ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
Authors: Pengcheng Huang
Zhenghao Liu
Yukun Yan
Haiyan Zhao
Xiaoyuan Yi
Hao Chen
Zhiyuan Liu
Maosong Sun
Tong Xiao
Ge Yu
Chenyan Xiong
Venue: NeurIPS 2025 poster ()
Keywords: contextual faithfulness
retrieval-augmented generation
Primary area: applications

Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence. However, they remain susceptible to unfaithful generation, where outputs contradict retrieved context despite its relevance and accuracy. Existing approaches aiming to improve faithfulness primarily focus on enhancing the utilization of external context, but often overlook the persistent influence of internal parametric knowledge during generation. In this work, we investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases. Building on this insight, we propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs and calibrating the model toward retrieved knowledge. To evaluate our approach, we introduce CoFaithfulQA, a benchmark specifically designed to evaluate faithfulness in scenarios where internal knowledge conflicts with accurate external evidence. Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory. These findings underscore the importance of mitigating internal knowledge dominance and provide a new direction for improving LLM trustworthiness in RAG. All codes are available at https://github.com/OpenBMB/ParamMute.

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmenter Generation
Pengcheng Huang', Zhenghao Yukun Yan", Haiyan Zhao?, Xiaoyuan Yi
Hao Chen Zhiyuan Liu", Maosong Sun", Tong Xiao Ge Yu², Chenyan Xiong
Schooo of Computer Science and Engineering, Northeastern University, China Department of Computer Science and Technology, Institute for AI, Tsinghua University, China *Microsoft Research Asia, Beijing, China Language Technologies Institute, Carnegie Mellon University, United States
Abstract
Large language models (LLMs) integrated with retrieval-augmeented generation (RAG) have improved factuality by grounding outputs in external evidence. How- ever, they remain susceptible unfaithful generation, where outputs contradict retrieved context despite its relevance and accuracy. Existing approaches aiming improve faithfulness primarily focus on enhancing the utilization of external context, but often overlook the persistent influence of internal parametric knowl- edge during generation. In this work, we investigate the internal mechanisms behind unfaithful generation and identify subset of mid-to-deep feed-forwan networks (FFNs) that are disproportionately activated in such cases. Building on this insight, we propose Parametric Knowledge Muting through FFN Suppression (PtramMute), a framework that improves contextual faithfulness by suppress- ing the activation of anffaihfuuness-asssccii toward retrieved knowledge. To evaluate our approach, we introduce CoFaith- fulQA, benchmark specifically designed to evaluate faithfulness in scenarios where internal knowledge conflicts with accurate external evidence. Experimen- tal results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory. These findings underscore the importance of mitigating internal knowledge dominance and provide new di- rection for improving LLM trustworthiness in RAG. All codes are available https:/ /aata:////ttttt//// wwt.ccmmmmocp.ee ParraMMttea
FFNs and calibrating the model
Introduction
Large language models (LLMs), such GPT-4 49 and LLaMA [59] have demonstrated excep-
tional performance across a wide range of natural language processing tasks [12] 38 64 77] (8).
Nonetheless, they are known suffer from hallucinations, frequently generating factually incorrect or fabricated information [14 25 42). To address this, retrieval-auge -uggenetd generation (RAG) has emerged promising paradigm, grounding model outputs external evidence to improve factual accuracy K5 76.. Despite these advancements, recent studies [1] have identified persistent and subtle challenge: L.LMs may still produce unfaithful responses that contradict or disregard external evidence even when this evidence is accurate and highly relevant 43 67]. Such unfaithful generation
can significantly undermine the reliability of RAG systems (26).
Recent approaches primarily seek to improve contextual faithfulness by enhancing the model's ability incorporate external evidence-eitthr through advanced prompting strategies 79 or context-
indicates corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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aware decoding techniques 23]. However, these externally focused methods often overlook the role of internal knowledge in undermining generation faithfulness. Motivated by this gap, we turn our attention to examining how parametric knowledge influences the generation process. Specifically, we focus on the feed-forward networks (FFNs) within Transformer-based L.MMs, which are widely recognized as key repositories of memorized knowledge [TO] 20). Indeed, our pilot study reveals that when a specific subset of mid-to-deep FFN layers exhibits excessive activation, the model tends to rely more heavily its internal knowledge, consequently producing unfaithful outputs.
Building on this observation, we propose Parametric Knowledge Muting through FFN Suppression (ParamMutee), a novel framework designed to enhance the contextual faithfulness of LLMs. Specif- ically, ParamMute first identifies the FFN layers most associated with unfaithful generation and suppresses their activation to mitigate the undue influence of internal knowledge. A balugand--lay knowledge preference calibration module is then applied to the suppressed L.M to further encourage reliance on external evidence, ultimately yielding more trustworthy responses.
Additionally, reliably evaluate LLM faithfulness, we introduce CoFaithfulQA, a comprehensive benchmark built from six open-domain QA datasets. It focuses on realistic scenarios where model responses may conflict with accurate retrieved evidence. Experimental results demonstrate that ParamMute consistently outperforms strong baselines on both CoFaithfulQA and the established ConFiQA benchmark (T. It improves faithfulness by an average of 6.17% and 54.63% on the two benchmarks, respectively, while substantially reducing reliance on parametric knowledge. These results highlight the importance of explicitly accounting for internal knowledge as a key step toward building more faithful and trustworthy language models.
2 Preliminaries: Understanding the Role of FFN in Unfaithful Generation
In this work, we aim to investigate the influence of internal knowledge on unfaithful generation. To explore this, we focus on feed-forward networks, which interpretability studies have identified as primary repositories of parametric knowledge (4] This makes them ideal targets for analyzing the role of internal knowledge in unfaithful generation. This section begins by outlining the foundational concepts of knowledge representation and neuron activation in LLMs. We then conduct an empirical analysis using FFN activation patterns as a proxy for internal knowledge utilization, aiming to investigate their correlation with unfaithful model outputs.
2.1 Background: FFNs Knowledge Carriers and Activation Analysis
Recent interpretabiility studies have shown that FFNs function similarly to key-value memory mechanisms, storing the majority of the parametric knowledge [20) through two parameter matrices K, V E Rdd.. xd where dm and d are the dimensions of the intermediate and input representations, respectively, For the i-th token in the input sequence, the FFN processes its representation æ; E Rd from the last layer through linear transformations. Formally, the computation in the 1-th FFN can be expressed key-value memory mechanism:
Feed-Forward Networks as Parametric Knowledge Stores.
FFN(=)) = (a(K' a())
(1)
where is the activation function. Geva et al. (20] further show that the FFN output can be expressed as a weighted sum over a set of value vectors:
den
FFN(x)) Σ Coaa k))o)
> Edy
(2)
where kt and oy denote the j-th row of K (the subkey) and the j-th column of V (the subvalue), respectively. The term dtj = a (æc
k)) represents the activation coefficient associated with the
neuron v/.. Following Mu et al. [7 we consider neuron activated when aly exceeds zero.
iitivation-basee Metric. Since each activated FFN neuron contributes independently to the final output 22 22), we can quantify the overall activation level through an activation ratio. For a token representation æ layer the activation ratio R2 (x/) at layer is defined as the fraction of neurons that are activated:
1 dim
R' (x/) = dm M
Hoigh
(3)

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Unfalthful response Falthful
04
PCC (Pearso Corrittion
0.3
5.8
Neuron Activtion
02
Average
5.2
1m
4,9
Control
Expermant
(a) Difference in Neuron Activation (btio:() Pearson Correlation: Neuron Activation (c) UA-FFNs suppres- Faithful Unfaithful.
Ratio Unfaithful Label.
sion increases NLL.
Figure 1: Activation Pattern Differences and Causal Impact on Unfaithfulness, (a) Activation ratio comparison between faithful and unfaithful generations. (b) Pearson correlation between unfaithfulness and FFN activation ratio, with UA-FFNs layers highlighted. (c) Suppressing UA-FFNS increases the Negative Log- Log-Likeld Loss (NLL) on unfaithful data, indicating a causal role.
where I[ayy] is an indicator function that returns if aly > O, and 0 otherwise. Intuitively, higher R (az]) indicates that more neurons in the FFN are actively participating in computing the output, reflecting a greater involvement of parametric knowledge stored in the FFN layer [15 20 (4]. We compute the response-level activation ratio by averaging the activation ratios over all tokens in the
response i = .rr)
T
(F(F) 1 Σ
En(rr (r))
(4)
2.2 Pilot Study: Are Certain FFNs Implicated Unfaithful Generation?
Building on the activation-based analysis framework introduced above, we now conduct empirical investigation into key hypothesis: Do unfaithful responses correspond to disproportionately high activation in certain FFN layers?
Dataset for Activation Analysis. fulQA, denoted as D, which consists of model-generatee responses annotated with binary faithfulness labels. These annotations enable direct comparison of activation patterns between faithful and un- faithful generations. Each instance (y, C, C. . Dyy E 'D includes an input query q. retrieved context e, a ground-truth answer y derived from the evidence e, a model-generated response f, and a binary label yf E {0, 1}. indicating whether F is faithful to the context (see Section 4 for construction and annotation details). For comparative analysis, we partition into a faithful subset D and an unfaithful subset D based on the faithfulness label yy. We then analyze the FFN activation patterns of the LLMMA3-8B-nsstrue model across the two groups to investigate how activation behavior differs between faithful and unfaithful generations.
To support this analysis, we use the proposed benchmark CoFaith-
Activation Differences Between Faithful and Unfaithful Responses. To quantitatively examine the relationship between FFN activation and response faithfulness, we compute the layer-wise activation ratio Re (f), as defined in Eq, 4 for both the unfaithful subset D and the faithful subset D+ We then define their difference as the activation gap, given by:
AR' = Ep- [t' (f) Ep- [RL (P))
(5)
As shown in Figure ((a) while most FFN layers exhibit minimal differences, we observe consistently higher activation in D within a narrow range of layers, particularly in the middle-to-deep transformer blocks. This pattern suggests that unfaithful generations may be associated with distinct activation behaviors concentrated in these specific layers (robust across diverse settings; see Appendix A..).
Correlation and Causal Analysis of FFN Activation for Unfaithful Generation. To statistically verify this association, we compute the Pearson Correlation Coefficient (PCC) between the activation ratio R' (f) and the unfaithfulness indicator (1 yf) across the dataset. As shown in Figure ((b) mid-
to-deep FFN layers exhibit increasingly positive correlations (p-value < 0.05), confirming a significant positive correlation between activation in these layers and unfaithful generation. This evidence Unfaithfulness- Associated FFNs (UA-FFNs plays central role in unfaithful generation. When these layers exhibit
supports our hypothesis that a specific subset of mid-to-deep FFN
3

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excessive activation. the model increasingly relies on internal parametric knowledge (as further evidenced in Appendix/A.13 overriding retrieved context and leading unfaithful outputs.
To examine whether the observed correlation reflects a causal relationship, we perform a causal intervention (17) by suppressing the activation of selected FFN layers. Specifically, we compare the Negative Log-Likelihood (NLL) loss between an experimental group (with suppressed UA-FFNs) and control group (using the vanilla model) the unfaithful subset D The detailed intervention procedures are described in Appendix A.3 As shown in Figure [[c] the experimental group exhibits consistently higher NLL than the control group, as expected indicating that suppression of UA- FFNs makes unfaithful responses harder to generate. These results provide causal evidence that the activation strength of UA-FFNs directly influences the likelihood unfaithful generation.
Summary and Implications. Our pilot study reveals that unfaithful generation in LLMs is associated with the over-reliance internal parametric knowledge through UA-FFNs. Motivated by this, ParamMute ($3) applies selective suppression to UA-FFNs activations to limit parametric knowledge expression and improve contextual faithfulness.
Methodology
In this section, we present Parametric Knowledge Muting through FFN Suppression (ParamMute), two-stage framework for improving the contextual faithfulness of LLMs. ParamMute first mitigates the influence parametric knowledge by suppressing the activation of UA-FFNs ($3.1) I. and then incorporates an adaptation module to promote reliance on external knowledge ($3.2.
