googlecloudplatform--generative-ai
wehub-resource-sync
171 行
5.9 KiB
Markdown
171 行
5.9 KiB
Markdown
# gemini-hallcheck
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Confidence-targeted, abstention-aware hallucination evaluator for Gemini via `google-genai`.
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- Implements the Kalai et al. idea in practice: **"Answer only if you're > t confident; otherwise say `IDK`."**
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- Scores with an abstention-aware loss: **correct = +1, wrong = −t/(1−t), IDK = 0**.
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- Produces a **risk–coverage curve** (conditional accuracy vs. coverage) with **labeled t-points**.
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- Works from **CSV** or directly from **MMLU** (Hugging Face), with **random sampling** and an **`--idk-frac`** mixer to create **IDK-only** items (tests true abstention).
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- **LLM semantic judge** (Gemini 2.5 Flash-Lite) or **exact** judge.
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- **Async** with progress bar, **quota-aware retries** (honors server `RetryInfo`) and optional **client-side RPM throttle**.
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- Runs on **Gemini API** or **Vertex AI** (env-based switch).
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> ℹ️ We do **not** implement MMLU-Pro here.
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---
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## Install
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```bash
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python -m venv .venv && source .venv/bin/activate
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pip install -e .
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```
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### Auth options
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**Gemini API (Developer API):**
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```bash
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export GOOGLE_API_KEY=YOUR_KEY # or GEMINI_API_KEY
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```
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**Vertex AI:**
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```bash
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export GOOGLE_GENAI_USE_VERTEXAI=true
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export GOOGLE_CLOUD_PROJECT=your-gcp-project
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export GOOGLE_CLOUD_LOCATION=us-central1 # or europe-west1, etc.
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# Do NOT set GOOGLE_API_KEY when using Vertex AI mode.
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```
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---
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## Quickstart
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### CSV mode
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```bash
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gemhall run --data examples/toy.csv --thresholds 0.5 0.75 0.9 --model gemini-2.5-flash-lite --progress --out outputs
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```
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### MMLU (direct from HF Datasets)
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```bash
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gemhall mmlu --thresholds 0.5 0.75 0.9 --model gemini-2.5-flash-lite --split test --subjects all --limit 200 --judge llm --async --concurrency 16 --progress --out outputs/mmlu
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```
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**Mix in "IDK-only" items** (turns a fraction of sampled items into unanswerables so the only correct behavior is `IDK`):
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```bash
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--idk-frac 0.3
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```
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---
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## What you get
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- `results.csv` – one row per (item × t): prediction, abstained flag, correctness, score
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- `metrics.json` – coverage, conditional accuracy (on answers), hallucination rate among answers, avg expected score
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- `behavior.json` – simple behavior checks (e.g., monotonic coverage as t increases)
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- `rc_curve.png` – **risk–coverage curve** with **"t=…"** labels on each point
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- `report.md` – short summary plus the chart embedded
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---
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## Interpreting the curve
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- **Coverage** (x-axis): fraction of items the model answered (didn't say `IDK`).
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- **Conditional accuracy** (y-axis): how often it was correct **when it did answer**.
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- As **t** rises ⇒ coverage falls, conditional accuracy should rise.
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- If **accuracy at t** is **well below t**, the model is **over-confident or non-compliant** → raise t, harden prompts, add retrieval/handoffs, or re-calibrate.
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---
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## CLI reference
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Shared flags (both `run` and `mmlu`):
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```sh
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--thresholds FLOAT... Confidence thresholds (e.g., 0.5 0.75 0.9) [required]
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--model TEXT Gemini model id (default: gemini-2.5-flash)
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--temperature FLOAT Sampling temperature (default: 0.0)
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--thinking-budget INT Optional thinking budget (default: 0)
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--seed INT RNG seed for sampling (default: 1234)
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--judge {exact,llm} Validity judge (exact or LLM; default: exact)
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--async Use async client for parallel requests
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--concurrency INT Max concurrent requests in async mode (default: 8)
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--progress Show a progress bar
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--out PATH Output directory (default: outputs)
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--rpm-limit INT Client-side requests-per-minute cap (optional)
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--max-retries INT Max retries on 429 with backoff (default: 6)
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```
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`run` (CSV):
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```sh
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--data PATH CSV with columns: id, question, gold, unknown_ok
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```
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`mmlu` (Hugging Face "cais/mmlu"):
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```sh
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--split TEXT Split (e.g., test, dev) [default: test]
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--subjects STR... Subject names or 'all' [default: all]
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--limit INT Randomly sample N items after filtering subjects
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--idk-frac FLOAT Fraction [0..1] to convert to IDK-only items (default: 0.0)
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```
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**MMLU loader notes:**
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We first try the unified `"all"` config and fall back to stitching per-subject configs if needed. No `trust_remote_code` required. We also ensure a `subject` column exists.
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---
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## Judges
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- **exact** – strict match for MCQ (letters A/B/C/D), or numerical/text equality for free-form.
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- **llm** – Gemini 2.5 Flash-Lite "YES/NO" grader; for `unknown_ok=1`, only `IDK` is considered correct (no LLM call).
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---
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## IDK detection & scoring
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- We normalize model outputs; `IDK` is recognized case-insensitively with common variants.
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- Score per item at threshold **t**:
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- answered & correct: **+1**
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- answered & wrong: **−t/(1−t)**
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- abstained (`IDK`): **0**
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---
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## Rate limits & retries
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- If you omit `--rpm-limit`, we still **auto-retry** on `429 RESOURCE_EXHAUSTED`, honoring server **`RetryInfo`** with jittered exponential backoff.
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- Set `--rpm-limit` to smooth out bursts and avoid 429s when running with high `--concurrency`.
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- Typical stable settings: `--async --concurrency 12 --rpm-limit 180 --max-retries 8`.
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---
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## Business mapping
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- **t≈0.5: Drafting & triage** – high coverage, human-in-the-loop
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- **t≈0.75: Assistive answers** – support suggestions, FAQ with citations
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- **t≈0.9: Self-serve replies** – public answers in non-regulated flows
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- **t≈0.95: High-stakes** – regulated/brand-critical, else handoff
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---
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## Troubleshooting
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- **MMLU config error**: we now request `"all"` and fall back per-subject; ensure `datasets>=2.18.0`.
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- **Curly-brace crash in prompts**: fixed by using f-strings (brace-safe).
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- **429 quota**: use `--rpm-limit`, and/or lower `--concurrency`.
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- **Vertex AI vs API key**: set `GOOGLE_GENAI_USE_VERTEXAI=true` (+ project/location) to use Vertex AI; don't set an API key at the same time.
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---
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## Citation
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This project is based on the following paper.
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[arXiv:2509.04664](https://arxiv.org/abs/2509.04664)
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