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

# uncertain-calibrate

Fine-tuned from `meta-llama/Llama-3.1-8B-Instruct` via **GRPO reinforcement learning** to emit a special `<uncertain>` token when the model is uncertain during reasoning, enabling uncertainty-guided adaptive retrieval.

## What it does

The model reasons step-by-step and inserts `<uncertain>` at any point where it lacks confidence in a fact. A lightweight ridge regression probe (trained on layer-13 hidden states at the `<uncertain>` span) then decides whether to trigger BM25 retrieval and a second-pass generation.


## Training

- **Base model**: `meta-llama/Llama-3.1-8B-Instruct`
- **Training method**: GRPO (Group Relative Policy Optimization) with EM-based reward; the model is rewarded for correct final answers, encouraging it to emit `<uncertain>` in contexts where retrieval would help
- **Target datasets**: Multi-hop QA (HotpotQA, MuSiQue, 2WikiMultiHopQA) and open-domain QA (NQ, TriviaQA)

## Retrieval gating (probe)

A separate ridge regression probe on layer-13 hidden states over `<uncertain>` spans must be trained to use this model for adaptive RAG. The probe AUROC on held-out data is ~0.82. Use the companion probe artifact `uncertain_probe_layer13_alpha3000.pkl` from the [AdaRAGUE repository](https://github.com/JamesJunyuGuo/AdaRAGUE).

## Evaluation (dev_500_subsampled, 500 questions × 5 datasets, with probe gating)

| Dataset | EM | F1 | Trigger Rate |
|---|---|---|---|
| HotpotQA | 32.6 | 42.7 | 67.4% |
| MuSiQue | 7.6 | 14.1 | 94.2% |
| 2WikiMultiHopQA | 26.2 | 29.6 | 59.2% |
| NQ | 31.4 | 41.0 | 52.0% |
| TriviaQA | 56.6 | 63.2 | 34.0% |
| **Overall** | **30.9** | **38.1** | **61.4%** |

Trigger rate = fraction of questions where the probe decided to retrieve.

## Intended use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

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

SYSTEM = (
    "You are a helpful reasoning assistant. Think step by step. "
    "If at any point you are uncertain about a fact, emit the special token "
    "<uncertain> to signal that you need more information. "
    "End your response with 'Answer: <your answer>' on the last line."
)

prompt = tokenizer.apply_chat_template([
    {"role": "system", "content": SYSTEM},
    {"role": "user",   "content": "Who directed the film Interstellar?"},
], tokenize=False, add_generation_prompt=True)