REVIEW 4 major objections 7 minor 9 references
Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models
T0 review · 4 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read This paper proposes Collaborative Decoding (CoDe), a training-free decoding strategy that fuses knowledge-conditioned and context-only output distributions with adaptive weights, claiming to make LLM responses simultaneously more faithful…
desk verdict CoDe is a solid, well-tested decoding method for knowledge-grounded dialogue with a promising adaptive dual-stream fusion, but its central reranking term (Eq. 9) is under-specified, possibly far more expensive than claimed, and the paper tunes on the test set without code. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is the fused distribution in Eq. (3), an interpolation between the prior distribution over next tokens given only the conversation and the posterior distribution given the external knowledge: $p_{\text{CoDe}}(y_t) \propto p_\theta(y_t\mid x,y_{<t})\,\left[p_\theta(y_t\mid x,k,y_{<t})/p_\theta(y_t\mid x,y_{<t})\right]^\alpha$, which is a pointwise-mutual-information reweighting of the context-only distribution. The scalar $\alpha$ is not fixed: it is computed per decoding step from a confidence score (geometric mean of max probability and inverse entropy) and a dynamic divergence term $\delta=\gamma\exp(\mathrm{JSD}(p_c\parallel p_k))$, so the model leans on the knowledge stream exactly when the two distributions disagree or when the parametric prior is uncertain. The second piece is the knowledge-aware reranking (Eq. 9), which adds semantic and attentive rewards--max cosine similarity of hidden states to knowledge tokens and max-pooled attention across layers and heads--to the fused score before the final argmax. This combination is what carries the claim: adaptation preserves expressiveness, while the reranking enforces faithfulness.
What would settle it
Build a set of prompt instances from NQ-Swap, where the provided knowledge contradicts the model's parametric answer, and record, for each reranked token, the attention-to-knowledge and hidden-state similarity scores. If CoDe promotes the knowledge-supported token on confident-conflict cases less often than CAD does, or if the reranking rewards are higher for tokens the model already believed before seeing the passage, the mechanism's core assumption fails.
Extended reading notes
Core claim
CoDe's central claim is that the fidelity-expressiveness trade-off in external-knowledge-augmented LLMs can be broken at decoding time by treating the model as two cooperating streams: an 'expressiveness-oriented' stream conditioned only on the conversation context and a 'faithfulness-oriented' stream conditioned on both context and knowledge. Their outputs are combined as $p_{\text{CoDe}}(y_t) = \mathrm{softmax}[\alpha\, \mathrm{logit}_\theta(y_t\mid x,k,y_{<t}) + (1-\alpha)\,\mathrm{logit}_\theta(y_t\mid x,y_{<t})]$, where the weight $\alpha$ is adapted per token from the model's confidence (maximum probability and entropy) and the Jensen-Shannon divergence between the two distributions, so that a confident, knowledge-aligned prior keeps its voice while conflicting or uncertain internal knowledge is down-weighted. A knowledge-aware reranking then scores top-k candidates by attention and cosine similarity to knowledge tokens, pulling the final selection toward evidence rather than parametric over-reliance. The paper reports that CoDe surpasses ten decoding baselines on three faithfulness metrics across six LLMs and three knowledge-grounded dialogue datasets, while matching or beating them on diversity, coherence, and creative knowledge use, and that it also improves accuracy on faithfulness-only QA and summarization benchmarks.
Load-bearing premise
The method assumes that a token's max attention to knowledge tokens and the cosine similarity of its hidden state to them reliably indicate whether the token is supported by the external knowledge, rather than reflecting the model's pre-existing parametric preferences.
Editorial extensions
If this is right
- RAG-style assistants can adopt CoDe as a plug-and-play decoding change, improving faithfulness and expressiveness without fine-tuning, auxiliary models, or added generation budgets.
- Deterministic decoding no longer forces a choice: CoDe's diversity and coherence scores match or beat stochastic sampling while keeping the fidelity gains of greedy and beam search.
- Smaller models benefit disproportionately: on several metrics CoDe lets a 3B model match or surpass larger baseline models, suggesting decoding strategy can partially compensate for scale.
- The method transfers beyond dialogue: accuracy on Natural Questions, NQ-Swap, and HalluEval improves, showing the fusion and reranking help in faithfulness-only settings too.
- The fidelity-expressiveness trade-off identified in prior work is not intrinsic; it can be dissolved by dynamically balancing parametric and external knowledge at the token level.
Reading between the lines
- A testable consequence the paper leaves implicit: the adaptive $\alpha$ formally resembles Bayesian model averaging, where the prior stream is discounted when evidence (knowledge) conflicts with it; similar confidence- and divergence-based gating could be applied to other evidence sources, such as vision or tool outputs.
- The reranking's reliance on attention and hidden-state similarity could be probed directly: if those signals track parametric confidence rather than external support, CoDe's gains on adversarial knowledge conflicts (e.g., NQ-Swap) would erode, and measuring per-token attention fidelity would clarify where the method's boundary lies.