3.1 Reducing Internal Knowledge Reliance via Activation Suppression
Our pilot study in Section 2.2 reveals that unfaithful responses tend to involve a greater degree of internal parametric knowledge within specific subset of FFNs (UA-FFNss. Motivated by this finding, we propose to suppress the activation of UA-FFNs, aiming to reduce the influence of internal knowledge and thereby enhance contextual faithfulness. Formally, for each layer I, compute the average activation ratio R' RF() on both the unfaithful subset D and the faithful subset D+ We then use the previously defined activation gap AR' (Eq. 5) to capture the difference FFN activation between unfaithful and faithful outputs. Finally, rank all layers L by their corresponding A/t' and select the top-N layers with the highest activation gaps for subsequent suppression:
Tsup = (1 E T. ranks Top-N of AR').
(6)
A suppression coefficient À E [0, 1 is introduced to reduce the activation of UA-FFNs. Accordingly, the original FFN computation (Eq, I reformulated as:
FFN Fx)) = (x o(Kaa))) V", ifl Lsup-
(7)
Setting À restores the model's original behavior. As À decreases, the contribution of the selected FFNs is progressively reduced. When À = 0, the suppressed FFNs are fully deactivated and no longer influence the model's 's output. This soft suppression mechanism enables fine tne-grained control over the contribution of internal parametric knowledge (see Appendix A.14 for a detailed experiment and analysis).
3.2 Knowledge-Augmented Adaptation through Preference Optimization
After suppression, we further incorporate plug-and-play adaptation module (24 to recalibrate the model's knowledge utilization preferences, enabling more effective usage of external evidence.
L = Σ LKAT + B LxPO.
(8)
where D denotes the set of all training instances, each comprising query q. retrieved context c, and a ground-truth answer y"; and a and 3 are hyperparameters that control the balance between the two objectives. The Knowledge-Augne Augmented Training (LxAt) and Knowledge Preference Optimization (Lxpo) objectives guide the suppressed model to both generate accurate answers and calibrate its knowledge usage preference towards externl knowledge.

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Knowledge-Angmenter Finetuning, Following Lin et al. [40,, we maximize the likelihood of generating the ground truth answer y" based on both query q and external knowledge c:
LKAT log P(y" 2, c),
(9)
This objective trains the suppressed model to leverage both internal and external knowledge to answer the question accurately.
Knowledge Preference Optimization. To further refine the model's reliance on external versus internal knowledge, we apply a max-margin loss [[]] to optimize the likelihood of generating ground truth answers that depend more on external knowledge:
LrP l7 log Ply" [g.c) + log Ply" 19)]+
(10)
where y is a margin parameter that controls the preference constraint, and the [+] function ensures non-negativity. This objective further finetunes the suppressed model to shift its reliance towards external knowledge, improving the reliability and faithfulness of its responses.
4 CoFaithfulQA: A Consistency-Flleere Contextual Faithfulness QA Dataset
In this section, we introduce Consistency-fiteree Contextual Faithfulness QA (CoFaithfulQA), a benchmark designed to evaluate the faithfulness of LLMs. and present the data collection pipeline along with the manual effort involved in construct- ing CoFaithfulQA.
Dataset
#Full*
#Faith.
#Unfaith.
HotporQA NewsQA
5,901
1,546
1,427
4.212
374
886
NQ
7,314
3,010
572
SearchQA
16,980
10,692
1,441
SQuAD
10,490
2,799
2,225
Characteristics of CoFaithfulQA. Evaluating contextual faithfulness requires scenarios in which external evidence should override model's incor- rect internal knowledge. However, prior work has primarily relied synthetic counterfactual con- texts that contradict known correct answers [43] While effective for controlled testing, such synthetic settings often fail to reflect the nat- urally occurring inconsistencies between retrieved evidence and model responses that commonly arise in real-world applications.
TriviaQA 7,785
5,887
767
Table I: Statistics of the CoFaithfulQA dataset. #Full denotes the number of deduplicated ex- amples from the original dataset. #Faith. and #Unfaith. indicate the number of samples la- beled as faithful and unfaithful, corresponding to D+ and D respectively.
45 57 65
Data Collection and Processing Pipeline. Our CoFaithfulQA is constructed from six widely-used open-domain QA datasets: Natural Questions (NQ) 33 SQuAD [11, NewsQA [60]. TriviaQA [I], SearchQA [3] and HotpotQA (69]. These datasets span diverse range of domains, question types, and reasoning requirements. collectively forming comprehensive evaluation testbed. Each QA triplet (g, c, y 'D consists of the query q, relevant evidence e and the ground truth y". Then the QA triplet is augmented as (g. c, y f, ys) with a model-generated response f and faithfulness label yy. based on which we form the subsets of CoFaithfulQA, (yı 0) and D (yy 1).
To facilitate the evaluation of LLM faithfulness, CoFaithfulQA is constructed to reflect realistic scenarios where models expected to rely accurate external evidence rather than incorrect parametric knowledge. Specifically, we employ a two-stage pipeline: we first extract the model's dominant parametric knowledge through self-consistency filtering, and then identify conflicts between this belief and retrieved evidence using multi-model verification. This procedure ensures that the resulting dataset captures genuine failures of contextual faithfulness.
Parametric Knowledge Elicitation. We adopt closed-book QA setup and apply a self-consistency mechanism [61] (4) to robustly capture the model's parametric knowledge. Specifically, prompt the model n times with the same query and designate the most frequently generated answer (i.c., the majority answer, denoted as f) its dominant belief. Queries for which the majority answer appears fewer than n/2 times are discarded to ensure consistency and reliability. Appendix A.7 provides evidence that higher self-consistency improves the quality of faithfulness assessment.
Conflict Detection. To identify whether the model's parametric knowledge contradicts the external evidence, we compare the dominant answer A with the ground-truth answer and the retrieved context. Two advanced pretrained LLMSs GPT-4o [49] and GL-M---puus [22] are
are used to assess whether conflict exists. To mitigate model-specific bias, only instances where both models agree the
5

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presence of a conflict are retained. Based on this judgment, we assign a faithfulness label By E {0, 1}. where y, 0 indicates that f conflicts with the context, and y = otherwise. Appendix [A.3 details the implementation procedure. Furthermore, we manually verify subset of the detected confticts to confirm their validity against human annotations (see Appendix (A.6).
5 Experimental Methodology
This section describes datasets, evaluation metrics, baselines and implementation details.
Datasets. We evaluate the contextual faithful generation performance of different models on the subset D of CoFaithfulQA, as Dt samples are already contextually faithful and thus less informative for evaluation. To ensure a comprehensive evaluation, we also include two out-of- benchmarks: ConFiQA [ and EvithEval 45]. ConFiQA assesses faithfulness in counterfactual scenarios across three Puasets-Question Answering, Multi-hop Reasoning, and Multi-Conflicts citi-Coonffitss-echhe containing 6,000 carefully constructed instances. Similarly, FaithEval tests a model's ability to prioritize the given text over its parametric knowledge.
Evaluation. Following Longpre et al. 433, we adopt a suite of metrics to evaluate the contextual faithfulness of model outputs. To ensure comparability, both generated responses and reference answers are normalized using the approach of Li et al. [3)] We report two primary metrics: context recall (ConR1), which reflects the degree to which the model's ' responses align with the provided external context, and memory recall (MemR(), which indicates reliance on the model's internal parametric knowledge. To further characterize the model's preference between these two sources, we also report the memorization ratio, defined as MR MonK++Conk which quantifies the model's relative tendency favor memorized content over retrieved evidence.
MemR
Baselines. We evaluate ParamMute against a range of competitive baselines, categorized into four groups: (1) Prompt-based approaches, including the attributed prompt pottp) and the combined opinion- and instruction based prompt (grmm))))) from Zhou et al. [79]; (2) becoding-based based methods, where select the representative COIECD 775,, which incorporates entropy-
eatropy-bbase based con- straints to perform context-aware contrastive decoding; (3) Fine-tuning methods, consisting of standard Supervised Fine-Tuning (SFT) and Knowledge Aware Fine-Tunine (KAFT) (37) KAFT enhances context faithfulness through counterfactual data augmentation; and (4) Alignment-based methods, including Con (C-DPO) []]. which applies the DPO framework (50) encourage context-grounder responses while penalizing reliance on parametric memory, and DDR (39), which incorporates differentiable data rewards train models better use contextual knowledge.
Implementation Details. To ensure a fair comparison, we use LLaMA3- --33-8---Instuut as the backbone model for all methods throughout suppressing N = UA-FFNI with a coefficient of 0.0 (Eq. [T]. Notably, this specific set of layers is kept consistent across all datasets. The hyperparameters a and 3, which balance LKAT and LKPO in Eq. [8] are both set to 0.5. Additional implementation details for our method and the baselines provided in Appendix AA.8 and A.9 Furthermore, report detailed failure case analysis in Appendix A. and results on different backbone models in Appendix A.16
experiments. Our configuration for ParamMute involves
6 Experiment Results
followed by a compre- In this section, we first present the overall performance of ParamMute (.6.Ip.
hensive ablation study ($6.2). detailed parameter sensitivity analysis ($6.3). and an investigation into how ParamMute calibrates the knowledge utilization of LLMs (66.2)
6.1 Main Results
This experiment evaluates ParamMute on CoFaithfulQA to assess its overall performance. Addition- ally, test ParamMute on the ConFiQA dataset, which represents out-of-domain setting.
As shown in Table 2 ParamMute significantly outperforms baseline models on CoFaithfulQA, demonstrating its effectiveness in generating more accurate and contextually faithful responses. Compared to the vanilla RAG model, ParamMute achieves an average improvement of 5% in ConR and reduces MemR by 4%, effectively mitigating the model's 's reliance parametric knowledge

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HotPotQA
NQ
NewsQA
Models
ConR f
MemR
MR
ConR 1
MemR
MR
ConR
MemR
MR
Vanilla-RAG
52
60.34
13.88
18.70
53.09
14.41
21.35
60.27
8.24
12.03
promp
19
58.93
13.95
19.13
55.36
11.07
16.67
58.80
7.56
11.39
O&t peompt
peompt
79
47.79
10.72
18.32
49.25
8.23
14.32
52.03
5.30
9.25
COIECD
75
62.51
12.19
16.32
56.21
12.28
17.93
51.81
6.21
10.70
SFT 63
70.92
6.24
8.08
59.76
10.29
14,69
61.96
5.08
7.58
KAFT 37
69.52
6.87
8.99
60.89
9.23
13.16
65.09
4.74
6.79
C-DPO 1
67.20
7.64
10.21
62.24
9.79
13.6
61.4
4.74
7.17
DDR
68.66
7.15
9.43
63.29
10.33
14.03
64.74
5.03
ParamMute
71.06
6.17
7.99
60.68
9.08
13.02
65.24
4.85
6.92
SearchQA
SQuAD
TriviaQA
Models
ConR
MemR
MR
ConR f
MemR
MR
ConR
MemR
MR 4
Vanilla-RAG
52
66.76
10.55
13.64
77.93
6.79
8.01
61.80
11.47
15.66
Attrpromp
79
10.55
14.43
77.35
7.62
59.97
10.43
14.81
O&
52.26
15.01
76.81
55.41
COIECD
75
69.74
11.66
14.32
73.12
7.64
9.46
63.62
11.99
15.86
SFT 63
75.29
6.87
8.36
79.19
4.22
5.06
59.6
8.34
12.38
KAFT
37
77.38
7.43
8.76
80.04
4.18
4.96
62.32
8.74
12.29
C-DPO [
64.12
5.62
8.06
80.08
5.26
6.16
58.67
8.74
12.96
DDR 39
78.07
6.48
7.66
81,36
4.75
5.52
60.71
7.73
11.29
PuramMute
78.76
6.04
7.12
80.58
4.04
4.78
60.89
6.91
10.19
Table 2: Performance on the CoFaithfulQA dataset. The highest scores are highlighted in bold, while
the sscond-highes scores are underlined.
