finece-confidence

name	finece-confidence
title	Fine-Grained Confidence Estimation During LLM Generation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2508.12040
keywords	["confidence-estimation","uncertainty-quantification","generation-process","calibration"]
description	Provide continuous confidence scores throughout LLM text generation via supervised learning and backward confidence integration, enabling real-time uncertainty awareness

Mind the Generation Process: Fine-Grained Confidence During LLM Generation

Core Concept

FineCE enables language models to assess confidence in their own text generation in real-time. The approach trains a supervised confidence predictor that can leverage future context (backward confidence integration) to improve estimates for current sequences. Unlike post-hoc confidence estimation, FineCE provides granular confidence at each position during generation, enabling applications like beam search biasing, early stopping, and uncertainty-aware retrieval.

Architecture Overview

• Continuous Confidence Prediction: Score each position during generation
• Supervised Learning: Train separate confidence prediction model
• Backward Integration: Use future context to improve current estimates
• Multiple Strategies: Three positions for confidence estimation in sequence
• Calibration-Aware: Train to avoid overconfidence in incorrect predictions

Implementation Steps

Stage 1: Design Confidence Prediction Module

Create a model to estimate generation confidence.

# Supervised confidence prediction
import torch
from torch import nn
from typing import Tuple, List

class ConfidencePredictor(nn.Module):
    """Predict confidence scores for generated text"""

    def __init__(
        self,
        model_dim: int = 4096,
        hidden_dim: int = 1024,
        confidence_dim: int = 128
    ):
        super().__init__()
        self.model_dim = model_dim
        self.hidden_dim = hidden_dim

        # Input projection (from LM hidden state)
        self.input_proj = nn.Linear(model_dim, hidden_dim)

        # Confidence estimation backbone
        self.confidence_backbone = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.1)
        )

        # Output: confidence score per position
        self.confidence_head = nn.Linear(hidden_dim, 1)

    def forward(
        self,
        hidden_state: torch.Tensor,  # [batch, seq_len, model_dim]
        mask: torch.Tensor = None    # [batch, seq_len]
    ) -> torch.Tensor:
        """
        Predict confidence for each position.

        Returns:
            confidence: [batch, seq_len, 1] normalized to [0, 1]
        """
        
        projected = .input_proj(hidden_state)

        
        features = .confidence_backbone(projected)

        
        logit = .confidence_head(features)

        
        confidence = torch.sigmoid(logit)

        
         mask   :
            confidence = confidence * mask.unsqueeze(-)

         confidence


 :
    

     ():
        .predictor = predictor
        .optimizer = torch.optim.Adam(predictor.parameters(), lr=lr)

     () -> []:
        
        training_data = []

         gen_text, gt_text, hidden_state  (
            generated_texts, ground_truth, hidden_states
        ):
            
            gen_tokens = gen_text.split()
            gt_tokens = gt_text.split()

             pos, gen_token  (gen_tokens):
                 pos < (gt_tokens):
                    is_correct = gen_token == gt_tokens[pos]
                :
                    is_correct = 

                label = (is_correct)

                training_data.append({
                    : hidden_state[pos].unsqueeze(),
                    : label,
                    : pos,
                    : gen_text
                })

         training_data

     () -> :
        
        total_loss = 

         example  batch:
            hidden_state = example[]
            label = torch.tensor([example[]], dtype=torch.float32)

            
            confidence = .predictor(hidden_state)

            
            loss = torch.nn.functional.binary_cross_entropy(
                confidence,
                label.unsqueeze(-)
            )

            total_loss += loss.item()

            
            .optimizer.zero_grad()
            loss.backward()
            .optimizer.step()

         total_loss / (batch)

Stage 2: Implement Backward Confidence Integration (BCI)

Use future tokens to improve current position confidence.

# Backward Confidence Integration
class BackwardConfidenceIntegrator:
    """Integrate future context to improve confidence estimates"""

    def __init__(self, predictor: ConfidencePredictor):
        self.predictor = predictor

    def forward_confidence(
        self,
        hidden_states: torch.Tensor,  # [seq_len, model_dim]
        mask: torch.Tensor = None
    ) -> torch.Tensor:
        """Standard forward confidence: left-to-right"""
        confidence = self.predictor(hidden_states.unsqueeze(0), mask)
        return confidence.squeeze(0)

    def backward_integrated_confidence(
        self,
        hidden_states: torch.Tensor,  # [seq_len, model_dim]
        mask: torch.Tensor = None
    ) -> torch.Tensor:
        """
        BCI: Use future context to improve confidence.