- If the trade-off is genuinely broken at decoding time, a parallel hypothesis is that it is also breakable in training: objectives that preserve complementary internal/external knowledge streams might make the required fusion unnecessary.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces Collaborative Decoding (CoDe), a decoding-time method for knowledge-grounded generation in LLMs. CoDe has two components. The Adaptive Dual-Stream Fusion module computes two next-token distributions, one conditioned on context alone and one conditioned on context plus external knowledge, and combines them via a weight alpha that depends on model confidence and on the Jensen-Shannon divergence between the two distributions (Eqs. 3-8). The Knowledge-Aware Reranking module re-ranks the top-K candidates using a weighted combination of semantic cosine similarity and attention between candidates and knowledge tokens (Eq. 9). The method is evaluated on six LLMs (Llama-2-7B-chat, Llama-3.1-8B-chat, Mistral-7B-Instruct-v0.2, Qwen-2.5-3B/7B/14B) on FaithDial, HalluDial, and Wizard of Wikipedia, plus Natural Questions, NQ-Swap, and HalluEval, against ten baselines, with automatic, LLM-judge, and human evaluations. The paper reports consistent improvements in faithfulness (K-BP, F-Critic, H-Judge) while preserving or improving expressiveness (DIV, COH, CRE), and claims to break the faithfulness-expressiveness trade-off without additional training or generation budgets.
Significance. If the method works as described, it is a practically appealing contribution: it requires no training, is compatible with any autoregressive LLM, and the empirical evaluation is unusually broad. The paper's strengths include the breadth of models and datasets, the inclusion of human and LLM-based evaluation, and ablations that decompose the contributions of the fusion and reranking components. However, the central reranking mechanism in Eq. 9 is currently under-specified to the point of being ambiguous, and the hyperparameters are tuned on the test set. These issues must be resolved before the claim that CoDe breaks the trade-off can be accepted.
major comments (4)
- [Section 4.2, Eq. (9), and Table 5] The attentive reward att(y_t, k_j) is not computable as written. In a causal transformer, attention weights are defined over input positions, and the candidate next token y_t is not yet an input position at decoding step t. If att is read as the attention from the last input position to the knowledge tokens, it is identical for every candidate and therefore cannot change the argmax; in that case the ablation row '-Att' in Table 5 could not produce the reported drop in H-Judge from 90.8 to 88.3. If att is instead computed by appending each candidate to the prefix and running a forward pass, each decoding step requires K additional forward passes (K=4 in Appendix D), which contradicts the stated claim of 'without requiring additional ... generation budgets'. The same position ambiguity applies to the hidden state h_{y_t} in the semantic reward. The paper releases no code and gives no implementation note for Eq. 9, so this load-bearing faithfulness mechanism is currently under-specified. Please provide unambiguous pseudocode for one decoding step, state precisely where h_{y_t} and att(y_t, k_j) come from, and reconcile the compute cost with the no-additional-budget claim.
- [Section 5.3 and Figure 7] The hyperparameters beta and gamma are selected on the FAITH DIAL test set. Figure 7 reports optimal performance at beta=0.6 and gamma=3 with no held-out validation split, and the same values are then used for all experiments and all datasets. Because the headline results on FAITH DIAL (Tables 1 and 2) are obtained with these test-set-tuned values, the reported improvements may partly reflect overfitting to the evaluation set. This is load-bearing for the generalizability claim. The authors should either tune on a separate validation split (for example, a portion of the training set) or report results across a range of hyperparameters to show that the qualitative conclusions do not depend on the chosen values.
- [Introduction, Section 4.1, and Appendix D] The paper claims CoDe operates 'without requiring additional training, model, or generation budgets' (contributions bullet and Section 4), but the dual-stream fusion in Eq. 3 requires two full forward passes per decoding step, one with and one without external knowledge. If Eq. 9 is implemented with per-candidate forward passes, the compute cost grows further. No wall-clock time, FLOP counts, or forward-pass counts are reported for any method, so the budget claim is unsupported. Please define what counts as a 'generation budget' and quantify the actual overhead of CoDe relative to the baselines, or revise the claim accordingly.
- [Appendix E.1 and Table 1] The H-Judge metric is a re-trained version of HalluJudge, not the released model, because the original weights were unavailable. The paper states that Meta-Llama-3-8B was trained on HalluDial, but no evidence is given that this retrained judge reproduces the original HalluJudge's judgments, and the training-data size, evaluation accuracy, and agreement with the original model are not reported. Since H-Judge is one of the three headline faithfulness metrics and drives several specific claims, please report agreement on a sample with the original HalluJudge if obtainable, or at minimum give the training details and validation accuracy of the re-trained judge.
minor comments (7)
- [Section 5.1] There is a typo in the first sentence: 'e evaluated' should be 'We evaluated'.
- [Eq. (7)] The expression for C_t is ambiguous as typeset: it can be read as 2*sqrt(pmax)/H_t + eta. Since the text says a geometric mean is intended, please write C_t = 2 * sqrt(pmax / (H_t + eta)) with explicit parentheses.