Question Answering
Multi-hop Reasoning
Multi-Conflicts
Models
ConR 1
MemR
MR
ConR
MemR
MR
ConR
MemR 1
MR +
VanillaRRAG
52
26.24
38.51
59.47
14.87
24.98
4.49
13.53
prompe
79
47.33
25.78
35.26
17.69
22.42
55.90
6.60
14.67
68.97
O& grempi
66.22
13.69
17.13
16.78
17.18
50.59
11.64
12.60
51.97
COIECD 75
71.69
15.33
17.62
53.36
17.13
24.31
57.11
9.60
14.39
SFT 63
78.02
5.02
6.05
61.40
13.47
17.99
61.98
9.54
13.34
KAFT [37
82.04
5.58
6.36
63.71
13.64
17.63
67.31
9.98
12.91
C-DPO
81.82
6.20
7.04
58.89
14.00
19.21
58.24
8.71
13.01
DDR 39
80.71
6.07
6.99
60.64
15.60
20.46
61.07
8.93
12.76
PuramMute
81.20
3.69
4.35
63.09
12.82
16.89
65.20
9.29
12.47
Table 3: Performance of different models on the testing sets of ConFiQA
and encouraging better utilization of external context. The evaluation results also indicate that prompt-base based methods and decoding-basee approaches such as prumpp. O&I and COIECD decrease the MemR score, showing their effectiveness in reducing the model's reliance parametric knowledge. However, they also lead to a decline in answer correctness, as reflected by lower ConR score compared to the Vanilla RAG model. In contrast, fine-tuning-based approaches, such as SFT, KAFT, DPO, and DDR, enhance contextual faithfulness by adjusting the parameters of LLMs, highlighting the crucial role these parameters play in the emergence of knowledge conflicts within the models. ParamMute usually shows better performance than these fine-tuning based methods, which thrives its
mechanism.
To further evaluate the generalization capability of ParamMute, we tested it on the ConFiQA and FaithEval datasets. As shown by the results (Table ConFiQA and Appendix A.13 for FaithEval), ParamMute outperforms both prompt-based and fine-tuning methods in enhancing contextual faithful- ness and reducing reliance parametric knowledge. These improvements highlight the effectiveness of ParamMute in encouraging LLMs to prioritize contextual evidence over internal memorization, demonstrating its strong generalization ability.
6.2 Understanding ParamMute via Ablation and Component Analysis
We conduct ablation studies to analyze the effectiveness of ParamMute's suppression strategy and to evaluate the contributions of its key components. Specifically, compare suppression across
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different model sublayers, examine alternative FFN selection strategies, and assess the individual impact the suppression and adaptation modules.
Are FFNs the Primary Drivers of Unfaithful Generation? To assess the contribution of different transformer components to unfaithful generation, evaluate different suppression strategies. In addition to suppressing the UA-FFNs sublayers identified by ParamMute, we evaluate three alterna- tives: suppressing multi-head attention sub-layers (MHA), suppressing knowledge-related parameters (Parameter) [34. and suppressing entire transformer layers (Layer). All strategies share the same implementation setup, except for the specific component being suppressed. Technical details are provided in Appendix A.10 As shown in Table (rows 3), ParamMute yields the most significant improvements in contextual faithfulness. This suggests that FFN sublayers play a more central role in parametric knowledge recall than other components, consistent with prior findings that position FFNs as key repositories internal memory
10 21
Can Other FFNs Match the Effect of Those Se- lected by ParamMute? To assess whether al- ternative FFN selections can achieve similar im- provements in contextual faithfulness, ParamMute with several variants that suppress dif- ferent subsets of FFNs. Specifically, we experiment with suppressing FFNs in bottom layers, mid lay- ers, and randomly selected layers, detailed in Table 4 (rows 8-11). Our results show that sup- pressing bottom-layer FFNs leads substantial drop in ConR, indicating poor contextual ground- ing. Mid-layer and randomly selected FFNs sup- pressing methods yield moderately better perfor- mance, but still underperform ParamMute. These findings highlight the crucial role of the FFNs iden- tified by ParamMute, underscoring their effective- ness in mitigating parametric knowledge reliance and improving contextual faithfulness.
Method
ConR
MemR 4
MR
Suppressed Component Selection
compare
FFN
69.54
6.18
8.34
MHA
68.35
6.81
9.23
Layer
62.52
6.03
8.92
Parameter
68.71
6.67
8.85
Suppressed Layer Selection
UA-FFNs
69.54
6,18
8.34
Bottom
62.29
7.15
10,38
Middle
67.11
6.85
9.43
Random
67.65
7.20
9.84
Faithful Enhuncement Strategies
ParamMute
69.54
6.18
8.34
w/o Suppression
69.47
7.01
9.34
w/o Adaption 68.57
6.32
8.58
Table 4: Comparison of suppression strategies ParamMute, covering component-level and layer-level variants, along with ablation studies on suppression and adaptation components.
Contributions of Different Components of PatamMute. As shown in Table (rows 13-15), we compare ParamMute with two ablated variants:
ParamMute w/o Suppression and ParamMute w/o Adaptation, in order to examine the contributions of each component. Removing the suppression module results in increase of approximately 0.8% MemR, suggesting that suppressing activation is effective reducing reliance on parametric knowledge. In contrast, removing the Adaptation module leads to 1 1% drop in ConR. highlighting its role in promoting better use external context. These findings confirm the effectiveness of ParamMute in reducing the dependence of LLMs on internal memory for faithful generation.
-O-
Conk
70
ConR
70
Cank
Metrics (%)
70
Metrics (%))
60
-O-
MemR
Metrics (%)
-O- MemR
50
69
69
40
0.25 0.5 0,75 30
68
0.0
101112131415
0.5:0.51:2
51:2
2:1
1:3
1:5
1:10
Value of À
Value N
Value a:
(a) Suppression Coefficient A.
(b) Top-N FFN for Suppression.
(c) Loss Weight a
Figure 2: Variation in ConR and MemR under different hyperparameter settings. Each point reflects the average metric across all subsets within CoFaithfulQA. Higher ConR and lower MemR indicate better contextual faithfulness with reduced parametric reliance.
6.3 Impact of Key Hyperparameters in ParamMute
To investigate the impact of key hyperparameters ParamMute, we conduct a sensitivity analysis. The experimental setup remains identical to main implementation, varying only the specific

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hyperparameter under investigation. Specifically, we investigate three factors: (1) the suppression coefficient X, which controls the strength of activation suppression applied to selected FFNs; (2) the number of top-N FFNs selected for suppression; and (3) the weighting coefficients o and |3 used to balance the LKAT and LxPO during training. The results are presented in Figure
Suppression Coeflicient A. We vary À E [0.0, 1.0] to analyze the impact of suppression strength applied to UA-FENs activations, where to the original model without intervention. The same value for À is used consistently during both training and inference. As shown in Figure 2(a) decreasing consistently reduces MemR and improves ConR, indicating that smaller À values lead to better contextual faithfulness and reduced reliance on internal memory. At À 0.0, the model achieves the best overall performance. Given its strong effect in promoting contextual faithfulness, we adopt À = 0.0 as the default setting in all experiments unless otherwise specified.
0.0 denotes full suppression and À - 1.0 corresponds
The Number of Suppressed FFNs (N). We investigate how the number of top-activated FFNs selected for suppression affects the model's behavior. Specifically, we vary N from I 15, covering nearly half of all FFN layers. As shown in Figure 2 increasing N expands the suppression scope and consistently reduces MemR. However, when N reaches 10, we observe sharp drop in ConR, suggesting that excessive suppression may interfere with functions beyond knowledge storage. This indicates that not all FFN layers are suppressible without adverse effects, and overly broad suppression can impair the model's 's ability to utilize external context.
Loss Balancing Coefficients a and B. During joint training, Augmented Training (Lxxt) and Knowledge Preference Optimization (Lxpo), respectively. We empirically test different ratios of a : B and find that varying this ratio has limited impact on overall performance. Nonetheless, moderate weighting (e.g., = 0.5, 8 0.5) achieves a good balance between suppressing parametric interference and maintaining task accuracy (see Figure 2(c)
and 33 weight Knowledge-
1.0
1.00
1.0
0.91
Inf
10a
46.89
Similarity
Eimilard
0.81
0.8
PPL
261.89
PPL
0.6
0.63
0.49
0.59
0.6-
D.58
0.63
10a
10
7.35
0.4
0.4
28.44
1.62
Vandia
Venlie Wille-AAG Sup
Vin VaG
Suspressed Par Syramblls
Yunfia
supphetind Parorslltte
Numile
Suppressed, PacurnMtee
(a) Response Similarity (b) Response Similarity (c) Perplexity w/o Context. (d) Perplexity Context. with Parametric Answer. with Contextual Answer.
Figure 3: Evaluation of knowledge utilization of different models. We assess the response similarity with parametric answer and contextual answer (Figure 3(a) and Figure (b)), and compute the PPL score when reproducing the ground truth answer (Figure 3(c) and Figure 3(d)p. The Suppressed model refers to ParamMute w/o Adaption, which only incorporates the knowledge suppression.
6.4 Effectiveness of ParamMute in Calibrating Knowledge Usage Behavior
To assess whether ParamMute improves contextual faithfulness by guiding LLMs to favor retrieved evidence over incorrect internal knowledge, conduct comparative analysis D= the unfaithful subset of CoFaithfulQA. We compare the performance three models: the vanilla LLM, the Suppressed model (ParamMute w/o Adaptation), and our ParamMute.
We first evaluate the model's knowledge usage preference by computing the semantic similarity between its outputs and two reference answers: (1) the parametric answer r., representing the model's internal belief obtained in a closed-book setting (Section 4. and (2) the contextual answer y" derived from retrieved evidence. As shown in Figure 3(a) the Suppressed model achieves the lowest similarity to parametric answers, indicating that activation suppression effectively weakens reliance on internal knowledge. Meanwhile, Figure 3(b) shows that ParamMute achieves the highest similarity with contextual answers, indicating the effectiveness of ParamMute in enhancing the context knowledge usage ability of LLMs by using plug-and- and-play knowledge adaptation module.
To further assess knowledge calibration, we measure the perplexity (PPL.) of each model when reproducing the ground-truth answer, both with and without contextual input. A lower PPL. indicates greater confidence in generating the correct response. Figure 3(c] shows that when no context provided, the Suppressed model exhibits higher PPL, confirming its effectiveness in reducing the

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dependence on parametric memory. Alternatielyy, ParamMute displays extremely high PPL in the absence of context but significantly lower PPL when context available (Figure ((d)). confirming that the model has shifted to reliance primarily on retrieved evidence instead of the parametric knowledge.
7 Related work
Despite considerable advancements of Retrieval-Augmented Generation (RAG) models [22] 56 770,, unfaithful generation [25] [25]-where models produce content that not supported by, or even contradicts, the retrieved external evidence- remanns a critical and persistent challenge. Even when supplied with accurate and relevant external knowledge, RAG models frequently prioritize their internal parametric knowledge over retrieved information, leading to unfaithful outputs and diminishing the reliability of such systems [ 6 73 65]. Thus, the demand for contextually faithful
LLMs has significantly increased, particularly within RAG applications [4 36 [5].
Numerous studies have systematically investigated this phenomenon from both evaluation and analyt- ical perspectives. For instance, certain research constructs synthetic scenarios by manually replacing entities in retrieved passages, highlighting the propensity of L.MMs to generate responses aligned
with their internal knowledge rather than provided external evidence [29] 4 45 Other studies
demonstrate that LLMs often opt for contextually plausible but internally memorized information
when faced with conflicting sources, underscoring the difficulty of overcoming ingrained parametric knowledge biases 3 65]. Additionally, Jin et al. (30] identifies separate context and memory attention heads, which respectively attend to external and internal sources of information, offering more granular view into the mechanisms that underlic unfaithful generation. Complementarily. Sun et al. (58) suggest that certain FFNs within LLMs act as knowledge injectors, amplifying the influence of internal memory within the residual stream and thereby contributing to unfaithful generation.
