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Model: jamesjunyuguo/uncertain-calibrate
Source: Original Platform

README.md

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+---
+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)
+
+