---
license: apache-2.0
tags:
- finance
- financial-qa
- hallucination-detection
- verifier
- grpo
- lora
- transformers
- earnings-calls
- pytorch
language:
- en
pipeline_tag: text-generation
---

# PIT Finance GRPO + Verifier System

This repository contains a verifier-guarded financial question answering system built on top of the PIT-4B point-in-time financial language model family.

The project combines:

- A merged GRPO-aligned PIT generator
- Numeric verification
- NLI-based semantic grounding verification
- Abstention behavior for unsupported answers

The objective is to improve reliability and reduce hallucinations in financial QA over earnings-call transcripts and financial documents.

---

# Model Overview

## Base Model

- `Diamegs/PIT-4B-FT-202212`

## Alignment

The model was aligned using:

- reasoning supervision
- GRPO-style preference optimization
- LoRA fine-tuning
- hallucination-aware financial QA data

The LoRA adapter was merged into the final model weights.

## Final Merged Model

- `NurErtug/pit-finance-grpo-merged`

---

# Intended Use

The system is designed for:

- grounded financial QA
- earnings-call reasoning
- hallucination reduction
- numeric consistency
- abstention on unsupported questions
- verifier-guided inference

---

# Verifier Architecture

The verifier is implemented as an inference-time guardrail wrapper.

The verifier is **NOT** merged into model weights.

The pipeline operates as follows:

```text
User Question
      ↓
Merged PIT Generator
      ↓
Numeric Verifier
      ↓
NLI Verifier
      ↓
Abstention Policy
      ↓
Final Answer
```

---

# Verification Components

## 1. Numeric Verifier

Checks whether numeric claims generated by the model are supported by the evidence/context.

Examples:
- unsupported dollar amounts
- hallucinated percentages
- fabricated growth figures

---

## 2. NLI Verifier

Uses:

- `MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli`

to verify semantic support between:
- evidence/context
- generated answer

Possible outcomes:
- entailment
- neutral
- contradiction

---

## 3. Abstention Policy

If the verifier determines:
- insufficient evidence
- unsupported claims
- semantic contradiction

the system returns:

```text
NOT ENOUGH INFORMATION.
```

instead of hallucinating an answer.

---

# Installation

Clone the repository:

```bash
git clone https://huggingface.co/NurErtug/pit-finance-grpo-merged
cd pit-finance-grpo-merged
```

Install dependencies:

```bash
pip install -r requirements.txt
```

---

# Loading the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "NurErtug/pit-finance-grpo-merged"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True,
    use_fast=False,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

model.eval()
```

---

# Using the Verifier Wrapper

```python
from verifier_wrapper import VerifierWrapper

wrapper = VerifierWrapper()

context = """
Revenue increased 18% year-over-year.
Alternative minimum tax credits totaled $125 million.
"""

question = "What was the revenue growth?"

result = wrapper.answer(
    context=context,
    question=question,
)

print(result)
```

---

# Example Output

```python
{
    "initial_answer": "Revenue increased 18% year-over-year.",
    "final_answer": "Revenue increased 18% year-over-year.",
    "rejected": False,
    "reward": 1.8
}
```

Example rejected output:

```python
{
    "initial_answer": "$250 million",
    "final_answer": "NOT ENOUGH INFORMATION.",
    "rejected": True,
    "reject_reason": "verifier_failed"
}
```

---

# Repository Contents

```text
README.md
verifier_wrapper.py
requirements.txt
config.json
generation_config.json
model.safetensors
tokenizer files
```

---

# Research Motivation

Financial QA systems are especially vulnerable to:
- hallucinated financial figures
- unsupported claims
- temporal leakage
- fabricated reasoning

This project explores verifier-guided inference as a lightweight alternative to:
- expensive RLHF pipelines
- larger judge-model architectures
- fully integrated verifier training

The verifier wrapper provides an interpretable post-generation filtering mechanism for improving reliability in financial language models.

---

# Limitations

The system may still:
- over-abstain
- reject partially correct answers
- fail under highly ambiguous contexts
- struggle with extremely long contexts

The verifier improves robustness but does not guarantee factual correctness.

---

# Citation

```bibtex
@misc{pit_finance_verifier_2026,
  title={PIT Finance GRPO + Verifier System},
  author={Nur Ertug},
  year={2026},
  publisher={Hugging Face}
}
```

---

# License

Apache-2.0