-optimization
  - apo
  - failure-mode-taxonomy
  - llm-evaluation
  - dspy
  - mipro
  - opro
  - gepa
  - reliability-audit
pretty_name: APO Failure-Mode Dataset

APO Failure-Mode Dataset

A labelled dataset of failure modes from Automated Prompt Optimization (APO) methods. Each record is one (run, held-out evaluation trace) tuple from a sweep cell where the optimised prompt underperformed the manual baseline by at least 0.5 standard deviations (the D1b inclusion criterion described below). Records carry one primary failure-mode tag from a locked 5-tag taxonomy plus zero or more secondary tags.

This dataset is the primary deliverable of an ongoing USTH Master's thesis (defence target September 2026) titled Automated Prompt Optimization for Large Language Model Applications Using DSPy and OPRO. The dataset is the empirical substrate for Claim 1 of the thesis (descriptive — when APO underperforms, what kind of failed prompt does it produce?).

Dataset Summary

• Records: 1,695 per-trace labels across 27 included runs from 27 sweep cells.
• Sweep substrate: 32 realised cells (optimiser × dataset × model) at N=5 seeds — the complete Path-B grid, closed out 2026-08-02/03 — across 3 optimisers, 5 datasets, and 3 model families (see Dataset Structure below).
• Annotation pipeline: a locked LLM-as-judge classifier (DeepSeek V4-flash) running the prompt at prompts/failure_mode_judge.txt of the source repository, with a deterministic short-circuit for trivial_restatement cases (similarity ≥ 0.95 to the manual baseline).
• Inclusion criterion: D1b (effect-size). A run is included iff optim_acc < baseline_acc − 0.5 × baseline_std where baseline_std is the std across the N=5 seeds of the matching optimizer="none" cell. Fallback to D1a (point estimate) when the baseline cell has fewer than 3 successful seeds; threshold_fallback flags these records.
• Total judge cost for the labels: $0.98 USD (DeepSeek V4-flash, max_tokens=4096).
• Status: v0.2, public release (2026-08-30). Label validity is measured four ways (see Annotators): test–retest decoding stability κ = 0.925 (passes the pre-registered κ ≥ 0.7 gate), score-blind κ = 0.650, a 79% false-positive anchoring disposition, and the pre-registered seal — a blind human annotation of the frozen 50-trace audit form, measured 2026-08-02 at author–judge κ = 0.925 (47/50 agreement; author-rater under a blind protocol, judge labels withheld). The human audit that gated public release is cleared. See Considerations.

Supported Tasks

• Failure-mode classification. Given an (optimised prompt, baseline prompt, held-out trace, question, gold) tuple, predict the primary failure-mode tag from {trivial_restatement, instruction_collapse, exemplar_leak, over_specification, format_break}.
• APO reliability auditing. Each cell's underperformance pattern is a within-cell descriptive statistic; the dataset can be used to compare optimiser families' failure-mode profiles (e.g.\ MIPROv2's instruction_collapse + format_break vs.\ GEPA's trivial_restatement + instruction_collapse + exemplar_leak vs.\ OPRO's 100% trivial_restatement).

Languages

English (en) only. All prompts, traces, and rationales are English; the underlying APO benchmarks (GSM8K, HotpotQA, SST-5, AG News, HumanEval) are English-only.

