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Knowing Before Answering: Decoding Language Models for Reliable RAG

Syed Mahbubul Huq, Christopher Child, Tillman Weyde, Pranava Madhyastha

arXiv:2608.27661v1cs.CLcs.AI

TL;DR

RAG systems need to distinguish answerable evidence from insufficient or conflicting retrieval, because models may otherwise produce unsupported responses. The paper constructs a controlled three-way benchmark and decodes evidence state from internal representations with lightweight routers. Across 16 models, the activation-based router outperforms prompting and specialised-RAG baselines, with informative signals concentrated in middle layers.

  • Problem

    Retrieved RAG documents can be insufficient or contradictory, while existing approaches largely reduce triage to answering versus refusing and operate on output-side signals.

  • Method

    The paper creates a controlled benchmark with Answer, Refuse, and Conflict labels and trains lightweight routers on hidden activations and attention-derived features.

  • Results

    Across 16 language models, the activation-feature router consistently outperforms prompt-based and RAG-specialised baselines, reaching up to 0.91 accuracy and reducing false answer rates by up to 75%.

  • Takeaways & Limitations

    Language models internally encode whether retrieved evidence is sufficient, insufficient, or contradictory, and this signal can be reliably decoded for RAG triage.

  • Takeaways & Limitations

    The benchmark is a controlled diagnostic testbed rather than a replacement for naturalistic RAG evaluation, and hidden-state signals do not fully explain generation.

Abstract

from arXiv · show

In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should not only know when to answer but also be able to identify cases in which the documents provided in RAG are insufficient or contain conflicting information. This can be framed as a three-way classification problem, where we use the model's internal signals to determine whether the provided information in the input can be classified as sufficient, insufficient, or conflicting. We create a controlled benchmark dataset that replicates a RAG setup with fictitious information and labels each instance as answerable, insufficient, or conflicting. We use hidden activations and attention-derived features as inputs to train a lightweight linear model to distinguish among the three classes. Across 16 language models spanning different architectures and a range of model sizes, our feature-based router consistently outperforms prompting-based baselines and the performance of specialised RAG-models. We further conduct analyses into the information dynamics of the models. We show that the most informative signals for the classification are available in the middle layers, with hidden activation states being more effective than attention values or the MLP-feature outputs in most of the tested models. Overall, our results suggest that language models internally encode whether retrieved evidence is sufficient to support answering, and that this signal can be decoded reliably for RAG triage.

1 Introduction

The paper reframes reliable RAG triage as distinguishing sufficient, insufficient, and contradictory evidence using internal model signals. A lightweight activation-based router outperforms prompting and specialised RAG baselines across 16 models, while informative signals concentrate in middle layers.

  • RAG retrieval can be insufficient or contradictory, yet models often generate unsupported or contradictory answers instead of acknowledging evidential limits.
  • Existing methods mainly use output-side prompting, confidence, retrieval relevance, or entailment and collapse evidence assessment into answering versus refusing.
  • The proposed triage router classifies retrieved evidence as Answer, Refuse, or Conflict using a logistic-regression module trained on one hidden layer.
  • Across 16 transformer models from 90M to 32B parameters, the router reaches up to 0.91 accuracy and reduces false answer rates by up to 75% versus the strongest prompt competitor.It requires no additional tokens or inference cost.
  • Layer analyses find the most informative representations in middle layers across model families and sizes, and patching those layers reshapes downstream behaviour.

2 Methodology

The methodology builds a controlled three-class benchmark that isolates evidence-state decisions, then trains and evaluates lightweight routers from internal representations and attention-derived features.