Efforts to improve contextual faithfulness primarily focus on enhancing external knowledge in- tegration through various strategies. One direction focuses on prompt design to guide models toward context-grounder responses [62 79). Another approach encompasses fine-tuning LLMs on degeeegg--augeeeeeed datasets, reinforcing the model's preference for retrieved information over internal memory [16
37 46 48) Alignment techniques have also been explored, aiming encourage
external grounding while suppressing dependence on internal parametric knowledge [1 [3]]. More- over, contrastive decoding methods have been proposed, explicitly differentiating between faithful and hallucinated responses to promote alignment with external evidence during generation [2) (5,_5_2)
Beyond external interventions, prior work [30 58 has also highlighted the role of internal compo- nents such as FFNs in shaping model behavior. Building on this, our work analyzes FFN activation patterns to identify over-active layers strongly correlated with unfaithful outputs. We propose suppression- based strategy to reduce their influence and enhance contextual grounding.
8 Conclusion
In this paper, we introduce ParamMute, a novel framework designed to enhance the contextual faithfulness of LLMs. Our approach addresses the persistent challenge of LLMs favoring inter- nal parametric knowledge over retrieved evidence. ParamMute first mitigates this over-reliance by strategically suppressing the activation of specific FFNs that exhibit strong correlation with unfaithful generation. To further promote adherence to external information, ParamMute incorporates a alug-and-plas adaptation module that reinforces the model's grounding in the retrieved content. Additionally, we introduce CoFaithfulQA, a comprehensive benchmark constructed from six diverse QA datasets, enabling controlled evaluation of faithfulness under conflicting knowledge settings. Extensive experiments on CoFaithfulQA and ConFiQA demonstrate that ParamMute significantly enhances generation faithfulness while substantially mitigating dependence on internal knowledge.
Acknowledgment
This work is partly supported by the National Natural Science Foundation of China (No. 62206042 and No. 624611 146205), CCF-zhipu Large Model Innovation Fund (No. 202403), and the Fundamental Research Funds for the Central Universities (No. N257ILL045). This work is also supported by the A19Stars community.
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A Appendix
A.1 License
We are committed to ethical research practices and ensuring the reproducibility of our work. To this end, we present the licensing information for all datasets utilized in our experiments. These include Natural Questions (CC BY-SA 3.0). NewsQA (MIT License), SearchQA (Apache License 2.0). TriviaQA (Apache License 2.0), HotporQA (CC BY-SA 4.0), and SQuAD (CC BY-SA 4.0). Our use of these datasets in full compliance with their terms, as all aforementioned licenses expressly permit their application in academic research.
A.2 Ethics Statement
Our data construction process involves prompting L.MSs to elicit their internal parametric knowledge in order to investigate the underlying causes of hallucinations in generated outputs. While this approach enables targeted analysis of model behavior, may lead to the generation of inaccurate or hallucinated contents. To ensure the responsible usage, we strictly limit the distribution of the resulting dataset to academic research purposes. The dataset does not contain any personally identifiable information or offensive material, and all contents are curated in accordance with ethical guidelines for responsible Al research and data sharing.
Additionally, we conducted human evaluations to assess the reliability of the LLMs in identifying knowledge conflicts. Evaluation data was carefully distributed human evaluators solely for research purposes, ensuring it adheres to ethical standards and contains no content that violates these standards. We also recognize that the capacity suppress model's knowledge raises important ethical concerns about its potential for misuse, such as obscuring facts or entrenching bias. Our work explores this mechanism purely as a means to improve contextual faithfulness and mitigate hallucinations. We stress that the transition of such capabilities from academic research to real-world deployment would require rigorous oversight and transparent governance to ensure responsible use.
A.3 Causal Intervention on UA-FFNs Activation
To establish the causal role of UA-FFNs activa- tion in unfaithful generation, perform interven- tion experiments by manipulating the activation strength of the seatthffulness-Asoccittttt FFNs (UA-FFNs). These FFNs are identified in Sec- tion 2 exhibiting strong correlations with un- faithful outputs. Our goal is to examine whether suppressing or enhancing their activation causally affects the faithfulness of the model's generation.
5.6
Average NLL
Average NLL
- Average NLL
Average
5.3
4.0
Control
Control
5.0
3.7
0.00
0.25 50
9,75..00
1.25
o.
15.000000 1.25
Value of A
Value A
(a) Unfaithful subset D
(b) Faithful subset D
Figure 4: Average NLL loss under different FFN activation scales (A) for an unfaithful subset D and a faithful subset D
Intervention Setup. We conduct our interven- tion experiments on the CoFaithfulQA using the LLaMA3-8B-Imm Instrucct model. Each instance (q. c, y", f.31)) D is labeled as faithful (yy=1) or unfaithful (yy=0), allowing us to partition the data into D and D for subsequent analysis. To modulate the influence of parametric knowledge, we apply scaling factor À to the output of the selected UA-FFNs layers:
UA-FFN' (a!) (A.o(KKa))) v'.
(11)
Here, À controls the activation of each UA-FFNs layer: when À 1, the contribution of parametric knowledge is suppressed; when À > 1, is amplified. To evaluate the model's sensitivity to such interventions, we vary À across {0.0, 0.25, 0.5, 0. 75, 1.0, 1.25]. The unmodified model with À 1.0 serves as the control group, while all other settings constitute the experimental group.
We evaluate the effect of suppression on model behavior by computing the average negative log-likelihood (NLL) loss over two disjoint subsets of the dataset: the faithful subset D and the unfaithful subset D= For each setting of the suppression coefficient À E [0.0. 1.0), we measure the model's NLL loss separately on both subsets. The suppression is applied UA-FFNs with varying X, where x= 0.0 denotes full suppression and À = 1.0 corresponds to suppression.
Evaluation Protocol.
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Results. Figure 4 summarizes the model behavior across range of suppression coefficients A. The endpoints, X 0.0 (full suppression) and À 1.0 (no suppression). correspond to the intervention and control settings introduced earlier in Figure [[c] (Section 2.2).
When evaluated on the unfaithful subset D the NIL increases monotonically as À decreases, with the highest value observed under full suppression (A = 0.0). This trend indicates that suppressing UA- FFNs activation effectively disrupts the model's ability to generate unfaithful responses, suggesting that these FFNs play a functional role in f'ciliittting hallucinated content. Meanwhile, on the faithful subset D the NLL also decreases as À decreases. This trend suggests that suppressing UA-FFNs activation not only avoids harming faithful generation, but may even improve it. A possible explanation that reducing reliance on parametric knowledge encourages the model to more effectively utilize the retrieved context, resulting in more faithful and confident responses. To further validate this trend, we increase the suppression coefficient to À = 1 1.25, thereby amplifying the activation of UA-FFNs. As shown in Figure|4] this leads to a decrease in NLL on the unfaithful subset D and moderate increase on the faithful subset D+ These findings further confirm that enhanced activation of UA-FFNs facilitates unfaithful generation.
Results. Figure 4 summarizes the model behavior across range of À values. The endpoints-A 0.0 (full suppression) and À = 1.0 (no ppppression--corrspppoo to the intervention and control settings shown earlier Figure [ (Section 2.2 To better understand the effect of suppression strength, we examine model performance on the two subsets separately. For the unfaithful subset D we observe a consistent increase in NLL loss as decreases, with a peak at = 0.0. This monotonic trend confirms that suppressing UA-FFNs activation disrupts the model's ability to produce hallucinated content, implying that these FFNs play functional role in facilitating unfaithful generation. In contrast, the loss on the faithful subset D+ shows only a mild increase as À decreases, indicating that UA-FFNs contributes little to the generation when the model relies on retrieved context.
Conclusion. These results provide strong causal evidence that the over-activation of UA-FENs drives unfaithful generation by injecting parametric knowledge into the output. By suppressing these layers, the model becomes less confident in producing hallucinated content, as reflected in the increased loss on This confirms that internal memory representations in LLMs-particun particullly within specific FFNs-are not merely correlated with unfaithful generation, but actively responsible for their emergence.
Dataset
Full Size*
Consistency
Faithful Subset (D+)
Unfaithful Subset (D
HotpotQA
5,901
2,973 (50%)
1,546 (26%)
1,427 (24%)
NewsQA
4,212
1,260 (30%)
374 (9%)
886 (21%)
NQ
7,314
4,419 (60%)
3,010 (41%)
1,409 (19%)
SearchQA
16,980
12.133 (71%)
10.692 (63%)
1,441 (8%)
SQuAD
10,490
5,024 (48%)
2,799 (27%)
2,225 (21%)
TriviaQA
7,785
6,654 (85%)
5,887 (75%)
767 (10%)
Table 5: Number of instances at each stage in the CoFaithfulQA construction pipeline.
A.4
Robustness of UA-FFN Patterns Across Diverse Settings
To test the generalizability of our finding-that excessive activation in specific subset of mid- to-deep FFN layers causes the model to rely more on internal knowledge and produce unfaithful outputs-wwe we extended our Pilot Study (8 2.2) to different datasets, larger models, and models from different families. Specifically, to verify our finding's ' independence from the dataset, we evaluated M-MMA-3-8B-Instruce HotpotQA and SQuAD. The results, shown in Figure 5(a) and Figure 5(b) combined with our initial findings on CoFaithfulQA (Figure I(a)). confirm that our observation is datasct-agnostic. Furthermore, to assess gencralizability across model scale and architecture, we experimented with Owwn-225-33B--mmnn Instruct and and Figure A.3) revealed the same activation patterns identified in our pilot study.
The outcomes (Figure A.3
The results consistently demonstrate that our key finding is robust: excessive activation in a specific subset of mid-to-deep FFN layers (typically 60%-85% of the model depth) is strongly associated with unfaithful outputs, regardless of dataset, model family, or size. This not only highlights the generality of our observation but also provides valuable insights into the mechanisms underlying
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Neuron Activation Ratio
0.6
Unfaithful response
Neuron Activation Ratio
0.6
Unfalthful response
0.5
Faithful response
0.5-
Faithful response
AR
AR
0.4
0.3
0.2
0.2
0.1
0.1
0.1
10
15 Layers
20
25
30
5
10
15
20
25
30
Layers
(a) Difference in Neuron Activation Ratio
Hot (b) Difference in Neuron Activation Ratio on TriviaQA.
potQA.
Figure 5: Comparison of neuron activation ratios between faithful and unfaithful generations HotpotQA and SQuAD, evaluated with LLaMA-3-88 10-8B-Innttcct
Neuron Activation Ratio
Unfaithful response
0.3
Faithful response
AR
0.2
0.1
0-
-0.1
10
20
40
50
Layers
Figure 6: Comparison of neuron activation ratios between faithful and unfaithful generations on Qwen-2.5-32B-Insttuue
Neuron Activation Ratio
Unfalthful response
0.6
Falthful response
AR!
0.4
0.2
0.0
0
10
20
30
40
50
60
70
80
Layers
Figure 7: Comparison of neuron activation ratios between faithful and unfaithful generations on MLMMA-3--0B-Instrus
unfaithful generation and the role of parametric knowledge in LLMs. We hope these findings will inform future research.
A.5 Details of CoFaithfulQA Construction
In this section, we detail the two main steps in constructing CoFaithfulQA.
Parametric Knowledge Elicitation. To elicit the LLM's parametric knowledge, we prompt the model a closed-book setting i.e., without providing any external context. To improve the reliability
of the elicited responses, adopt consistency-base based filtering strategy [68 72). For each query
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the model is prompted = 5 times, yielding a set of responses f2......... .... majority response f as the one that appears most frequently. query q: is retained if and only if the frequency of i is at least 3 (i.e., appears in 2 3 out of 5 responses), thereby filtering out inconsistent generations and ensuring the reliability of the extracted parametric knowledge.