Dataset Structure

Data Fields

field	type	description
run_id	string	Cell run identifier (cell_<optimizer>_<task>_<model>_seed<N>_<unix_ts>).
cell_id	string	Cell identifier without timestamp (groups the 5 seeds of one cell).
trace_index	int32	Position of this trace within the run's held-out evaluation slice.
task_name	string	One of gsm8k, hotpotqa, sst5, ag_news, humaneval.
target_model	string	The held-out evaluator model id (e.g.\ deepseek-v4-flash).
actual_model	string	The provider-returned model id per the X.PIN.1 audit trail.
optimizer	string	One of dspy_mipro, opro, gepa. (Baseline none cells are not in the dataset.)
seed	int32	Seed from {42, 43, 44, 45, 46}.
baseline_prompt_file	string	Path of the frozen baseline file (prompts/wei2022_gsm8k.txt for GSM8K; prompts/<task>_baseline.txt for the others, per X.BASELINE.1).
optimized_prompt	string	The optimised prompt template the held-out evaluation ran with.
trace	string	The model's raw completion on this held-out example.
completion_tokens	int32	Token count of the completion (per the provider's tokenizer).
is_correct	bool	Did the trace score correct on this example under the task's primary metric?
inclusion_threshold	string	Either d1b (effect-size) or d1a (fallback).
threshold_fallback	string	d1a iff the baseline cell had < 3 seeds; null otherwise.
baseline_acc	float64	Mean accuracy across the matching none cell's N=5 seeds.
baseline_std	float64	Std across the matching none cell's N=5 seeds.
optimized_acc	float64	This run's accuracy on the held-out slice.
gap_std	float64	(baseline_acc − optimized_acc) / baseline_std.
primary_tag	string	The primary failure-mode tag. One of {trivial_restatement, instruction_collapse, exemplar_leak, over_specification, format_break} or null.
all_tags	list	Multi-label tag set (subsumes primary_tag).
rationale	string	The judge's natural-language explanation.
confidence	float64	Judge's reported confidence in primary_tag ∈ [0, 1].
source	string	deterministic (similarity short-circuit), llm_judge (real LLM call), judge_parse_failure (empty / malformed JSON), or excluded_dspy_serialization_bug (substrate-level exclusion).
judge_actual_model	string	Provider-returned judge model id, null for deterministic / excluded paths.
judge_cost_usd	float64	Per-record API cost in USD.
manual_label_subset	string	Null in the parquet. The blind human audit's 50 labels (§ Annotators) are versioned in the source repository as a keyed sidecar (outputs/audits/2026-07-28/labels.csv, keyed by audit-form row id with the mapping in form.md).

Data Splits

This is a single-split dataset (no train/val/test partition); records are organised by (cell, seed) provenance rather than by inference split. The dataset is intended as labelled evaluation material for downstream APO-reliability research, not for training.

Per-source breakdown

source	count	%
llm_judge	1004	59.2%
deterministic	691	40.8%

Per-primary-tag breakdown

primary_tag	count	%
trivial_restatement	708	41.8%
format_break	376	22.2%
over_specification	308	18.2%
exemplar_leak	253	14.9%
instruction_collapse	49	2.9%
(none)	1	0.1%

Per-task breakdown

task	count
sst5	660
hotpotqa	484
ag_news	434
gsm8k	117

Per-optimiser breakdown

optimiser	count
gepa	1652
opro	22
dspy_mipro	21

Per-model breakdown

model	count
Qwen/Qwen2.5-7B-Instruct-Turbo	847
claude-haiku-4-5-20251001	610
deepseek-v4-flash	238

Dataset Creation

Curation Rationale

Two concurrent works frame the gap this dataset fills:

• Coin Flip (Zhang et al., 2026; arXiv:2604.14585) reports that 49% of APO runs on Claude Haiku 4.5 underperform zero-shot, but releases only ANOVA results, not labelled failure traces.
• ETGPO (Singh, Yadav & Blanco, 2026; arXiv:2602.00997) uses an error taxonomy to drive APO optimisation but does not release a labelled dataset of optimiser-side failures.

The labelled-dataset deliverable in this dataset is, to our knowledge, the first published collection of multi-method × multi-task × multi-model APO failures with a fixed taxonomy and per-record provenance metadata.

Source Data

The held-out evaluation traces are model completions on standard NLP benchmarks (GSM8K, HotpotQA, SST-5, AG News, HumanEval), produced by three APO methods (DSPy MIPROv2, OPRO, GEPA) and one baseline (none — the manual baseline prompt). Each trace's underlying dataset is publicly available under its original license:

• GSM8K: MIT License (Cobbe et al., 2021). Source: https://huggingface.co/datasets/gsm8k.
• HotpotQA: CC BY-SA 4.0 (Yang et al., 2018). Source: https://huggingface.co/datasets/hotpot_qa.
• SST-5: derived from the Stanford Sentiment Treebank (Socher et al., 2013) via the SetFit redistribution. Source: https://huggingface.co/datasets/SetFit/sst5.
• AG News: CC BY-SA 3.0 / public domain headlines. Source: https://huggingface.co/datasets/ag_news.
• HumanEval: MIT License (Chen et al., 2021). Source: https://huggingface.co/datasets/openai_humaneval.

Only the model's completions and the gold/predicted scoring are reproduced in this dataset; the underlying questions / inputs are referenced by (task_name, seed, trace_index) so the upstream datasets can be reloaded deterministically.

