| """Three-judge validity comparison figure. |
| |
| Why: the original two-judge figure predates (a) the gpt-oss-120b result and |
| (b) the UNREADABLE/BLIND distinction. It also understated the sharpest finding: |
| gpt-oss-120b scores the ROBOTIC transcript HIGHER than the real one. |
| """ |
| import json, matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
|
|
| SRC = { |
| "Qwen2.5-32B": "judgecheck/judgecheck.json", |
| "Qwen2.5-72B-AWQ": "judgecheck_72b/judgecheck.json", |
| "gpt-oss-120b": "judgecheck_gptoss120b_v2/judgecheck.json", |
| } |
| D = {} |
| for name, f in SRC.items(): |
| D[name] = json.load(open(hf_hub_download("ygoldi/edumirror-repro-results", f, |
| repo_type="dataset"))) |
|
|
|
|
| def verdict(d): |
| """Derive the verdict under current semantics (the two early probes predate the field).""" |
| a = d["absolute"] |
| if "verdict" in a: |
| return a["verdict"] |
| |
| return "VALID" if all((m or 0) > 0 for m in a["margins"].values()) else "BLIND" |
|
|
|
|
| judges = list(SRC) |
| corr = ["shuffled", "robotic"] |
| COL = {"Qwen2.5-32B": "#C44E52", "Qwen2.5-72B-AWQ": "#4C72B0", "gpt-oss-120b": "#DD8452"} |
|
|
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.2, 4.6)) |
| x = np.arange(len(corr)); w = 0.26 |
|
|
| for i, j in enumerate(judges): |
| m = [D[j]["absolute"]["margins"][c] for c in corr] |
| off = (i - 1) * w |
| bars = ax1.bar(x + off, m, w, label=f"{j} [{verdict(D[j])}]", color=COL[j]) |
| for xi, v in zip(x + off, m): |
| ax1.text(xi, v + (0.06 if v >= 0 else -0.17), f"{v:+.2f}", ha="center", fontsize=8, |
| fontweight="bold" if v <= 0 else "normal", |
| color="#B22222" if v <= 0 else "#333") |
| ax1.axhline(0, color="#333", lw=1.2) |
| ax1.axhline(2.45, color="#C9A24A", ls="--", lw=1.5) |
| ax1.text(-0.45, 2.52, "paper's Table 1 spread (2.45)", fontsize=8, color="#8a6d1f") |
| ax1.set_xticks(x); ax1.set_xticklabels(["shuffled\n(kills coherence)", "robotic\n(kills naturalness)"]) |
| ax1.set_ylabel("Score margin: real − corrupted") |
| ax1.set_title("Absolute rater: does it score a corrupted transcript lower?") |
| ax1.legend(fontsize=7.5, loc="upper left"); ax1.grid(axis="y", alpha=0.3) |
| ax1.set_ylim(-0.9, 2.9) |
| ax1.annotate("gpt-oss-120b rates the ROBOTIC transcript\nHIGHER than the real one (inverted)", |
| xy=(1 + w, -0.55), xytext=(-0.42, 1.35), fontsize=8.5, color="#B22222", |
| fontweight="bold", ha="left", |
| arrowprops=dict(arrowstyle="->", color="#B22222", lw=1.3, |
| connectionstyle="arc3,rad=-0.15")) |
|
|
| for i, j in enumerate(judges): |
| p = [D[j]["pairwise"][c]["real_win_rate"] for c in corr] |
| off = (i - 1) * w |
| ax2.bar(x + off, p, w, label=j, color=COL[j]) |
| for xi, v in zip(x + off, p): |
| ax2.text(xi, v + 0.03, f"{v:.2f}", ha="center", fontsize=8, |
| fontweight="bold" if v < 0.5 else "normal") |
| ax2.axhline(0.5, color="#333", ls=":", lw=1.2); ax2.text(-0.45, 0.52, "chance", fontsize=8) |
| ax2.axhline(0.7, color="#888", ls="--", lw=1); ax2.text(-0.45, 0.72, "validity bar", fontsize=8, color="#666") |
| ax2.set_xticks(x); ax2.set_xticklabels(["shuffled\n(kills coherence)", "robotic\n(kills naturalness)"]) |
| ax2.set_ylabel("Win rate of the REAL transcript"); ax2.set_ylim(0, 1.18) |
| ax2.set_title("Pairwise judge: does it prefer the real transcript?\n(SATURATES at 1.00 — a gate, not a ranking)") |
| ax2.legend(fontsize=7.5, loc="lower right"); ax2.grid(axis="y", alpha=0.3) |
|
|
| fig.suptitle("Judge validity across three open judges: bigger is not better.\n" |
| "Only Qwen2.5-72B is usable for absolute scoring — and only barely.", fontsize=11) |
| fig.tight_layout() |
| fig.savefig("outputs/figures/judge_validity.png", dpi=150) |
| print("wrote outputs/figures/judge_validity.png") |
|
|
| import csv |
| rows = [] |
| for j in judges: |
| a, pw = D[j]["absolute"], D[j]["pairwise"] |
| for c in corr: |
| rows.append({"judge": j, "corruption": c, |
| "absolute_real_avg": a["real"]["average"], |
| "absolute_corrupt_avg": a[c]["average"], |
| "absolute_margin": a["margins"][c], |
| "absolute_verdict": verdict(D[j]), |
| "pairwise_real_win_rate": pw[c]["real_win_rate"], |
| "pairwise_n": pw[c]["n"], |
| "pairwise_verdict": pw.get("verdict", "VALID" if all( |
| (pw[k]["real_win_rate"] or 0) >= 0.7 for k in corr) else "BLIND")}) |
| with open("outputs/judge_validity.csv", "w", newline="") as f: |
| wr = csv.DictWriter(f, fieldnames=list(rows[0])); wr.writeheader(); wr.writerows(rows) |
| print(open("outputs/judge_validity.csv").read()) |
|
|