. We identify the
The following prompt template is used to elicit responses from the model:
Prompt for Eliciting Parametric Knowledge
Answer the question (brevityjinstructinn and provide supporting evidence. Question: (question)
The
is incorporated to encourage the LLM to produce more concise responses, following the guidance strategy proposed by Kortukov al. 332
Conflict Detection. Next, we categorize each instance obtained from the previous step into one of groups-conflicting or non-conflicting-ee based on whether the model's parametric knowledge aligns with the retrieved context. To assess the presence of conflict, we employ LLMs to compare the parametric answer and the contextual evidence.
mitigate model-specific bias, we adopt dual-model agreement strategy: conflict label is only assigned when both GPT-4o [49] and
GLM-4-plus [22 agree on its presence. For both models, we use the following prompt:
Prompt for Identifying Conflict Knowledge
You tasked with evaluating the correctness of model-generated based the given information.
Context: [contexr]
Question: [question]
Contextual Answer: (contextual answer)
Mooell-Genertter Answer: Modeel-Genevaeed_annee
(Detailed task description...]
Output Format:
Evaluate result: /Correct/ / Partially Correct Incorrect)
Based on this process, we assign each instance an additional binary label yf indicating faithfulness: Vf = O (unfaithful) if the parametric knowledge conflicts with the context, and y/ = 1 (faithful) otherwise. The unfaithful subset D is used for downstream evaluation experiments, while the faithful subset D+ is used for activation analysis.
A.6 Assessing the Reliability of LLMs in Knowledge Conflict Identification
In this subsection, we conduct a human evaluation to as- sess the reliability of GPT-4o and GLM-4-plus in identi- fying knowledge conflicts. This evaluation aims to verify whether LLMs can serve trustworthy tools for auto- matically detecting conflicts between different knowledge sources, critical step in our data construction pipeline.
Subset
Agreement (%)
HotpotQA NewsQA
91.3
NQ
89.2
SearchQA
94.6
SQuAD
87.5
To ensure broad coverage, we randomly sample 150 in- stances from each of the six subsets of CoFaithfulQA. resulting a total of 900 examples that span diverse query types and conflict scenarios. Among them, 100 instances are randomly selected and independently annotated by multiple annotators to compute inter-annotator agreement (IAA). The annotations are conducted by six senior re- searchers (each holding at least bachelor's degree) with backgrounds in computational linguistics and LLM behavior analysis, ensuring high-quality and consistent evaluations.
TriviaQA
90.3
Average
90.4
Table 6: Agreement between human an- notators and LLMs across different sub- sets of our CoFaithfulQA benchmark.
For each instance, annotators are provided with the question, the contextual answer, the model- generated response, and the corresponding supporting evidence. Unlike binary classification ap- proaches (e.g., NI.I-bssed models), we adopt more fine-grained evaluation protocol. Annotators are asked classify each response into one of three categories: No Conflict, Somewhat Conflict, or High Conflict. The detailed annotation instructions are follows:
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Annotation Instruction
You tasked with determining whether the parametric knowledge of LLMs conflicts with the given context to facilitate the study of knowledge conflicts in large language models. Each data instance contains the following fields:
Question: (question]
Answers: (answers)
Context: {context)
Parametric knowledge: (LLMs' parametric carametric_knowledge The annotation process consists of two steps.
Step 1: Compare the model-generated answer with the ground truth answers, based on the given question and context, determine whether the model's parametric knowledge conflicts with the context.
Step 2: Classify the results into one of three categories: (No Conflict) if the model-generated answer is consistent with the ground truth answers and context, (Somewhat Conflict) if it is partially inconsistent
(High Conffict) if it significantly contradicts the ground truth answers context.
To ensure annotators fully understand the task, we first instruct them using of five gold-standard examples. Additionally, annotators had access to clarification support throughout the annotation process. We observe strong annotation consistency, with Cohen's 's к of 0.766 between human annotators, indicating substantial inter-annotator agreement (9, [6]. Table
the agreement rate between human annotators and LLMs across different subsets. LLMs achieves an average agreement of 90.4% with human judgments, demonstrating strong alignment with expert evaluations. Notably, the majority of disagreement cases occur in borderline Somewhat Conflict instances, suggesting that LLMs is particularly reliable in identifying clear-cut conflict or non-conflict cases. These results support the use of LLMs as practical and effective tools for scalable conflict identification.
A.7 Self-Consistency Filtering for Reliable Parametric Knowledge Extraction
In this subsection, we assess the effectiveness of our self- consistency-bant filtering method in extracting reliable paramet- ric knowledge from LLMs. The core idea is to filter out unstable model beliefs by leveraging generation consistency: for each query, we prompt the model five times and identify the most fre- quent answer and its occurrence frequency. Queries with low an- swer frequency likely reflect uncertain or non-committal model behavior, making them unreliable for evaluating the model's s true reliance internal knowledge. To quantify this effect, we group data into sub-datasets based on answer frequency, and apply our "Conflict Detection" method to retain only instances where knowledge conflicts are detected. We then evaluate ConR and MemR each sub-dataset.
ConR
Metrics (%)
68
MemR
15
64
10
Frequency
Figure 8: Performance compari- son of ConR and MemR across sub-datasets grouped by the an- swer frequency of LLMs.
As shown in Figure & clear trend emerges: as answer frequency increases, ConR decreases while MemR increases. This suggests that when the model becomes more consistent in its responses, also tends to rely more heavily on internal (parametric) knowledge, leading to a higher rate of unfaithful generation. Conversely, instances with an answer frequency of exhibit minimal reliance on parametric knowledge (MemR 3%), indicating that their apparent faithfulness may result from the model's uncertainty rather than true contextual alignment.
These results validate the importance of consistency- based filtering: only when the model confidently expresses its parametric knowledge can we meaningfully assess and intervene in cases of unfaithful generation. This approach also distinguishes our methodology from prior studies [43 65], which do not account for the stability of model beliefs.
A.8 Additional Experimental Details
This subsection describes the training prompt, training data, and experimental setup for our study. Prompts. For all methods except Attfprompt and Iproppt, we use simple QA-format prompt template, following Zhou et al. [79].
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Base Prompt
(context) Q: (question)
? A: {answer).
Training Datasets. During the training stage of ParamMute, we construct the training data by randomly sampling 32,580 instances from the combined training sets of the six sub-datasets included in our benchmark, all of which are derived from the MRQA 2019 benchmark [88.
Experimental Setup. In this work, all models are trained for 2, 100 steps with a total batch size of 32 and learning rate of le-4. To enhance training efficiency,
implement ParamMute with LoRA [24]. For ParamMute, we set the number of suppressed UA-FFNs layers to = 8, and the suppression coefficient in Eq. [ fixed at 0.0. The hyperparameters a and 3, which control the relative contributions of LKAT and Cxpo in Eq S are both set to 0.5. Additionally, we adopt dynamic 7 in LxPo (Eq. TOP, which linearly transitions from an initial margin (7o 1) to a final margin (-* 5) as training progresses. This adaptive strategy gradually reduces the model's 's reliance on internal parametric knowledge, encouraging
rely more on external knowledge. To facilitate faithful evaluation on CoFaithfulQA, we adopt controlled setting for each dataset-followine prior works 29 58 to ensure that the provided documents are sufficient to answer the questions, thereby isolating the model's faithfulness from retrieval quality.
A.9 Implementation Details of Baselines
This subsection describes the implementation details of all baseline methods.
We adopt two prompt-based baselines designed to reflect common prompting strategies: the attributed prompt (Atttprompp) which directly asks the model to state factual knowledge, and the opinion-and- instruction prompt promp))))) which combines subjective framing with task-oriented instructions. The corresponding prompt templates are shown below:
Attr based prompt
(context)
Q: (question) based on the given text? A: [answer).
O&I based prompt
Bob said )(contexi)" Q: (question) in Bob's opinion? A: (answer).
For the SFT baseline, we incorporate context during training, similar to ParamMute, while keeping the remaining experimental settings identical. To construct preference pairs for DPO training, we use contextually aligned answers from the dataset as "preferred responses" to ensure the consistency with the provided context. The "rejected responses" are generated by identifying parametric knowledge conflicts through our data construction methodology ($4). For KAFT, we employ hybrid dataset containing both counterfactual and factual data. Specifically, we integrate the counterfactual data developed by Xie et al. [65]. leveraging their advanced data construction framework. For DDR, follow the strategy described in Li et al. [39] to construct preference data. Specifically. for each training instance, generate multiple outputs under different decoding conditions by varying the sampling temperature and enabling or disabling the use of retrieved context. Each output evaluated using accuracy- accuracy-based reward function. The responses with the highest and lowest reward scores are selected as the positive and negative samples, respectively, for DPO training.
By maintaining equivalent dataset size and ensuring comparable data quality across all baselines, we provide a rigorous and fair comparison with our proposed ParamMute.
A.10 Implementation Details of Different Suppression Strategies
This subsection provides implementation details of four suppression strategies designed to reduce the influence of specific model components. These strategies are introduced to investigate how different types of internal suppression affect contextual faithfulness. All methods are applied to the same set of layers identified using the approach in Section 3.1 and implemented on shared model backbone -LLMMA-8B--nnstree to ensure fair comparison. For consistency, we use a uniform suppression coefficient of X 0.0, effectively nullifying the contribution of the targeted submodules.
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FFN Suppression (ParamMute). We identify a fixed set of unfaithfuuness--ssonn associited FFN sublayers (as described in Section 2.2) and suppress them by scaling the hidden activations after the nonlinearity with suppression coeffccient À 0.0 in Eq.
Multi-Head Attention (MHA) Suppression. For Multi-Head Attention suppression, target the same transformer blocks selected for the ParamMute setting and suppress the MHA modules within these layers by scaling their output by a factor of X.
Parameter Suppression Inspired by SNIP [d we explored a more fine-grained suppression strategy targeting individual parameters crucial for faithfulness. To achieve this, we followed the SNIP criterion to compute saliency score for each parameter within the identified FFN layers, defining the score as the product of the parameter's 's value and the gradient of the loss with respect to that parameter. We then select the top-k parameters with the highest saliency scores, where is set to match the total number of parameters suppressed in FFN suppression strategy. These parameters are suppressed by applying binary mask matrix scaled by the suppression coefficient X, effectively modulating their contribution without altering the remaining model weights. This setup aligns the overall suppression magnitude with that of FFN suppression, allowing for a more consistent comparison between strategies.
Layer Suppression. We apply suppression to the same set of transformer blocks used in the FFN suppression strategy. For each selected block, scale the output of the entire compricnn both the multi-heat head attention and FFN submodules- by the suppression coefficient À during inference. This allows us assess the impact of suppressing entire transformer layers while keeping the number and location of suppressed blocks consistent across strategies.
A.11 Failure Case Analysis
To better understand the task's challenges and our model's limitations, we conducted failure case analysis. Specifically, following the methodology of Kortukov et al. [32, we manually analyzed 50 incorrect predictions made by ParamMute on the CoFaithfulQA dataset and grouped them into five distinct error types, as detailed Table 7
Error Typc
Ratio
Partial Match Annotation Error
48%
20%
Context Ambiguity Hallucination Parametric Hallucination
6%
4%
22%
Among these errors, we identified 11 instances where the model reverted to its own parametric knowledge instead of using the provided context. These cases typically involved factual in- formation such as dates, counts, or specific entities (c.g., people, locations) tied events. We hypothesize that this occurs when the model has memorized these facts from its training data, leading to overly confident predictions. To investigate this, we generated five outputs for each of the 11 corresponding questions. In 8 of these 11 cases, the model produced the exact same incorrect answer across all five generations, confirming its high confidence. This suggests that while suppressing knowledge pathways is effective, future advancements may require mechanisms that can dynami- cally arbitrate between internal knowledge and external evidence, especially cases of high model confidence.
Table 7: Distribution of error types.