Annotations

The annotation pipeline is a hybrid deterministic + LLM-as-judge classifier:

1. Deterministic short-circuit (source == "deterministic"). If the optimised prompt has cosine similarity ≥ 0.95 (whitespace-normalised) or is byte-identical to the manual baseline, the record is tagged trivial_restatement without an LLM call. This accounts for ~30% of dataset records.
2. LLM-as-judge (source == "llm_judge"). The remaining records are scored by DeepSeek V4-flash running the locked judge prompt at prompts/failure_mode_judge.txt (max_tokens=4096 to accommodate the model's hidden reasoning channel, see Limitations). The judge returns a JSON object with tags, primary, rationale, confidence.
3. Parse-failure fallback (source == "judge_parse_failure"). Records where the judge returned malformed JSON or empty content are tagged with a null primary_tag and a rationale preserving the raw response for human follow-up.
4. Substrate exclusion (source == "excluded_dspy_serialization_bug"). Records affected by the X.DSPY-SERIAL.1 substrate bug were excluded from the dataset pre-v0.1; the bug was fixed on 2026-06-02 and the affected cells re-classified, so v0.1 has 0 records in this bucket.

Annotation Procedure

• Annotator: DeepSeek V4-flash (versioned id deepseek-v4-flash, pinned per the X.PIN.1 audit). judge_actual_model records the provider-returned id per record.
• Prompt: frozen at prompts/failure_mode_judge.txt once the production sweep started on 2026-06-02. Editing the prompt invalidates the inter-rater κ measurement per AGENTS.md §2 rule 6.
• Taxonomy: 5 base tags (trivial_restatement, instruction_collapse, exemplar_leak, over_specification, format_break) — locked. Up to 2 reserved slots may be populated from an open-coding pass (P4.OPENCODE.1) before sweep lock; v0.1 ships with the 5 base tags only.

Who are the annotators?

The primary annotator is the DeepSeek V4-flash model running the locked judge prompt. Label reliability was measured four ways, in increasing order of independence:

1. Test–retest decoding stability (locked fallback): the judge re-labelled a 50-trace stratified subsample of its own judge-assigned traces (10 per primary tag; deterministic trivial_restatement labels excluded as reproducible by construction). κ = 0.925 (observed 0.940, chance 0.200; PABAK 0.88) — passes the κ ≥ 0.7 gate. This is self-agreement at temperature 0, not inter-rater agreement.
2. Score-blind re-judge: masking the accuracy gap and the underperformance premise drops blind-vs-dataset agreement to κ = 0.650 — the judge's labels partly depend on seeing the scores.
3. Anchoring false-positive arm: shown traces from runs that did not underperform, the judge still assigned a failure mode 79% of the time — an upper bound on its premise-following disposition.
4. Blind human audit (the pre-registered seal): author–judge κ = 0.925 (measured 2026-08-02; observed 0.940, chance 0.200; PABAK 0.88) — passes the κ ≥ 0.7 gate. A human rater labelled the frozen 50-trace stratified form blind (judge labels withheld until all 50 labels were committed). Per-tag agreement: exemplar_leak 10/10, format_break 10/10, instruction_collapse 9/10, over_specification 9/10, trivial_restatement 9/10; all three disagreements lie within the interpretive trio (instruction_collapse ↔ over_specification/trivial_restatement). Two caveats: the rater is the source-thesis author (blind to labels, but not independent of the rubric — a second, independent rater would be the strongest version), and the audited sample is drawn from the two-family (DeepSeek + Qwen) dataset build, so traces contributed by the Haiku family after the audit carry judge labels only.

Net verdict: the judge is internally consistent, the deterministic backbone plus the content-visible categories survive score-blinding, and a blind human rater concurs at κ = 0.925 — so the labels carry measured author–judge agreement, one step short of independent inter-rater confirmation. instruction_collapse remains the most score-anchored category (both raters saw the underperformance premise).

Personal and Sensitive Information

None. The underlying benchmarks are pre-existing public NLP datasets; model completions do not contain personal information. Prompt content is purely task instructions plus optimisation artefacts.

Considerations for Using the Data

Social Impact

The dataset may be useful for researchers studying:

• when automated prompt optimisation fails and why (Claim 1 / RQ1 of the source thesis),
• whether published APO reliability benchmarks are reproducible across optimiser families (the cross-task profiles in this dataset answer this for the 32-cell substrate),
• whether human evaluators agree with LLM-as-judge classification of optimisation failures (judge-internal bounds plus a blind human audit at κ = 0.925 are reported — see Annotators).

It is not suitable for training stronger APO methods directly — the dataset is small (≈1k records), single-language, and was not constructed for generative training.