- [Eq. (9)] The notation 'topK' is undefined. Please clarify whether the top-K candidates are selected before or after the weighted combination with the semantic and attentive rewards, and whether the resulting distribution is renormalized.
- [Section 5.2] The claim that CoDe 'also achieves top-2 performance in diversity and relevance metrics' is not supported by the reported tables; for example, in Table 6 (WoW-Seen) the CRE of CoDe is 27.7, lower than top-k (33.4), Nucleus (33.1), CD (32.9), DoLa (30.4), CS (29.0), and Greedy (28.7). Please qualify the claim or point to the specific metrics and tables that support it.
- [Appendix D] The sentence 'We conducted three experiments for all methods, using a different set of samples in each experiment' is unclear. Please specify what differs across the three runs and report standard deviations or confidence intervals so the reader can assess the stability of the reported averages.
- [Appendix H/I] Theorem 1 (F |= T) is proved under the assumption that the provided external knowledge K is a proper subset of world knowledge K_w, but this assumption is not stated in Definition 2. If the external knowledge contains outdated or erroneous information, a response can be faithful to K yet not factual with respect to K_w, so the theorem should be restated as conditional on K being a subset of true world knowledge.
- [General] No code is released. Given the implementation ambiguity in Eq. 9, a public implementation or detailed pseudocode would greatly aid reproducibility and verification.
Circularity Check
No circularity found: the method's fused distribution and reranking scores are empirical heuristics evaluated against external metrics, not predictions defined from their own inputs.
full rationale
The paper's core derivation is Eq. 3, a convex fusion of the context-only and knowledge-conditioned distributions, and Eq. 9, a token-reranking score combining model hidden-state similarity and attention to knowledge tokens. Eq. 4 is an algebraic rewrite of Eq. 3, not a separate predictive claim. The adaptive weight alpha (Eqs. 5-8) uses model confidence and Jensen-Shannon divergence, both computed from the model's own distributions; this is a legitimate controller design rather than circularity, because the predicted tokens are not defined in terms of the faithfulness metrics that the paper later reports (K-BP, F-Critic, H-Judge, human evaluation). Eq. 9's rhetorical rewards are heuristics intended to approximate faithfulness, and the paper tests that assumption through ablations in Table 5; even if attention or hidden-state similarity is a weak proxy, the paper does not define faithfulness as attention, nor does it fit a parameter to the evaluation metrics and then call that fit a prediction. The authors' self-citations appear in related work or as supporting references, not as a load-bearing uniqueness theorem or an ansatz on which the derivation depends. The main substantive concern with Eq. 9 is implementation under-specification: att(y_t, k_j) is not well-defined for an ungenerated candidate token in a single forward pass, which is a reproducibility and correctness risk, not a circularity of the paper's claimed derivation chain. The empirical claims are benchmarked against ten external baselines with independent metrics, so the central result is not forced by construction.
Assumptions & free parameters
free parameters (4)
- beta =
0.6
- gamma =
3
- k =
4
- eta =
small constant
assumptions (5)
- domain assumption External knowledge K is a proper subset of world knowledge Kw.
- domain assumption The confidence score Ct = 2*sqrt(pmax/(Ht+eta)) tracks factual confidence.
- ad hoc to paper Max attention weight from the decoding model to knowledge tokens indicates token-level faithfulness.
- ad hoc to paper Cosine similarity between candidate token hidden state and knowledge token hidden states indicates semantic alignment with knowledge.
- domain assumption Jensen-Shannon divergence between context-only and context-plus-knowledge distributions signals knowledge conflict.
Cite this review
Pith. "Pith review of Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models." pith.science (2026). https://pith.science/paper/G5GHPQJM
@misc{pith2026250818651,
author = {Pith},
title = {Pith review of: Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models},
year = {2026},
howpublished = {\url{https://pith.science/paper/G5GHPQJM}},
note = {Machine review of arXiv:2508.18651}
}
read the original abstract
Grounding responses in external knowledge represents an effective strategy for mitigating hallucinations in Large Language Models (LLMs). However, current LLMs struggle to seamlessly integrate knowledge while simultaneously maintaining faithfulness (or fidelity) and expressiveness, capabilities that humans naturally possess. This limitation results in outputs that either lack support from external knowledge, thereby compromising faithfulness, or appear overly verbose and unnatural, thus sacrificing expressiveness. In this work, to break the trade-off between faithfulness and expressiveness, we propose Collaborative Decoding (CoDe), a novel approach that dynamically integrates output probabilities generated with and without external knowledge. This integration is guided by distribution divergence and model confidence, enabling the selective activation of relevant and reliable expressions from the model's internal parameters. Furthermore, we introduce a knowledge-aware reranking mechanism that prevents over-reliance on prior parametric knowledge while ensuring proper utilization of provided external information. Through comprehensive experiments, our plug-and-play CoDe framework demonstrates superior performance in enhancing faithfulness without compromising expressiveness across diverse LLMs and evaluation metrics, validating both its effectiveness and generalizability.
Figures
Figures from the paper (5 more)
Reference graph
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