HotpotQA
Natural-Questions
NewsQA
Models
ConR
MemR
MR
EM
ConR 1
MemR
MR J
EM
ConR
MemR
MR L
EM f
Vanilla-RAG
56.90
14.51
20.31
11.63
45.64
19.23
29.65
2.48
57.34
9.14
13.75
3.61
Attrgnout
51.37
14.72
22.27
2.45
44.00
16.89
27.74
0.00
56.88
7.34
11.42
0.68
O&T
42.05
11.98
22.18
1.61
41.38
10.01
19.48
0.07
48.53
5.98
10.97
0.45
SFT
7.55
64.33
58.41
10.24
14.92
$7.98
64.02
8.08
53.50
KAFT
68.75
6.87
9.08
64.75
60.89
10.50
14.71
60.11
65.35
5.37
7.59
56.21
ParamMute
6.63
65.87
61.60
9.59
13.47
60.82
67.83
4.99
6.85
57.11
SearchQA
SQUAD
TrivisQA
Models
ConR
MemR
MR
EM 1
ConR
MemR
MR J
EM 1
ConR
MemR I
MR 1
EM 1
Vanilla-RAG
9.92
70.52
9.30
4.81
64.93
13.24
15.38
Attrgronge
65.44
10.41
13.72
11.87
69.39
9.08
11.57
0.94
60.63
9.52
13.57
3.91
9.30
2.71
65.89
6.83
9.39
0.36
13.11
0.00
SFT
78.94
6.94
8.08
76.27
80.44
4,09
4.84
70.74
61 15
8.47
12.17
55.93
KAFT
7.56
76.47
80.27
4.18
71.78
60.23
9.13
13.16
55.80
MaramMute
80,71
6.04
6.96
77.45
81.62
4.00
4.67
71.82
62.84
6.78
9.74
$7.24
Table 8: Performance of different methods on CoPaithfulQA under the noisy retrieval setting.
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A.12 Generalization to Noisy Retrieval Settings
In our main results ($ 6 we follow recent RAG faithfulness literature [ by adopting a controlled setting where a single retrieved document is guaranteed to contain the answer. This setup is necessary, as faithfulness can only be meaningfully evaluated when the answer present the provided context. However, retrieval in real-world applications is typically noisy. To broaden our evaluation and better assess the performance of ParamMute in realistic scenarios, we evaluate our method under a noisy retrieval setting. Specifically, we conducted evaluations on all six CoFaithfulQA sub-datasets (using both D and ). following the retrieval protocol from Li al. [39] During both training and inference, the model receives the ground-truth passage along with the top-two retrieved passages (deduplicated against the ground-truth), with the order randomly shuffled to prevent positional bias. In addition our main results, we also report Exact Match (EM) scores.
As shown in Table ParamMute consistently outperforms all baselines across most datasets, achiev- ing the highest faithfulness and the lowest reliance on parametric memory under the noisy retrieval setting. These results further demonstrate the robustness and generalizability of ParamMute in realistic RAG scenarios.
Model
Vanilla-RAG
Attr prompt
O&
COIECD
SFT
KAFT
ParamMute
LLaMA-3-8B 63.6
64.9
64.0
64.7
63.2
65.2
67.4
Qwen-2.5-7B
55.9
57.0
49.4
59.6
59.7
60.4
62.7
Table 9: Performance comparison of different methods on FaithEval. Experiments are conducted on both LaMMA-8B-Innsruu and Swen2.5--B-Instreen The best results for each model are highlighted bold, while the srond-highest scores are underlined.
A.13 Faithfulness Evaluation on FaithEval
While our main evaluation focuses on the proposed CoFaithfulQA, which primarily targets Temporal Misalignment [67] and CoFiQA []. both categorized as Misinformation Pollution according to the taxonomy of Xu et al. [67], it is also important to assess the generalizability of ParamMute on additional faithfulness benchmarks. To this end, we further evaluate ParamMute on the counterfactual subset of the FaithEval benchmark [67]. which can also be classified as Misinformation Pollution under the same taxonomy [67]
The results are shown in Table 9 It can be observed that ParamMute consistently outperforms all baselines on EvilhEval. Specifically, ParamMute achieves improvements of 2.4% and 2.3% over the strongest baseline on further highlight the robustness of ParamMute and demonstrate the effectiveness of suppressing parametric knowledge activation in enhancing model faithfulness.
and (rre2255-7BIInstuue respectively. These results
Models
GSM8K COT (8)
GPQA (5)
CoQA
Average
SFT
64.06
29.24
50.92
48.07
ParamMute (A=0.0)
9.93
27.90
45.55
27.79
ParamMute (A=0.5)
54.36
28.79
52.32
45.16
ParamMute (A=1.0)
63.70
30.58
57.5
50.59
Table 10: Closed-book performance on non-contextual tasks. We use 8-shot Chain-of-Thought (CoT) prompting for GSM8K and 5-shot prompting for GPQA. The Supervised Fine-Tuning (SFT) model serves as the baseline. The notion ParamMute (A = x)) indicates our model was trained with full suppression (A 0) and then evaluated with suppression coefficient of during inference.
A.14 What the Impact of UA-FFNS Suppression?
To evaluate whether suppressing the UA-FFNs affects the model's performance, we assess its closed- book performance on range of tasks under non-contextual settings, including mathematical reasoning and general knowledge probing. Specifically, we use the GSM8K [8) GPQA (54, and CoQA (53 datasets. For fair comparison, all methods are evaluated with Im-evaluation. harnessss following the protocols described in under different values of \ during inference. Notably, although ParamMute is trained with À = 0, our soft suppression mechanism (see Eq. [1 enables flexible adjustment of À at inference time.
[9]
28 41
We compare the SFT to our proposed ParamMute
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---

The results are shown in Table 10 Setting À = 0 leads to a substantial drop in performance, with an average decrease of 20.28% compared to the SFT baseline. indicating that our method can effectively suppress the use of parametric knowledge. As À increases, the model's performance gradually recovers and ultimately surpasses the SFT baseline when À = 1.0, achieving an average improvement of 2.52%. These findings demonstrate that Param.Mute not only preserves performance on non-contextual tasks, but also provides flexible control over the contribution of internal knowledge, allowing the model to adapt to different requirements by adjusting À during inference.
A.15 How Activation Strength Shapes Parametric Knowledge Reliance?
To better understand how activation strength af- fects the model's reliance internal paramet- ric knowledge, we conduct experiments under both the zero-shot and knowledge-adee adgated set- tings, Specifically, we evaluate Memory Recall (MemR) and Memorization Ratio (MR) across range of suppression coefficients À on ConFiQA and CoFaithfulQA.
-O- Cast os -O- Conabiru GA
20
-C Cortiga
60
Memn (%g
15
28
MR (16)
HD+ Colue
55
16
0.25 0.75
1.25
0.25 0.5 0.75 1.0 1.25
Value of
Value of A
(a) Memory Recall.
(b) Memorization Ratio.
As shown in Figure 9 panels (a)-(b) report results for the original model without fine- tuning. In both cases, we observe that decreas- ing A-ie., applying stronger suppression to UA- FFNs activations-coosistentty reduces MemR and MR, indicating that suppression effectively (lower MemR), without degrading the model's use of external context, as evidenced by the expected decline in MR with decreasing X.
Figure 9: Trends in Memory Recall (MemR) and Memorization Ratio (MR) under varying suppres- sion coefficients X, evaluated on ConFiQA and Co- FaithfulQA. Each point reflects the average metric across all subsets within the respective benchmark. reduces reliance on internal parametric memory
These findings empirically highlight the relationship between FFN activation strength and the model's dependency on parametric knowledge. Moreover, they demonstrate the potential of activation-level control as a mechanism for modulating knowledge reliance, offering practical insights for flexibly balancing internal memory and contextual grounding in downstream applications.
Varilla-MVG
20
Vanilla RAG
Vorill RAG Parambhute
Vorila RAG
Con't (%)
ar.
ParamMute
MemR (%)
ParamMute
ParamMute
67.0
67.8
11.5
92
$1.8
6.5
B.R
Cont
LLaMA 3.2-18
LLaMA 3.2-38
LLaMA 3.88
LLMMA 3.2-18
LLaMA 3.2-36
ULMMA
200
221158
2033
2010
224
2570
(a) ConR
Results
on (b)
MemR
Results
on (c)
ConR
Results
(d) MemR Results on Qwen Models
LLaMA Models
LLaMA Models
Qwen Models
Figure 10: Average ConR and MemR across different models based on the LLaMA and Qwen series, before and after applying PuramMute.
A.16 Extending ParamMute to More LLMs
We extend ParamMute to a diverse range of LLMs, encompassing multiple model families and sizes. Specifically, our evaluation includes LLLaMA--B--Instruee 3B-Instruct, Qwen2.5-0.5B Instructt Qwen2.5-1.5P Instrrcct Qwenn.5-3B-Instee IInstruut Qwen2.5-7B Instruct, and 11122.5-44B-Insteee The results ConR and MemR are summarized in Figures I0 while [I] presents the average performance of all models on CoFaithfulQA and ConFiQA. This comprehensive evaluation demonstrates the versatility and scalability of ParamMute across wide spectrum of model architectures and sizes.
1.aaMA...-11B-ssttree I.LaMA3.2-
These experimental results also illustrate several key insights: 1) Larger models tend to rely more on parametric memory. As model size increases in both the LLaMA and Qwen families, MemR also grows, indicating a tendency to overlook external knowledge in favor of internal parameters. ParamMute counteracts this behavior, decreasing larger models' MemR score to even below that of smaller models. 2) ParamMute consistently benefits all evaluated models. Across both LLaMA
25

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CoFaithfulQA
ConFiQA
Models
ConR
MemR
MR
ConR t
MemR
MR 4
I.I.A-3-8B
63.37
10.89
14.9
22.52
31.15
59.77
+ParamMute
69.54
6.18
8.34
69.83
8.60
11.24
LLaMA-3.I-SI
59.53
10.83
15.84
15.38
29.97
68.98
+ParamMute
68.45
6.65
9.10
71.12
9.01
11.44
L.IMAA3,2-1IB
35.44
9.63
21.74
32.09
18.32
36.28
+ParamMute
49.79
6.21
11.27
62.70
7.63
11.38
53.13
10.67
17.02
26.16
23.47
49.05
+ PuraamMute
65.04
6.50
9.28
69.61
8.39
11.09
Swen-22550.555
43.55
10,50
19.39
50.72
17.15
26.20
+ +utramMute
56.17
6.33
10.34
67.54
8.04
11.03
(oven-2.5-1.55
54.46
10.42
16.39
51.69
19.87
28.23
+utr
61.82
6.44
9.69
69.61
8.35
11.05
Qwen-2.5-33
58.60
13.59
18.79
25.47
29.34
55.70
+ParamMute
64.35
6.45
9.31
66.30
8.62
11.94
Qwen-2.5-7B
62.78
13.53
17.79
24.75
33.09
59.04
+ParamMute
65.79
6.30
8.94
69.54
8.85
11.58
Qwen-2.5 2.---255-1
62.13
15.27
19.66
7.86
32.88
83.71
+ParamMute
68.05
6.13
8.48
71.70
8.90
11.29
Table 11: Average performance of LLMs on CoFaithfulQA and ConFiQA before and after applying ParamMute.
and Qwen model families, ParamMute outperforms Vanilla-RAG by boosting accuracy and context faithfulness, underscoring its broad applicability and effectiveness. 3) Not all parameters in RAG models are essential. Pruning parametric knowledge not only reduces computation costs but also fosters better generalization without sacrificing accuracy, highlighting the potential of building parameter-efficient LLM within the RAG framework.
B Limitations and Societal Impacts
Limitations. While our method demonstrates consistent improvements across multiple benchmarks, several aspects remain open for future exploration.
Firstly, facilitate the evaluation of faithfulness in retrieval- augmented generation, CoFaithfulQA is constructed under a controlled setting where the retrieved context is guaranteed to contain sufficient information answer the question. As result, unfaithful responses caused by retrieval failures are not reflected in this benchmark. We aim to extend the benchmark to cover a diverse range of task scenarios future work, thus providing a more comprehensive evaluation of contextual faithfulness in LLMs. Secondly, our intervention strategy focuses on suppressing specific subset of FFN layers based on activation patterns. While effective, this design operates relatively coarse granularity. Exploring finer-gree tner-ngrined interventions, such as at the level of individual neurons, may yield further gains controlling parametric knowledge influence. Finally, due to computational constraints, our experiments are conducted on models of moderate scale. Although our findings generalize across multiple model families, future work could investigate whether similar patterns hold in larger-scale models, and whether scaling effeets introduce challenges or opportunities for intervention.
