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242 lines
12 KiB
Markdown
242 lines
12 KiB
Markdown
# Code-Switching Eval — Gold Set and Probes
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Language**.
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> To reorient, go back here:
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>
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> - [**Language** — multilingual processing and semantic alignment](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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A compact harness to verify **retrieval and reasoning under code-switching** and multilingual inputs. Use this to measure ΔS and λ across three variants of each question: source language, target language, and mixed script or code-switched.
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---
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## Open these first
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* Visual map and recovery → [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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* End to end retrieval knobs → [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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* Why this snippet, how to cite → [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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* Snippet schema fence → [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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* Embedding vs meaning → [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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* Chunk boundary sanity → [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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* Tokenizer issues → [tokenizer\_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/tokenizer_mismatch.md)
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* Mixed scripts inside one query → [script\_mixing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/script_mixing.md)
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* Locale normalization and variants → [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md)
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* End to end recipes → [multilingual\_guide.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/multilingual_guide.md)
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---
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## Acceptance targets
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* ΔS(question, retrieved) ≤ 0.45 for each of the three variants
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* Coverage of the target section ≥ 0.70
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* λ remains convergent across three paraphrases and two seeds
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* E\_resonance stays flat on long windows that mix scripts
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---
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## Gold set format
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Create a small file you can run in any stack. CSV or JSONL both fine. Recommended CSV schema:
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```
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id,question_src,question_l2,question_mix,anchor_doc_id,anchor_offsets,answer_short,notes
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ex001,"what is the dosage of abc?","藥品abc的劑量是多少？","abc dosage要怎麼算？",doc_042,"[120, 180]","10 mg BID","CJK mix"
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```
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Rules
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* `question_src` is your primary language.
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* `question_l2` is the same meaning in the second language.
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* `question_mix` is a natural code-switch that users would actually type.
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* `anchor_doc_id` and `anchor_offsets` point to the authoritative span.
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* Keep 20 to 50 rows for a first pass.
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---
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## 60-second runner outline
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You can paste this into any quick script. Replace the retrieval call with your own client.
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```python
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# minimal pseudo-runner for ΔS and λ probes
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from collections import defaultdict
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K_LIST = [5, 10, 20]
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VARIANTS = ["question_src", "question_l2", "question_mix"]
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def retrieve(q, k):
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# call your store, return list of {doc_id, offsets, text}
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return call_retriever(query=q, top_k=k)
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def delta_s(a, b):
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# use your ΔS implementation. lower is better.
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return compute_delta_s(a, b)
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def lambda_state(history):
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# classify →, ←, <>, × based on stability of attention or order
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return classify_lambda(history)
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def run_row(row):
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out = []
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for v in VARIANTS:
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q = row[v]
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lambda_hist = []
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best = None
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for k in K_LIST:
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hits = retrieve(q, k)
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if not hits:
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lambda_hist.append("×")
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continue
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# pick your best hit by rerank or score
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best = hits[0]
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s_qr = delta_s(q, best["text"])
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s_ra = delta_s(best["text"], anchor_text(row["anchor_doc_id"], row["anchor_offsets"]))
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lambda_hist.append((k, s_qr, s_ra))
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out.append({
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"variant": v,
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"lambda": lambda_state(lambda_hist),
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"s_qr@5": lambda_hist[0][1] if lambda_hist and isinstance(lambda_hist[0], tuple) else 1.0,
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"s_qr@10": lambda_hist[1][1] if len(lambda_hist) > 1 and isinstance(lambda_hist[1], tuple) else 1.0,
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"s_qr@20": lambda_hist[2][1] if len(lambda_hist) > 2 and isinstance(lambda_hist[2], tuple) else 1.0,
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})
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return out
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```
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Output columns you want in a flat table
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* `id`, `variant`, `λ_state`, `ΔS@5`, `ΔS@10`, `ΔS@20`, `pass`
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* Mark pass when ΔS ≤ 0.45 and λ is convergent.
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---
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## Probe plan
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1. **Lock analyzers**
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Use the same normalization at index and query. If you must change anything, run the whole gold set again. Only one variable at a time.
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2. **Triplet run**
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For each row run `question_src`, `question_l2`, `question_mix` at k ∈ {5, 10, 20}. Log ΔS and λ per variant.
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3. **Three-paraphrase run**
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For the worst failing variant, add three small paraphrases in the same language. If λ flips while citations stay valid, apply schema locks and BBAM.
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4. **Anchor triangulation**
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Compare ΔS to the anchor span and to a decoy in the same document. If both similar, re-chunk and re-embed.
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---
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## Decision rules
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* **Only the mixed variant fails**
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Suspect script mixing or analyzer split. Open [script\_mixing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/script_mixing.md).
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Also verify you did not change casing or width in only one path.
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* **L2 fails, source and mix pass**
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Likely tokenizer mismatch or missing segmenter. Open [tokenizer\_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/tokenizer_mismatch.md).
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* **zh-Hans vs zh-Hant do not co-retrieve**
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Add variant mapping and normalize digits and punctuation. Open [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md).
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* **All three variants fail with high similarity**
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Embedding is not multilingual or metric is wrong. Open [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) and the Retrieval Playbook to rebuild.
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* **Hybrid worse than single**
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Fix query parsing split and reranker weights. Open [patterns/pattern\_query\_parsing\_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md) and [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md).
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---
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## Copy-paste prompt for the LLM judge step
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```
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You have TXTOS and the WFGY Problem Map loaded.
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Context:
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- gold id: {id}
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- variant: {variant}
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- retrieved snippet: "{snippet}"
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- expected anchor: "{anchor_excerpt}"
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- ΔS(question, retrieved) = {s_qr}
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- ΔS(retrieved, anchor) = {s_ra}
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Tasks:
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1) Decide pass or fail for retrieval alignment. If fail, name the failing layer.
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2) Return the exact WFGY page to fix it.
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3) Give a minimal repair plan with 3 steps.
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4) Output JSON only:
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{"pass": true|false, "fail_layer": "...", "open": [".../page1.md",".../page2.md"], "plan": ["step1","step2","step3"]}
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```
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---
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## Minimal dataset checklist
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* At least one trilingual example with names that have native and romanized forms
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* At least one code-switch that uses Latin terms inside CJK or Indic sentences
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* At least one RTL or diacritic heavy example
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* Keep a small “sanity” row that always passes after a clean rebuild
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---
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## What good looks like
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* Pass rate ≥ 90 percent on `question_src` and `question_l2`
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* Mixed variant pass rate ≥ 80 percent after analyzer unification
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* No λ flip in the three-paraphrase probe for the same variant
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* ΔS median ≤ 0.40 on all variants after fixes
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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| --------------------- | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| Starter Village | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: See the Hall of Fame** — Engineers, hackers, and open source builders who supported WFGY from day one.
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> [Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers) • [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) • [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md)
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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