        Key insight: A token's correctness often becomes clear from
        subsequent context. Use bidirectional information.
        """
        seq_len = hidden_states.shape[0]
        confidence_scores = []

        for pos in range(seq_len):
            # Current position hidden state
            current_hidden = hidden_states[pos:pos+1]

            # Future context (next 5 tokens or until end)
            future_start = min(pos + 1, seq_len)
            future_end = min(pos + 6, seq_len)
            future_hidden = hidden_states[future_start:future_end]

            if future_hidden.shape[] > :
                
                
                combined_hidden = torch.cat([
                    current_hidden,
                    future_hidden.mean(dim=, keepdim=)
                ], dim=-)

                
                combined_hidden = ._project_to_model_dim(combined_hidden)

                confidence = .predictor(combined_hidden)
            :
                
                confidence = .predictor(current_hidden)

            confidence_scores.append(confidence.squeeze())

        
        bci_confidence = torch.stack(confidence_scores)

         bci_confidence

     () -> torch.Tensor:
        
         tensor.shape[-] != .predictor.model_dim:
            
            proj = nn.Linear(tensor.shape[-], .predictor.model_dim)
            tensor = proj(tensor)
         tensor

Stage 3: Multiple Confidence Strategies

Offer different approaches for measuring confidence.

# Multiple confidence estimation strategies
class ConfidenceStrategies:
    """Different strategies for estimating confidence"""

    @staticmethod
    def strategy_1_current_only(
        predictor: ConfidencePredictor,
        hidden_states: torch.Tensor
    ) -> torch.Tensor:
        """
        Strategy 1: Confidence from current position only
        (Standard approach, left-to-right)
        """
        confidence = predictor(hidden_states.unsqueeze(0))
        return confidence.squeeze(0)

    @staticmethod
    def strategy_2_with_context(
        predictor: ConfidencePredictor,
        hidden_states: torch.Tensor,
        context_window: int = 3
    ) -> torch.Tensor:
        """
        Strategy 2: Use surrounding context (window of ±N positions)
        """
        seq_len = hidden_states.shape[0]
        confidence_scores = []

        for pos in range(seq_len):
            # Get context window
            context_start = max(0, pos - context_window)
            context_end = min(seq_len, pos + context_window + 1)

            context_hidden = hidden_states[context_start:context_end]

            # Mean pool or attention over context
            context_mean = context_hidden.mean(dim=0, keepdim=True)

            # Predict confidence
            confidence = predictor(context_mean)
            confidence_scores.append(confidence.squeeze())

        return torch.stack(confidence_scores)

    @staticmethod
    def strategy_3_backward_integration() -> torch.Tensor:
        
        integrator = BackwardConfidenceIntegrator(predictor)
         integrator.backward_integrated_confidence(hidden_states)

Stage 4: Online Confidence During Generation

Compute confidence scores during decoding.

# Real-time confidence during generation
class OnlineConfidenceScorer:
    """Compute confidence scores during LLM generation"""

    def __init__(
        self,
        language_model,
        confidence_predictor: ConfidencePredictor,
        strategy: str = "strategy_3_backward_integration"
    ):
        self.lm = language_model
        self.predictor = confidence_predictor
        self.strategy = strategy

    def generate_with_confidence(
        self,
        prompt: str,
        max_tokens: int = 256,
        temperature: float = 0.7,
        confidence_threshold: float = 0.5
    ) -> Dict:
        """
        Generate text and track confidence at each step.

