Becoming Experienced Judges: Selective Test-Time Learning for Evaluators (EACL 2026)

Official Implementation of the paper "Becoming Experienced Judges: Selective Test-Time Learning for Evaluators" (EACL 2026 [Short-Oral]).

Method	Description	Module
Vanilla	Fixed evaluation prompt.	methods/vanilla.py
Sample-Specific Prompt (SSP)	Static meta-prompt for all samples. Per-sample generated rubric, then judge.	methods/ssp.py
🌟 LWE	Evolving meta-prompt and per-sample generated rubric.	methods/lwe.py
🌟🌟 Selective LWE	Vanilla consistency check first -> LWE pipeline only on inconsistent samples.	methods/selective_lwe.py

Prompts are in the prompts/ directory.

Setup

cd lwe
pip install -r requirements.txt

Set API keys (add to .env or export directly):

export OPENAI_API_KEY=...       # for GPT models
export GEMINI_API_KEY=...       # for Gemini models
export ANTHROPIC_API_KEY=...    # for Claude models

Data Preparation

Click to expand

Download VL-RewardBench

python data/scripts/download_vlrewardbench.py --output_dir data/vlrewardbench

Saves data/vlrewardbench/vlrewardbench_test.jsonl and images under data/vlrewardbench/images/.

Download Multimodal RewardBench

python data/scripts/download_mmrewardbench.py --output_dir data/mmrewardbench

Creates data/mmrewardbench/mmrewardbench_test.jsonl, containing a random sample of 1000 examples (excluding Hateful Memes), and saves images to data/mmrewardbench/images/.

Options:

# Reproduce the exact 1000 examples used in the paper (recommended)
python data/scripts/download_mmrewardbench.py --paper_ids data/scripts/mmrewardbench_paper_ids.json

# Keep all examples (no subsampling)
python data/scripts/download_mmrewardbench.py --no_subsample

# Custom sample size
python data/scripts/download_mmrewardbench.py --sample_size 500

Data format

Each row in the JSONL must contain:

Field	Type	Description
ID	str	Unique sample identifier
Text	str	Question / instruction
Output1	str	First candidate response
Output2	str	Second candidate response
Better	str	"Output1" or "Output2" — the ground-truth preferred response
Image	str | null	Path to image file, or null for text-only

You can run with your own custom data by providing a JSONL file with the above format.

Simply point dataset.data_path in your config YAML to your custom JSONL file.

Run

Run from the lwe/ directory.

GPT

python judge.py --config configs/gpt_vanilla_vl.yaml # method: vanilla, dataset: VL-Rewardbench
python judge.py --config configs/gpt_vanilla_mm.yaml # method: vanilla, dataset: Multimodal Rewardbench
python judge.py --config configs/gpt_ssp_vl.yaml
python judge.py --config configs/gpt_lwe_vl.yaml
python judge.py --config configs/gpt_selective_lwe_vl.yaml

Gemini

python judge.py --config configs/gemini_selective_lwe.yaml

Claude

python judge.py --config configs/claude_selective_lwe.yaml

Additionally, you can override the method, seed, or model specified in your YAML config directly from the command line using arguments:

python judge.py --config configs/gemini_lwe.yaml --method selective_lwe --seed 42 --model gemini-2.5-flash

Outputs

Results are written under out_dir (default runs/), one subdirectory per run:

runs/<method>_<model>_<timestamp>/
  config.yaml                  — copy of the run config
  <dataset>.jsonl              — per-sample results
  cumulative_metrics.jsonl     — accuracy / consistency after each batch
  meta_prompt_v0_initial.txt   — (LWE / Selective-LWE) initial meta-prompt
  meta_prompt_final.txt        — (LWE / Selective-LWE) final meta-prompt
  meta_prompt_snapshots/       — (LWE / Selective-LWE) per-batch snapshots

Per-sample fields include pred, acc, swap_pred, swap_acc, consistency, pair_acc where applicable.

Code Structure

Click to expand

lwe/
  judge.py                     # entrypoint
  configs/                     # example YAMLs (vanilla/ssp/lwe/selective_lwe × gpt/gemini/claude)
  data/
    scripts/
      download_vlrewardbench.py  # HuggingFace download: MMInstruction/VL-RewardBench
      download_mmrewardbench.py  # HuggingFace download: syhuggingface/multimodal_rewardbench
    vlrewardbench/             # downloaded data
    mmrewardbench/             # downloaded data
  models/
    base.py                    # BaseModel interface
    gpt.py                     # OpenAI GPT
    gemini.py                  # Google Gemini
    claude.py                  # Anthropic Claude
  methods/
    vanilla.py
    ssp.py
    lwe.py
    selective_lwe.py
  prompts/
    vanilla.py                 # vanilla judge prompt
    lwe_prompts.py             # SSP / LWE prompt templates
  utils/
    dataset.py                 # PairwiseDataset + dataloader
    utils.py
  requirements.txt

Citation

If you find this work useful, please cite:

@inproceedings{jwa-etal-2026-becoming,
  title = {Becoming Experienced Judges: Selective Test-Time Learning for Evaluators},
  author = {Jwa, Seungyeon and Ahn, Daechul and Kim, Reokyoung and Kang, Dongyeop and Choi, Jonghyun},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2026},
  address = {Rabat, Morocco},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2026.eacl-short.50/},
  pages = {697--721}
}

License

This project is licensed under the MIT License. See the LICENSE file for details.

All datasets and codebases are used strictly for non-commercial academic research in compliance with their respective licenses. Users of this repository are responsible for ensuring compliance with the original dataset licenses.