  • Task formulation: Each question and retrieved document set receives one label: ANSWER, REFUSE, or CONFLICT, based on whether evidence is sufficient and uniquely grounded, insufficient, or supports multiple answers.
  • Dataset construction: The benchmark modifies TriviaQA, HotpotQA, and Natural Questions, sampling 797 question–answer–evidence triples from each source.Counterfactual entity substitution reduces recovery of original answers from parametric knowledge.
  • Dataset construction: Each question produces aligned five-document ANSWER, REFUSE, and CONFLICT contexts, yielding 7,173 instances with a fixed 70%/10%/20% question-level split.
  • Dataset construction: The controlled benchmark is intended as a diagnostic testbed rather than a replacement for naturalistic RAG evaluation.Automated and human checks reported 100% agreement on a stratified 10% sample.
  • Evaluation: Evaluation reports Accuracy, Macro-F1, and False Answer Rate, with FAR measuring REFUSE or CONFLICT cases incorrectly predicted as ANSWER.Confidence intervals use 10,000 bootstrap resamples from 1,435 test instances.
  • Routers and baselines: The main router extracts last-token hidden-state and MLP features across layers, selects the best validation layer, and compares against an attention-weight router and multiple prompting, specialised-RAG, and surface-text baselines.

3 Results

The activation-feature router consistently outperforms prompting, specialised-RAG, and surface-text baselines, while layer-wise analyses show that useful evidence-state signals generally peak in middle layers.

  • Overall performance: Across all 16 models, the activation-feature router consistently outperforms prompt-based and RAG-specialised baselines on Accuracy, Macro-F1, and FAR.
  • Baseline performance: 0.34 and 0.37 overall accuracy are reported for Self-RAG and Chat-QA-1.5, respectively, indicating near-chance performance.
  • Baseline performance: 0.356 accuracy and 0.352 Macro-F1 are achieved by TF-IDF+LR, while zero-shot NLI DeBERTa achieves 0.369 accuracy and 0.338 Macro-F1.
  • Prompting failure modes: Prompt-based baselines commonly reach near-chance accuracy around 0.33, with smaller and non-instruction-tuned models often ignoring refusal or conflict instructions.
  • Prompting failure modes: Prompt engineering alone does not fix the observed failure modes, which are described as model-level behavioural biases rather than prompt interpretation errors.
  • Leakage control: NO CTX accuracy ranges from 0.24 to 0.36 across the model suite, supporting the effectiveness of entity substitution against parametric answer recovery.
  • Layer-wise analysis: The best extracted-feature layers generally occur in the middle region of models, although Falcon-H1-Tiny-90M and Falcon3-7B-1.58bit show different layer-performance characteristics.

4 Mechanistic Analysis

Across the tested models, hidden-state features are generally the strongest routing signals, and the most informative layer usually lies in the middle-to-upper-middle network. Model-specific exceptions include earlier peaks in Falcon-H1-Tiny-90M and unusually early attention peaks in Falcon3-7B-1.58bit.

  • Feature comparison: Hidden-state features are strongest in almost all models, followed by MLP features and attention-weight features.Hidden states integrate information from attention and MLP blocks, whereas MLP outputs and attention weights capture narrower signals.
  • Model-specific patterns: OLMo-2-1B is the exception where the MLP router slightly outperforms the hidden-state router; post-trained variants reverse this ordering without changing the depth region.The result suggests post-training integrates MLP information into hidden states while preserving the optimal layer range.
  • Layer location: Most models show an inverted-U layer profile peaking in the middle-to-upper-middle network, with broadly similar relative peak positions across model sizes.The OLMo-2 variants retain similar curve shapes and peak ranges after post-training, while fine-tuning mainly increases peak accuracy.
  • Model-specific patterns: Falcon-H1-Tiny-90M peaks earlier than all other tested models for all three router types.Its small hybrid Mamba/attention structure distinguishes its layer-wise behavior from standard dense transformers.
  • Model-specific patterns: Falcon3-7B-1.58bit has an extremely early attention-router peak, while its hidden-state and MLP routers retain middle-layer optima.The authors plausibly attribute this divergence to low-bit quantisation affecting attention-derived signals more strongly.
  • Model-specific patterns: Granite-3.1-8B-Instruct shows the strongest layer-to-layer fluctuation among dense models.Its scaling terms, depth, attention dropout, and grouped-query attention are proposed as factors behind the jagged profile.

5 Ablation

The ablations test transferability, prompt robustness, intervention effects, feature representations, and external transfer. Results support middle-layer control and three-way routing, while revealing sensitivity to prompt ordering and context-length shift.