Societal Impacts. Enhancing the faithfulness of cantly improve the reliability of Al systems in digital assistants, and knowledge- based services. By reducing the likelihood of generating factually incorrect or misleading responses, our method contributes to safer and more trustworthy deployment of large language models in practice. Furthermore, the proposed activation suppression mechanism offers flexible means of controlling the model's reliance on parametric knowledge. This flexibility enables task-specific adaptation-ddnammicllll dynamically increasing or decreasing dependence on internal mem- ory according to contextual demands-making making approach potentially beneficial in wide range of downstream scenarios where different levels of grounding are required, such as healthcare, finance, and scientific research, where factual consistency and evidence alignment are particularly critical.
language models can signifi- applications, such as question answering,
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Reviews:
[{"review_id": "oKKDNCejz0", "reviewer": "Reviewer_iu2z", "summary": "The paper hypothesizes that the faithfulness of the LLM in RAG setting is influenced by unfaithfulness-related FFN activation, which causes the model to use internal parametric knowledge and hallucination. Then the paper propose a way to suppress such action to improve contextual faithfulness to prioritize towards external contextual knowledge in RAG settings. This include identify and suppress the targeted activations, and then fine-tune with SFT and KTO on contextual QA dataset. The paper create a new benchmark called CoFaithfulQA and show their method improve the faithfulness significantly", "questions": "NA", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strength\n- The paper is novel in analyzing unfaithfulness by disecting the activation of FFN layers for unfaithful responses and identify those activation as source of unfaithfulness. It provide correlation analysis on when and where those neurons are activated when an unfaithful response is generated.\n- The method of suppressing the activation based on the faithful and unfaithful subsets is simple. The training part, which is SFT and KTO, is straightforward and intuitive.\n- Experiments are conducted throughly and extensively.\n\n\n## Weakness\n- The new benchmark is highly duplicative of FaithEval (https://arxiv.org/abs/2410.03727). This is not mentioned or compared in the paper. The authors need to add this to the paper.\n- While the unfaithful neurons identification is novel, the training part is not novel, but intuitive.\n- **A big question remained unanswered**: What is actually lost after the deactivation of those FFNs? The paper should investigate more, by analyzing closed-book performance on various non-contextual tasks, such as math reasoning, GPQA. How much raw parametric knowledge have been lost (or gained?) after such process? Are there any unintended consequences of such pruning process?", "was_revised": true, "final_justification": "The authors made significant effort in rectifying the paper with supplemental experiments. The paper is more sound.", "rebuttal": "We sincerely thank Reviewer iu2z for the thoughtful and constructive feedback, and for recognizing the strengths of our work. Below, we first address the primary concern regarding the assessment of non-contextual tasks after deactivating the Unfaithfulness-Associated FFNs (UA-FFNs), and then provide point-by-point responses to the remaining comments.\n\n> Weakness 3: What is actually lost after deactivating those FFNs? The paper should further investigate by analyzing closed-book performance on non-contextual tasks (e.g., math reasoning, GPQA). How much raw parametric knowledge is lost (or gained) after this process, and are there any unintended consequences of such pruning?\n\n**Reply to Weakness 3:**\n\nWe appreciate the reviewer's suggestion to analyze the effects of suppressing UA-FFNs on non-contextual tasks. In our original submission, we did not include experiments on closed-book or general knowledge tasks, as our primary focus was to **improve the contextual faithfulness of LLMs in RAG scenarios** by mitigating their reliance on internal knowledge.\n\nThat said, we agree that evaluating the impact of UA-FFN suppression on non-contextual tasks can further clarify the boundaries of our method and help readers understand its scope. In response to the reviewer's suggestion, we have conducted additional experiments on several non-contextual tasks, including GSM8K, GPQA, and CoQA. We used the lm-evaluation-harness and followed the evaluation protocol in [3] for fair comparison. We compared four methods: the SFT baseline, and our proposed ParamMute with different settings of the suppression coefficient $\\lambda$ at inference time. Notably, although ParamMute is trained with $\\lambda$=0, our soft suppression mechanism (as described in Eq. 7) allows $\\lambda$ to be flexibly adjusted during inference, enabling fine-grained control over the contribution of internal parametric knowledge. The results are summarized in the table1 below:\n\n| Model         | GSM8K COT(8) | GPQA(5) | CoQA | AVG   |\n|-|-|-|-|-|\n| SFT  | **64.06**        | 29.24  | 50.92 | 48.07 |\n| ParamMute($\\lambda$=0)  | 9.93  | 27.90    | 45.55 | 27.79 |\n| ParamMute($\\lambda$=0.5) | 54.36 | 28.79   | 52.32 | 45.16 |\n| ParamMute($\\lambda$=1)   | 63.70  | **30.58**   | **57.5**  | **50.59** |\n\nTable 1: Comparison of Different Methods on Non-Contextual Tasks\n\nAs the results show, setting $\\lambda$ to 0 leads to a significant reduction in factual recall on non-retrieval tasks, confirming the intended effect of reducing the model's dependence on parametric knowledge. Importantly, this reduction in factual recall can be fully recovered by increasing $\\lambda$ (e.g., setting $\\lambda=1$); in non-RAG scenarios, the model's factual recall even surpasses the SFT baseline when $\\lambda=1$. This demonstrates that ParamMute provides practitioners with the flexibility to adjust $\\lambda$ according to their needs, **without unintended consequences, thereby further broadening its practical applicability**. Once again, we thank the reviewer for their insightful and constructive feedback. We believe these analyses significantly strengthen the empirical foundation of our work and demonstrate the practical flexibility of ParamMute in both RAG and non-RAG scenarios. We will incorporate these findings and explicitly highlight their implications in the revised manuscript.\n\n> Weakness 1: The authors need to mention or compare their work with FaithEval in the paper.\n\n**Reply to Weakness 1:**\nWe thank the reviewer for drawing our attention to FaithEval[1]. We would like to respectfully clarify that, although both CoFaithfulQA and the counterfactual subset of FaithEval are designed to evaluate LLM faithfulness under context-memory conflict, our benchmark differs from FaithEval in several important aspects:\n\n**Task Focus**: According to the taxonomy in [2], FaithEval targets **Misinformation Pollution** scenarios, where the retrieved context is intentionally erroneous (e.g., LLM-generated fake evidence), and the model is expected to override its internal knowledge to follow misleading context. In contrast, CoFaithfulQA focuses on **Temporal Misalignment**, where the context is accurate but the model's parametric knowledge is outdated or incorrect. This setup reflects a more prevalent failure case in practical RAG deployments [3], as the model may ignore correct evidence and instead rely on obsolete internal knowledge.\n\n**Construction Pipeline**: FaithEval uses LLM-generated modified contexts, validated by both LLMs and human annotators. In contrast, CoFaithfulQA identifies cases where the model's closed-book answers conflict with the ground-truth context, confirming these instances through multiple LLMs and manual review to ensure genuine disagreement between outdated internal knowledge and accurate external evidence.\n\nTherefore, while both benchmarks contribute to advancing faithfulness evaluation for LLMs, they target different failure modes. To better situate our work within the broader landscape and clarify these distinctions, we fully agree that a more comprehensive discussion of related work-including FaithEval-will strengthen readers' understanding of the field and highlight the unique contributions of CoFaithfulQA. Accordingly, we will **add a dedicated section on this topic** in the revised manuscript.\n\n> Weakness 2: While the unfaithful neurons identification is novel, the training part is not novel, but intuitive.\n\n**Reply to Weakness 2:**\n\nWe thank the reviewer for recognizing the novelty of our core contribution-unfaithful neuron (UA-FFN) identification-and for their feedback regarding the training component. We would like to clarify that the adaptation module is not intended as a standalone novelty, but rather as an indispensable second stage that **synergistically complements our novel suppression mechanism**. The suppression stage addresses over-reliance on parametric knowledge by targeting UA-FFNs, while the adaptation stage recalibrates the model to effectively utilize external context. This recalibration is achieved through a **redesigned loss function that combines margin-based preference objectives with SFT principles**, specifically tailored for the RAG contextual faithfulness setting.\n\nImportantly, our experiments (see Tables 2, 3, and 4) demonstrate that the integrated suppression-and-adaptation framework outperforms baseline methods and ablated variants, validating the necessity and effectiveness of combining both stages. Thus, the novelty of ParamMute lies not in individual components, but in the overall design and empirical validation of the complete suppression-and-adaptation architecture. We will revise our manuscript to better clarify this design rationale and how the two stages work together. \n\nIn summary, we sincerely thank Reviewer iu2z for the thoughtful feedback. We appreciate your valuable input and will incorporate all additional analyses and clarifications in the revised manuscript. We hope these revisions address your concerns and further strengthen our work.\n\n---\n[1] Ming Y, et al.FaithEval: Can Your Language Model Stay Faithful to Context, Even If\" The Moon is Made of Marshmallows\". ICLR2025.\n\n[2] Xu R, et al. Knowledge Conflicts for LLMs: A Survey. EMNLP2024.\n\n[3] Kortukov E, et al. Studying large language model behaviors under context-memory conflicts with real documents. COLM2024."}, {"review_id": "IauWBK7SXy", "reviewer": "Reviewer_4WNi", "summary": "This paper introduces ParamMute, a framework designed to enhance the faithfulness of RAG in LLMs. The authors observe that unfaithful generation—where model outputs contradict retrieved evidence—often stems from over-reliance on internal parametric knowledge embedded in specific feed-forward network (FFN) layers. ParamMute mitigates this issue by identifying and suppressing Unfaithfulness-Associated FFNs (UA-FFNs) to reduce parametric interference and by incorporating a plug-and-play adaptation module that calibrates the model to prioritize external evidence.", "questions": "1. Are the findings (i.e., \"when a specific subset of mid-to-deep FFN layers exhibits excessive activation, the model tends to rely more heavily on its internal knowledge, consequently producing unfaithful outputs\") generalizable to models with larger sizes (e.g., 70B)?\n2. Are there any patterns in the selected set of FFN layers, such as distribution between shallow or deep layers, and does layer selection have any connections to model size?\n3. During evaluation, is the selected set of layers fixed across different tasks?\n4. How would suppressing FFNs impact the model's performance in non-retrieval situations (i.e., simply removing all retrieved documents during the evaluation)?\n5. Regarding the retrieval setup, based on Appendix A.7, it appears that retrieval recall is intentionally set as 100% for all benchmarks. Can the model be evaluated in a more realistic setup with imperfect retrieval? Would the proposed method still achieve high faithfulness?", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The paper provides a compelling explanation for unfaithful generation, linking it to specific FFN layers. Causal intervention experiments (e.g., increased NLL loss when suppressing UA-FFNs) robustly support this claim.\n\n- ParamMute is lightweight (suppression + adaptation) and effective across diverse model families (LLaMA, Qwen) and sizes.\n\n- The introduced CoFaithfulQA benchmark is useful for future research, and the proposed method is validated on both CoFaithfulQA and ConFiQA, demonstrating promising potential.