Discussion of Biases

The dataset reflects the substrate's biases:

• Task coverage is limited to 5 well-studied APO benchmarks. There is no evidence that the failure-mode profile generalises to alignment, dialogue, or multilingual tasks.
• Model coverage spans 3 model families (DeepSeek V4-flash, Claude Haiku 4.5, Qwen-2.5-7B-Instruct-Turbo); the Haiku tier was completed 2026-08-02/03 and carries GEPA runs only (see Optimiser coverage below). Haiku traces postdate the blind human audit and carry judge labels only.
• Optimiser coverage is asymmetric: GEPA runs at full 5 × 3 grid; DSPy MIPROv2 and OPRO run only on gsm8k × DeepSeek V4-flash (Path B narrowing per P4.MULTITASK.1 partial port). The full grid is a thesis-development item.
• Judge bias: DeepSeek V4-flash labels the dataset. It has not been independently audited for systematic bias toward particular taxonomy tags. The M2 concurrent-validity pilot (planned) will quantify this.

Other Known Limitations

• Human audit is author-rater, not independent: the blind human audit (author–judge κ = 0.925, § Annotators) was annotated by the source-thesis author under a blind protocol. Blinding removes label leakage but not shared training on the rubric, so this is author–judge agreement under blinding rather than independent inter-rater reliability; the score-anchored instruction_collapse should still be read with the anchoring caveat in mind.
• DeepSeek V4-flash hidden reasoning (X.PIN.2): V4-flash defaults to thinking-mode-on at the API layer, costing ~8pp accuracy on a GSM8K seed-42 probe. This dataset was produced from the reasoning-disabled substrate: all DeepSeek V4-flash cells were re-run with extra_body={"thinking": {"type": "disabled"}} (the D-NEW-2 re-run), and this rebuild deduplicates to those thinking-off runs. Earlier thinking-on aggregates are superseded.
• Open-coding pass (P4.OPENCODE.1): the 2 reserved taxonomy slots have not been populated. v0.1 ships with the 5 base tags only.
• Coin Flip replication cell (P4.REPLICATE.1): not included in v0.1. It will appear in v0.2 as a separate replication_cell subset.

Comparison to Concurrent Corpora

dataset	unit of analysis	size	release
APO Failure-Mode Dataset (this dataset)	optimiser-output failure (prompt + trace)	~1,695	HF Hub
ETGPO (Singh et al., 2026; arXiv:2602.00997)	task-error (LLM output failure)	not released as dataset	(paper only)
Tian et al. (arXiv:2509.14404)	manually-written prompt defect	not released	(paper only)
MemAPO (arXiv:2603.21520)	optimiser-internal memory artefact	not released	(paper only)
Coin Flip (Zhang et al., 2026; arXiv:2604.14585)	ANOVA variance decomposition	aggregate only	(paper only)

This dataset is the first publicly-released dataset that is keyed on (optimiser-output, failure-mode label) tuples with per-record provenance.

Reproducibility

The dataset is fully reproducible from the source repository (private during thesis development; public release with thesis defence):

1. Run the 32-cell sweep via scripts/run_sweep.py --config configs/sweep_main.yaml. Cost: ≈ $16 USD across all cells (the Anthropic tier dominates).
2. Classify the failure modes via scripts/classify_failures.py --all --concurrency 4 --judge-max-tokens 4096. Cost: ≈ $2 USD.
3. Rebuild this card via scripts/build_dataset_card.py.

Every record's parent run carries a run_metadata.json with git SHA, resolved cell config, provider-returned actual_model, and per-call cost. The substrate is git-pinned to deepseek-v4-flash (versioned id) per the X.PIN.1 audit and frozen baselines per X.BASELINE.1 (prompts/<task>_baseline.txt for each task).

Citation

TODO (defence September 2026): replace this placeholder with the thesis citation + an arXiv companion link.

@misc{apo_failure_modes_2026,
  title         = {APO Failure-Mode Dataset: A Labelled Dataset of Automated Prompt Optimization Failures},
  author        = {Dang Vu Son Tung},
  year          = {2026},
  note          = {USTH Master's thesis, v0.2},
  howpublished  = {Hugging Face Hub, \url{https://huggingface.co/datasets/tungdvs/apo-failure-modes}},
}

License

This dataset is released under CC BY 4.0. The underlying benchmark datasets remain under their original licenses (see Source Data); each record's task field identifies the upstream license that applies to that record's underlying input.