  • Cross-Model Transferability: Fine-tuned OLMo-2 variants transfer well among SFT, DPO, and Instruct models, but transfer to and from the base model does not generalise.This pattern indicates that fine-tuning introduces a linear representation absent from the base variant.
  • Prompt Robustness: Performance is largely stable across Prompts P0 to P3 but drops on question-first Prompt P4, while style wrapping affects smaller base models more than larger ones.The findings indicate sensitivity to question position and model size under prompt variation.
  • Hidden-state Patching: Patching the best probe layer increases downstream source-class prediction across all directions and models, with refuse→answer and conflict→answer exceeding 0.70 in instruction-tuned models.Answer→refuse shifts are smaller but remain above chance in every tested model.
  • Hidden-state Patching: Interventions influence generated text but do not completely explain the model’s generation process.This tests propagation beyond downstream router predictions into free-form generation.
  • Three-way Ablation: 0.829 vs. 0.832 overall accuracy, while the three-way router achieves Macro-F1 of 0.829 vs. 0.811 and FAR of 0.104 vs. 0.127 against the binary router.Separating CONFLICT improves Macro-F1 and lowers false-answer rate without reducing overall accuracy.
  • Long-Context Feature-Source Ablation: Final-token representations outperform document-level pooling alternatives, but performance declines as retrieved-document count increases.The decline is larger when a router trained at k = 5 is tested on longer contexts, indicating context-length distribution shift.
  • External Transfer: 0.84–0.92 accuracy is achieved in zero-shot transfer to RAGTruth across four model families without RAGTruth supervision.Full results and limitations are reported in Appendix G.

6 Related Work

Prior RAG-faithfulness methods mainly use prompting, confidence, retrieval relevance, entailment, specialised models, or post-generation analysis. This work extends internal-state probing to three-way evidence-state classification before generation.

  • Prior approaches: Existing approaches often rely on prompting, confidence-based abstention, retrieval-side relevance estimation, or external entailment.These methods primarily operate on the model’s output side and generally frame the decision as answering versus refusing.
  • Internal signals: Internal-state methods have been used to probe reasoning correctness, detect hallucinations, and study interactions between retrieved and parametric knowledge.Related systems also use hidden states for binary retrieval decisions, uncertainty-guided retrieval, or post-generation hallucination detection.
  • Positioning: The paper studies whether ANSWER, REFUSE, and CONFLICT are linearly decodable from internal activations before generation.It identifies the three-way, pre-generation layer-probing and mechanistic analysis as an under-addressed direction in prior literature.

7 Conclusion

The experiments indicate that language models encode retrieved-evidence states internally before generating responses. A lightweight single-layer router decodes these states across diverse models, while middle-layer interventions reshape downstream behavior.

  • Conclusion: A logistic-regression router using one hidden layer distinguishes sufficient, insufficient, and contradictory evidence across 16 diverse language models.The approach achieves up to 0.91 accuracy and reduces false-answer rates by up to 75% relative to the strongest prompt-based baseline without additional token budget.
  • Conclusion: The most informative triage representations consistently emerge in middle layers across model families and remain stable under post-training.Hidden-state patching shows that targeted intervention at these layers reliably reshapes downstream behavior.
  • Conclusion: Conflicting evidence is especially practically dangerous because models may answer confidently from contradictory documents, yet it remains linearly decodable at the appropriate layer.This finding connects the paper’s three-way formulation with its mechanistic evidence about internal representations.

A Artefact Robustness

The router is evaluated with diagnostic ablations and robustness tests, including altered document formatting and alternative feature combinations. These analyses examine whether routing performance depends on selected representations and remains stable under controlled changes.

  • Artefact robustness: Back-translation changes document structure while preserving semantics, enabling a robustness test against formatting artefacts.The router trained on original data is evaluated on 60 translated-and-back-translated instances spanning ANSWER, REFUSE, and CONFLICT.
  • Router-design ablations: Table 3 compares baseline routing with hidden-state, MLP, layer-aggregation, and nonlinear-router ablations across three models.H denotes hidden-state features, M denotes MLP outputs, Top N selects top-performing layers, and All-H/M combines all layers.
  • Router-design ablations: Aggregating top layers or all layers yields small and inconsistent gains over the best single-layer setup, while combining hidden-state and MLP features provides limited improvement.OLMo-2-1B benefits more from broader aggregation than Granite-3.1-8B-Instruct and Qwen3-4B-Instruct.