        Returns:
            - generated_text: model output
            - confidence_scores: per-token confidence
            - divergence_points: where confidence drops below threshold
        """
        token_ids = self.lm.tokenize(prompt)
        generated_tokens = []
        confidence_scores = []
        hidden_states_list = []

        for step in range(max_tokens):
            # Get model hidden states
            with torch.no_grad():
                outputs = self.lm.forward(
                    input_ids=torch.tensor(token_ids + generated_tokens).unsqueeze(0)
                )
                hidden_state = outputs.hidden_states[-1]  # Last layer
                hidden_states_list.append(hidden_state[0, -1])

                
                logits = outputs.logits[, -, :]

            
            next_token = ._sample_token(logits, temperature)
            generated_tokens.append(next_token)

            
             (hidden_states_list) > :
                
                all_hidden = torch.stack(hidden_states_list)

                 .strategy == :
                    confidence = ConfidenceStrategies.strategy_1_current_only(
                        .predictor, all_hidden
                    )
                 .strategy == :
                    confidence = ConfidenceStrategies.strategy_2_with_context(
                        .predictor, all_hidden
                    )
                :  
                    confidence = ConfidenceStrategies.strategy_3_backward_integration(
                        .predictor, all_hidden
                    )

                current_confidence = confidence[-].item()
            :
                current_confidence =   

            confidence_scores.append(current_confidence)

            
             current_confidence < confidence_threshold  step > :
                

        
        generated_text = .lm.tokenizer.decode(generated_tokens)

         {
            : generated_text,
            : confidence_scores,
            : (confidence_scores) / (confidence_scores),
            : (confidence_scores)  confidence_scores  ,
            : (generated_tokens)
        }

     () -> :
        
        probs = torch.nn.functional.softmax(logits / temperature, dim=-)
         torch.multinomial(probs, ).item()

Stage 5: Applications Using Confidence

Leverage confidence for downstream applications.

# Applications leveraging confidence scores
class ConfidenceApplications:
    """Use confidence scores for improved generation"""

    def __init__(self, scorer: OnlineConfidenceScorer):
        self.scorer = scorer

    def biased_beam_search(
        self,
        prompt: str,
        num_beams: int = 4,
        length_penalty: float = 1.0
    ) -> List[Dict]:
        """
        Beam search biased by confidence scores.

        Hypothesis with higher average confidence are preferred.
        """
        beams = [
            {"text": "", "logprob": 0, "avg_confidence": 1.0}
        ]

        for step in range(256):
            candidates = []

            for beam in beams:
                # Generate next tokens
                completions = self.scorer.generate_with_confidence(
                    prompt + beam["text"],
                    max_tokens=3,
                    confidence_threshold=0.3
                )

                # Score by logprob + confidence
                avg_confidence = completions["avg_confidence"]
                logprob = 0  # Placeholder

                score = logprob + 0.3 * avg_confidence

                candidates.append({
                    : beam[] +  + completions[],
                    : score,
                    : avg_confidence
                })

            
            beams = (candidates, key= x: x[], reverse=)[:num_beams]

         beams

     () -> []:
        
        
        result = .scorer.generate_with_confidence(prompt)

        
        low_conf_threshold = 
        low_conf_positions = [
            i  i, conf  (result[])
             conf < low_conf_threshold
        ]

        
        retrieved = []
         pos  low_conf_positions:
            
            tokens_at_pos = result[].split()[pos:pos+]
            query = .join(tokens_at_pos)

            
            best_docs = ._semantic_search(query, knowledge_base, top_k=)
            retrieved.append((pos, query, best_docs))

         retrieved

     ():
        
         docs[:top_k]

Practical Guidance

Confidence Strategies Comparison

Strategy	Pros	Cons	Use Case
Current Only	Fast, simple	Limited context	Real-time systems
With Context	Moderate accuracy	Moderate overhead	Batch processing
BCI	Best accuracy	More computation	Offline generation

Integration with Generation

• Beam Search: Weight hypotheses by confidence
• Early Stopping: Stop when confidence stabilizes
• Uncertainty Sampling: Use for active learning
• Retrieval Augmentation: Retrieve when model uncertain

Training Data

• Collect authentic model generations with ground truth labels
• Balance correct and incorrect examples
• Include diverse domains for generalization
• ~100K-1M training examples for good calibration

When to Use FineCE

• Applications requiring uncertainty awareness
• Real-time systems needing confidence bounds
• Active learning or selective prediction
• Combination with retrieval augmentation

When NOT to Use

• Latency-critical systems (adds inference cost)
• Domains with clear correctness (confidence less useful)
• Models already well-calibrated

Calibration Considerations

• Train on representative distribution
• Monitor for overconfidence on errors
• Validate on held-out test set
• Consider post-hoc calibration (temperature scaling)

Reference

Mind the Generation Process: Fine-Grained Confidence during LLM Generation. arXiv:2508.12040

• https://arxiv.org/abs/2508.12040