---
language:
- en
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- question-answering
- uncertainty-estimation
- retrieval-augmented-generation
- calibration
- llama-3.1
pipeline_tag: text-generation
---

# verbal-calibrate

This checkpoint is a fine-tuned variant of `meta-llama/Llama-3.1-8B-Instruct` for factual QA with explicit verbalized confidence.

## Intended behavior

Given a factual question, the model answers step by step and ends with exactly:

```text
Answer: <answer>
Confidence: <decimal between 0 and 1>
```

The confidence score is intended to reflect the model's uncertainty about the answer and can be used as a retrieval trigger in adaptive RAG pipelines.

## Motivation

- Adaptive retrieval gating with verbalized confidence
- Confidence-aware factual QA
- Research on uncertainty calibration and selective retrieval

---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
  - adaptive-rag
  - uncertainty-quantification
  - retrieval-augmented-generation
  - question-answering
language:
  - en
---

# verbal-calibrate

Fine-tuned from `meta-llama/Llama-3.1-8B-Instruct` to express **calibrated verbal confidence** for adaptive retrieval-augmented generation (RAG).

## What it does

Given a factual question, the model reasons step-by-step and ends every response with exactly two lines:


The confidence score reflects the model's genuine uncertainty. At inference, a confidence below 0.5 triggers BM25 retrieval and a second-pass generation with retrieved context. This allows the model to selectively retrieve only when it needs external evidence.

## Training

- **Base model**: `meta-llama/Llama-3.1-8B-Instruct`
- **Training method**: Supervised fine-tuning on QA data with confidence labels, followed by calibration to align expressed confidence with empirical accuracy
- **Target datasets**: Multi-hop QA (HotpotQA, MuSiQue, 2WikiMultiHopQA) and open-domain QA (NQ, TriviaQA)

## Evaluation (dev_500_subsampled, 500 questions × 5 datasets)

| Dataset | EM | F1 | Trigger Rate |
|---|---|---|---|
| HotpotQA | 32.0 | 43.8 | 61.6% |
| MuSiQue | 11.8 | 18.8 | 76.8% |
| 2WikiMultiHopQA | 28.4 | 32.9 | 48.2% |
| NQ | 32.4 | 44.4 | 25.0% |
| TriviaQA | 53.2 | 62.5 | 28.8% |
| **Overall** | **31.6** | **40.5** | **48.1%** |

Trigger rate = fraction of questions where confidence < 0.5 triggered retrieval.

## Intended use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/verbal-calibrate")
model = AutoModelForCausalLM.from_pretrained("your-username/verbal-calibrate")

prompt = tokenizer.apply_chat_template([{
    "role": "user",
    "content": (
        "Answer the following factual question step by step, then state your answer "
        "and how confident you are.\n\n"
        "{question}\n\n"
        "Your response must end with exactly these two lines:\n"
        "Answer: $Answer\n"
        "Confidence: $Confidence\n\n"
        "Where $Confidence is a decimal between 0 and 1."
    ).format(question="What is the capital of France?")
}], tokenize=False, add_generation_prompt=True)