C Activation Steering

Activation steering tests whether evidence-state directions can alter model behaviour, while prompt analyses examine robustness to template changes. Effects are generally strongest in middle-to-late layers, but vary by architecture and prompt structure.

  • Activation steering: Contrastive activation addition tests whether moving representations along probed evidence-state directions can control model responses.Steering vectors are constructed from REFUSE-vs-ANSWER and CONFLICT-vs-ANSWER mean differences at selected layers and intervention sites.
  • Activation steering: Figure 3 compares mean accuracy under hidden-state, MLP-output, and attention-output steering against a dashed baseline across downsampled layers.The evaluation uses 40 REFUSE and 40 CONFLICT examples per model.
  • Activation steering: Steering partially controls behaviour, with strongest effects usually in middle-to-late layers, while gains mainly come from MLP features rather than the most decodable hidden states.Falcon-H1-Tiny-90M and Llama-3.2-3B are excluded because they always generated answers in this setting.
  • Activation steering: Post-training increases OLMo steerability without relocating the effective layer, whereas instruction-tuned Qwen and Granite show smaller gains.Very late interventions can hurt performance, especially in Granite.
  • Prompt robustness: Prompt performance remains stable across P0–P3 but drops more under question-first P4 ordering, with P5 affecting some smaller models more strongly.The prompt set contains six variants, P0–P5, with deterministic output parsing.

G Zero-Shot Transfer to RAGTruth

The trained routers transfer zero-shot to the MS MARCO-derived portion of RAGTruth, retaining strong binary accuracy without in-domain adaptation. However, RAGTruth cannot directly evaluate transfer of the full three-way formulation because it lacks conflict annotations.

  • Transfer results: The routers retain 0.84 to 0.92 accuracy when transferred zero-shot to RAGTruth without additional training or adaptation.The evaluation uses the MS MARCO-derived portion and four model families.
  • Transfer results: RAGTruth transfer provides evidence that the learned routing signal extends beyond the controlled benchmark to naturally occurring RAG examples.This is treated as evidence of natural-domain transfer rather than a replacement for controlled three-way evaluation.
  • Evaluation boundary: Because RAGTruth lacks separate conflicting-evidence annotations, the transfer experiment evaluates a binary setting rather than Answer–Refuse–Conflict directly.A fully annotated naturalistic three-way benchmark would enable a more direct transfer test.
  • Model context: Table 4’s architectural comparison supplies context for model-specific layer-wise differences in the broader experiments.The model suite spans different families, parameter sizes, training stages, and architectures.

J Long-Context Feature-Source Ablation

Long-context and label-control analyses test whether routing depends on representation choice, context length, and meaningful evidence-state labels. The three-way formulation improves reliability metrics, while patched generations show substantial downstream changes.

  • Long-context ablation: Final-token representations remain stronger than document-level pooling as retrieved-document count increases, although performance generally declines for longer contexts.A router trained at k = 5 suffers an additional drop when evaluated at larger k values, indicating context-length distribution sensitivity.
  • Label control: Random-label training reduces average accuracy to 0.332 ± 0.013, close to three-way chance performance.The gap from real-label results indicates that probe performance depends on meaningful evidence-state labels.
  • Binary versus three-way: Binary and three-way formulations achieve similar average accuracy, but the three-way router has higher Macro-F1 and lower FAR.Separating CONFLICT improves reliability without substantially reducing overall classification accuracy.
  • Generation-level patching: 63–83% of patched generations reach Jaccard divergence ≥0.30 from unpatched outputs, with degeneration rate ≤0.01.Sentence-level comparisons show a semantic-similarity gain of +0.47 to +0.93 over within-class controls, with BERTScore F1 ≥0.87.
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