| # Which numbers are comparable |
|
|
| This repo holds runs made under three different configurations. Averaging or |
| ranking across them is invalid. Two axes vary independently: |
|
|
| - **Examiner** — `examiner/dyna_conv_v19.py:863-864` uses TWO models: |
| `llm_chat_context = LLMChat("gpt-5")` for grounded contexts and |
| `llm_chat_conv = LLMChat("gpt-4o")` for the question turns. "GPT-4o backbone" |
| refers only to the question turns. |
| - **SG parser** — all published rows were parsed with `gpt-4o-mini` |
| (`run_all_metrics.sh` sets `GED_MODEL=gpt-4o-mini`; other graders use local |
| Qwen3). |
|
|
| | tree | examiner | SG parser | comparable to published table? | |
| |---|---|---|---| |
| | `svg100_controlled/gemini-2.5-pro/` | gpt-5 + gpt-4o | gpt-4o-mini | **YES** | |
| | `svg500/gemini-2.5-pro/` | gpt-5.4-mini (both roles) | gpt-5.4-mini | no — both axes wrong | |
| | `svg500_gpt54_backbone/sg/*` | gpt-5.4-mini backbone | gpt-5.4-mini | parser axis wrong | |
| | `parser_ratio/` | n/a (fixed transcripts) | both, for measurement | n/a | |
|
|
| ## Why svg500/ is not usable as a Gemini row |
|
|
| `utils/llm.py` applied its Azure branch AFTER the OpenAI branch and overwrote |
| both the client and the requested model with `AZURE_OPENAI_DEPLOYNAME`. Because |
| the module calls `load_dotenv(".env")` at import and `.env` contains |
| `AZURE_OPENAI_KEY`, this could not be avoided by shell hygiene -- both examiner |
| roles silently collapsed into one deployment. Fixed: Azure is now an `elif` |
| fallback and logs any substitution. |
|
|
| Measured effect, same 100 images, paired: |
|
|
| | metric | controlled (gpt-5 + gpt-4o) | collapsed (gpt-5.4-mini) | |
| |---|---|---| |
| | rounds | 2239 | 4012 | |
| | avg rounds/conv | 11.2 | 20.1 | |
| | regular | 29.4% | 34.3% | |
| | follow-up | 25.0% | 47.5% | |
| | adversarial | 33.6% | 17.5% | |
| | **unanswerable** | **12.0%** | **0.7%** | |
|
|
| Ground truth was intact in both (adversarial `gt=="No"` 753/753 and 703/703; |
| unanswerable canonical 268/268 and 29/29) -- the transcripts are not corrupt, |
| the question mix is simply different, and the collapsed run also ran ~2x longer |
| conversations. |
|
|
| ## Parser effect (parser_ratio/, 60 fixed images) |
| |
| Same transcripts, same prompt and post-processing, only the parser model varies: |
| |
| - triples extracted: gpt-4o-mini 5008 vs gpt-5.4 2793 (**ratio 0.5577**) |
| - GED: 122.20 vs 71.35 (**ratio 1.7127**; against the stored column, 1.7225) |
| - per-image GED ratio: median 1.6887, range 1.07-3.14 |
| |
| Validation: recomputing GED from the stored gpt-4o-mini parse reproduced the |
| published column to within 0.57% (122.20 vs 122.90), 23/60 images exact. |
| |
| Caveat: GED is an anytime approximation against a 300s budget. 49/60 gpt-4o-mini |
| and 42/60 gpt-5.4 calls hit that cap, so both means are upper bounds. The ratio |
| is more trustworthy than either absolute number. |
| |
| **Do not apply the 1.71 parser ratio to `svg100_controlled/`** -- it is already |
| parsed with gpt-4o-mini and would be double-corrected. |
| |
| ## Headline numbers |
| |
| | run | GED | DeltaCon | images | |
| |---|---|---|---| |
| | gemini-2.5-pro, controlled + gpt-4o-mini parse | **119.03** | **4.5600** | 100 | |
| | gemini-2.5-pro, collapsed examiner + gpt-5.4 parse | 137.08 | 4.5100 | 500 | |
| | llava-1.5-7b, gpt-5.4 backbone + gpt-5.4 parse | 93.34 | 4.2540 | 286 | |
| | Qwen2.5-VL-7B, gpt-5.4 backbone + gpt-5.4 parse | 108.29 | 4.2919 | 217 | |
| |
| Only the first row is on the published footing. The llava/Qwen2.5 rows share |
| each other's configuration but not the published one. |
| |
