---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- text-generation
- dialogue
- gricean-maxims
- cooperative-communication
- t5
- text-repair
- seq2seq
- nlp
datasets:
- topical-chat
metrics:
- bleu
pipeline_tag: text-generation
base_model: google-t5/t5-base
model-index:
- name: GriceBench-Repair
  results:
  - task:
      type: text-generation
      name: Gricean Maxim Violation Repair
    dataset:
      name: Topical-Chat (GriceBench repair validation split, N=401)
      type: topical-chat
      split: validation
    metrics:
    - type: bleu
      value: 0.978
      name: Quality BLEU
    - type: bleu
      value: 0.925
      name: Manner BLEU
    - type: bleu
      value: 0.618
      name: Quantity BLEU
    - type: accuracy
      value: 0.930
      name: Violation Removal Rate
---

<div align="center">

# 🔧 GriceBench-Repair

**Rewrites Gricean maxim violations into cooperative dialogue — surgically, not generally.**

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![HuggingFace](https://img.shields.io/badge/🤗-GriceBench-yellow)](https://huggingface.co/Pushkar27)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

**Part of the GriceBench system** —
[GitHub](https://github.com/PushkarPrabhath27/Research-Model) |
[🔍 Detector](https://huggingface.co/Pushkar27/GriceBench-Detector) |
[⚡ DPO Generator](https://huggingface.co/Pushkar27/GriceBench-DPO)

</div>

---

## What This Model Does

GriceBench-Repair is a T5-base seq2seq model that rewrites Gricean maxim violations into cooperative responses. It is **violation-type-aware**: different maxims use different generation strategies because the nature of the repair task differs.

| Violation | Decoding Strategy | Why |
|-----------|------------------|-----|
| **Quantity** | Beam search (n=4) + length constraints | Needs precise length control |
| **Quality** | Beam search (n=4) + repetition penalty | Needs factual precision |
| **Manner** | Nucleus sampling (T=0.85, top-p=0.92) | Needs creative diverse rewrites |
| **Relation** | NOT this model — use FAISS retrieval | Entire response is off-topic; editing cannot fix it |

**Violation removal rate: 93.0%** (post-fix evaluation, N=200)

---

## Quick Start

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

model_name = "Pushkar27/GriceBench-Repair"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()

def repair_violation(context: str, response: str, violation_type: str) -> str:
    assert violation_type in ["quantity", "quality", "manner"], \
        "Relation violations must use the FAISS retrieval system — not this model."

    input_text = f"fix {violation_type}: [CONTEXT] {context} [RESPONSE] {response}"
    inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)

    with torch.no_grad():
        if violation_type == "manner":
            output_ids = model.generate(
                **inputs,
                do_sample=True, temperature=0.85, top_p=0.92,
                max_length=128, min_length=8,
                repetition_penalty=1.5, no_repeat_ngram_size=3,
            )
        else:
            output_ids = model.generate(
                **inputs,
                num_beams=4, max_length=128, min_length=8,
                repetition_penalty=1.5, no_repeat_ngram_size=3,
            )

    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Quantity (too short)
print(repair_violation(
    context="What do you think about commercial space travel?",
    response="It's fine.",
    violation_type="quantity"
))

# Manner (ambiguous pronouns)
print(repair_violation(
    context="Alice told Bob she would handle the project.",
    response="She said she would do it before she left.",
    violation_type="manner"
))
```

---

## Performance

**Violation removal rate: 93.0%** (post-fix evaluation)

Per-maxim BLEU scores on the repair validation set (N=401):

| Violation Type | BLEU | Notes |
|----------------|------|-------|
| Quality | **97.8%** | Near-perfect factual correction |
| Manner | **92.5%** | Strong clarity improvements |
| Quantity | 61.8% | Harder — requires insertions/deletions |
| Relation | N/A | Route to FAISS retrieval |

**Degeneracy fix (before vs. after violation-type-aware decoding):**

| Maxim | Before Fix | After Fix | Improvement |
|-------|-----------|-----------|-------------|
| Quantity | 30.1% degenerate | 2.1% | **−28.0pp** |
| Manner | 93.3% degenerate | 4.5% | **−88.8pp** |
| Overall | 64.4% degenerate | 5.2% | **−59.2pp** |

---

## Architecture & Training

- **Base model:** `google-t5/t5-base` (220M parameters)
- **Training pairs:** 3,210 (violation → cooperative) seq2seq pairs
- **Validation pairs:** 401
- **Epochs:** 5 | **Label smoothing:** 0.1 | **Hardware:** Kaggle T4

**Three-layer degeneracy prevention:**
1. Violation-type-aware decoding (nucleus sampling for Manner, beam for others)
2. Post-generation multi-signal filter
3. Graceful fallback with `is_fallback: True` flag

---

## Why Relation Violations Use Retrieval

Relation violations mean the *entire response* is off-topic — there is nothing to edit. We route Relation repairs to a FAISS index over 50,000 Topical-Chat responses (MRR > 0.70, Top-1 accuracy > 60%).

---

## Files

| File | Description |
|------|-------------|
| `config.json` | T5-base configuration |
| `model.safetensors` | Trained model weights |
| `tokenizer.json` | SentencePiece tokenizer |
| `tokenizer_config.json` | Tokenizer configuration |

---

## Limitations & Biases

- **Hallucination Risk:** T5 can occasionally introduce factual errors during repair. Always verify with the "Quality" detector.
- **Mode Collapse:** Avoid using beam search for "Manner" repairs.

---

## Citation

```bibtex
 @article{prabhath2026gricebench,
  title={GriceBench: Operationalizing Gricean Maxims for Cooperative Dialogue Evaluation and Generation},
  author={Prabhath, Pushkar},
  year={2026},
  note={Under review, EMNLP 2026}
}
```

---

## Related Models

| Model | Role | Link |
|-------|------|------|
| GriceBench-Detector | Detects which maxim was violated | [🔍 Detector](https://huggingface.co/Pushkar27/GriceBench-Detector) |
| GriceBench-Repair | Repairs violations (this model) | You are here |
| GriceBench-DPO | Generates cooperative responses | [⚡ DPO](https://huggingface.co/Pushkar27/GriceBench-DPO) |

**GitHub:** https://github.com/PushkarPrabhath27/Research-Model

---

## Environmental Impact

| Aspect | Value |
|--------|-------|
| Hardware Used | NVIDIA Tesla T4 GPU |
| Training Time | ~2 hours |
| Estimated Carbon Footprint | ~0.25 kg CO2eq