\n\n### Weaknesses\n\n- My main concern is about the limited scope of the evaluation: the paper assumes that the retrieved guideline fragments are always sufficient and accurate, but does not test robustness under noisy or incomplete retrieval, which would better reflect real-world conditions.\n- Reducing reliance on parametric knowledge may undermine the model's factuality in non-RAG scenarios.\n- It is unclear how sensitive the model is to the suppression coefficient. Plotting a curve with standard errors to study its impact would provide valuable insights.", "was_revised": true, "final_justification": "I've read the authors' responses and other reviewers' comments, and I remain positive about the work.", "rebuttal": "We sincerely thank Reviewer 4WNi for the constructive and insightful feedback. We appreciate your thoughtful questions and suggestions, which have helped us further clarify and improve our work. Below, we address each of your concerns in detail.\n\n> W1 & Q5: The current evaluation assumes that the retrieved guideline fragments are always sufficient and accurate, and does not assess robustness under noisy or incomplete retrieval, which would be more representative of real-world scenarios. Can the model still maintain high faithfulness in such a realistic setup with imperfect retrieval?\n\n**Reply to W1 & Q5:**\n\nWe thank the reviewers for raising this important point regarding the evaluation scope and retrieval setup. We fully agree that real-world retrieval is often noisy, and that evaluating robustness under such conditions is crucial for practical deployment. In our current submission, we adopt a controlled setting-consistent with recent RAG faithfulness literature [1-2]-mainly because (1) faithfulness evaluation requires that the provided context contains all key information, and (2) introducing noisy passages would add confounding variables, such as inter-context contradictions, complicating the analysis of faithfulness mechanisms [3].\n\nNevertheless, we agree that evaluating ParamMute in more realistic RAG scenarios-including noisy retrieval-is important for demonstrating its generalizability and robustness. To this end, we conducted additional experiments on all six CoFaithfulQA sub-datasets (using both D+ and D-) following the retrieval protocol described in [4]: during both training and inference, the model receives the ground-truth passage and the top-2 retrieved passages (deduplicated against the ground-truth), with order randomly shuffled. We compare ParamMute with three prompting baselines, SFT, and KAFT; results are shown in the table1 below.\n\n||AVG. ConR↑|AVG. MemR↓|HotpotQA ConR↑|HotpotQA MemR↓|NQ ConR↑|NQ MemR↓|NewsQA ConR↑|NewsQA MemR↓|SearchQA ConR↑|SearchQA MemR↓|SQuAD ConR↑|SQuAD MemR↓|TriviaQA ConR↑|TriviaQA MemR↓|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|Vanilla-rag|60.65|12.00|56.90|14.51|45.64|19.23|57.34|9.14|68.56|9.92|70.52|9.30|64.93|9.91|\n|Attr_prompt|57.95|11.33|51.37|14.72|44.00|16.89|56.88|7.34|65.44|9.13|69.39|9.08|60.63|9.52|\n|O&I_prompt|51.03|8.74|42.05|11.98|41.38|10.11|48.53|9.58|53.02|9.30|65.89|6.83|55.28|8.47|\n|SFT|68.66|7.15|69.01|7.55|58.41|10.24|65.02|8.14|78.94|6.94|80.44|4.07|65.11|8.34|\n|KAFT|69.09|7.27|68.75|6.87|60.89|10.50|65.35|8.57|79.04|7.56|80.27|4.18|62.84|6.94|\n|ParamMute|**71.08**|**6.34**|**71.90**|**6.63**|**61.60**|**9.59**|**67.28**|**6.85**|**80.71**|**6.04**|**81.62**|**4.00**|**68.24**|**6.78**|\n\nTable 1: Comparison of Methods in Noisy RAG Settings\n\nParamMute **consistently outperforms baselines across most datasets**, achieving the highest faithfulness and lowest reliance on parametric memory, even with noisy retrieval. These results demonstrate its robustness and generalizability in realistic RAG scenarios. We will include these findings to provide a more comprehensive evaluation under imperfect retrieval.\n\n> W2 & Q4: Reducing reliance on parametric knowledge may undermine the model's factuality in non-RAG (closed-book) scenarios. How does suppressing FFNs affect the model's performance when no retrieved documents are provided during evaluation?\n\n**Reply to W2 & Q4:**\n\nWe thank the reviewer for highlighting the importance of evaluating ParamMute in non-RAG (closed-book) scenarios. We agree that reducing reliance on parametric knowledge may undermine the model's factuality in such cases, and we have confirmed this in our supplementary experiments, as shown by the ParamMute ($\\lambda = 0$) results in the table2 below. This outcome is expected, as suppressing the model's dependence on parametric knowledge inevitably reduces its ability to recall facts from internal memory in the absence of external evidence.\n\nHowever, we would like to emphasize that ParamMute is designed to **provide fine-grained control over parametric knowledge** (see Eq. 7), allowing users to dynamically adjust $\\lambda$ during inference to balance internal knowledge and external evidence as needed. To demonstrate this flexibility, we also evaluated ParamMute with $\\lambda$ set to 0.5 and 1.0 during inference. As shown in the table2, gradually increasing $\\lambda$ enables the model to recover factuality, even outperforming the SFT baseline when $\\lambda = 1$. We will include these experiments in the revised version to further demonstrate the **adaptability and practical** utility of ParamMute across diverse scenarios.\n\n||HotpotQA|NQ|NewsQA|SearchQA|SQuAD|TriviaQA|AVG.|\n|-|-|-|-|-|-|-|-|\n|SFT|**15.5**|**13.83**|5.76|62.17|13.12|52.14|27.09|\n|ParamMute($\\lambda$=0)|4.25|3.10|1.97|14.13|3.61|12.59|6.61|\n|ParamMute($\\lambda$=0.5)|12.45|10.91|5.07|54.6|10.89|43.61|22.92|\n|ParamMute($\\lambda$=1)|15.22|13.24|**5.97**|**62.92**|**13.30**|**52.68**|**27.22**|\n\nTable2: Results on CoFaithfulQA in non-retrieval (closed-book) settings. The reported metric is accuracy.\n\n> W3:It is unclear how sensitive the model is to the suppression coefficient. Plotting a curve with standard errors to study its impact would provide valuable insights.\n\n**Reply to W3:**\n\nWe fully agree that plotting a curve to study the model's sensitivity to the suppression coefficient is important. As shown in Appendix A.9, we have already examined the effects of key hyperparameters, including the suppression coefficient **(Figure 5(a))**. To further address this point, we reran the experiments with different values of $\\lambda$ three times each and report the mean and standard error in the table3 below, as figures are not allowed in the rebuttal. The results confirm the same trend as in the paper: **increasing $\\lambda$ strengthens the model's reliance on parametric knowledge and reduces contextual faithfulness**. We will update the figure in the revised version to include standard errors for a more comprehensive presentation.\n\n|$\\lambda$|0.0|0.25|0.5|0.75|1.0|\n|-|-|-|-|-|-|\n|ConR↑ Mean|69.54|69.44|69.27|69.23|69.14|\n|ConR↑ Mean ± SE|69.54 ± 0.058|69.44 ± 0.047|69.27 ± 0.078|69.23 ± 0.072|69.14 ± 0.11|\n|MemR↓ Mean|6.18|6.33|6.63|6.94|7.23|\n|MemR↓ Mean ± SE|6.18 ± 0.07|6.33 ± 0.049|6.63 ± 0.13|6.94 ± 0.052|7.23 ± 0.055|\n\nTable 3: Performance Metrics (Mean ± SE) for Different Suppression Coefficients\n\n> Q1 & Q2:Are the findings about the relationship between mid-to-deep FFN activation and unfaithful outputs generalizable to larger models (e.g., 70B)? Additionally, are there any patterns in the selected set of FFN layers-such as their distribution across shallow or deep layers-and does layer selection depend on model size?\n\n**Reply to Q1 & Q2:**\n\nWe thank the reviewer for raising these important questions about the generalizability of our findings to larger models and the selection patterns of FFN layers. To address this, we conducted additional experiments on **Qwen2.5-32B and LLaMA3-70B**. We report $D_-(R)$, $D_+(R)$, and $\\Delta R^l$ in the table4 below. Due to space constraints, only a subset of representative UA-FFN layers is shown.\n\n||0|1|47|48|51|54|57|60|63|66|69|70|78|79|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|$\\mathbb{E}_{\\mathcal{D}-}[R^l(\\hat{r})]$|0.4653|0.3861|0.3715|0.3841|0.3792|0.3523|0.349|0.332|0.3137|0.3102|0.2946|0.2868|0.5594|0.7412|\n|$\\mathbb{E}_{\\mathcal{D}+}[R^l(\\hat{r})]$|0.4692|0.3817|0.3507|0.3611|0.3621|0.3373|0.3346|0.3192|0.2993|0.3011|0.2923|0.2866|0.5556|0.7425|\n|$\\Delta R^l$|-0.0395|0.0442|**0.2082**|**0.2294**|**0.1702**|**0.1503**|**0.1431**|**0.1276**|**0.1438**|**0.0909**|0.0235|0.0018|0.0376|-0.0126|\n\nTable4: Differences in Activation Patterns of LLaMA3-70B-Instruct (80 layers) on CoFaithfulQA. Notably, $\\Delta R^l$ values are substantially larger between layers **47 and 66**. For Qwen2.5-32B results, see Table 3 in our response to Reviewer EqKC.\n\nThe results confirm the **robustness of our key finding**: excessive activation in a specific subset of mid-to-deep FFN layers (typically 60%–85% of the depth) is consistently associated with unfaithful outputs, even for the largest models tested. Notably, the distribution of these layers is similar across different model sizes and architectures, indicating this pattern is not model-specific. We will include detailed results and visualizations in the revised manuscript. These findings suggest that our conclusions generalize well and provide valuable insight into the mechanism underlying unfaithful generation in LLMs, which we hope will inform and inspire future research. We will include the detailed results and visualizations in the revised manuscript.\n\n> Q3:During evaluation, is the selected set of layers fixed across different tasks?\n\n**Reply to Q3:**\n\nYes, the selected set of layers for a given model is fixed across different tasks and datasets, and is determined based on the model's activation gap on the D+ and D- subsets. Once identified, these layers remain unchanged during evaluation. We will clarify this procedure further in the revised manuscript to strengthen the overall presentation.\n\nWe sincerely thank Reviewer 4WNi again for the thoughtful feedback and constructive suggestions. We believe the newly added analyses and experiments address your concerns and further strengthen the empirical foundation and clarity of our work. We hope our responses and revisions will improve the paper's quality and make its contributions more compelling.\n\n---\n[1] Bi B, et al. Context-dpo: Aligning language models for context-faithfulness[J]. ACL2025.\n\n[2] Huang L, et al. Improving contextual faithfulness of large language models via retrieval heads-induced optimization[J]. ACL2025.\n\n[3] Xu R, et al. Knowledge Conflicts for LLMs: A Survey. EMNLP2024.\n\n[4] Li X, et al. Rag-ddr: Optimizing retrieval-augmented generation using differentiable data rewards[J]. ICLR2025."}, {"review_id": "OymscG0J5z", "reviewer": "Reviewer_MDE9", "summary": "This paper presents ParamMute, a novel framework aimed at improving the contextual faithfulness of large language models (LLMs). It tackles the challenge of LLMs overly relying on internal parametric knowledge instead of retrieved evidence. ParamMute reduces this bias by selectively suppressing feed-forward networks (FFNs) that are highly correlated with unfaithful generations. To further reinforce grounding in external content, it incorporates a plug-and-play adaptation module that strengthens the model’s reliance on retrieved information. It also introduces CoFaithfulQA, a benchmark derived from six diverse QA datasets, designed to evaluate faithfulness in the presence of conflicting knowledge. Extensive experiments on CoFaithfulQA and ConFiQA show that ParamMute significantly improves faithfulness while reducing dependence on internal knowledge.", "questions": "1. Can the authors clarify whether the performance gains shown in Table 2 are statistically significant compared to the baselines?\n\n2. Could the authors report the accuracy of the methods in Table 2? This would help readers better understand how well the models are performing on the core task beyond faithfulness metrics.\n\n3. Can the authors include or summarize qualitative examples of failure cases? Such analysis would offer valuable insight into the types of errors the model still makes and the limitations of the proposed approach.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "This paper has the following strengths:\n\n1. It proposes a novel method, Parametric Knowledge Muting through FFN Suppression (ParamMute), which improves contextual faithfulness by suppressing FFNs associated with unfaithful generation and calibrating the model toward retrieved evidence.\n\n2. It introduces CoFaithfulQA, a new benchmark tailored to evaluate faithfulness in settings where internal knowledge conflicts with accurate external sources, offering a valuable resource for future research.\n\n3. Extensive experiments demonstrate that ParamMute consistently improves faithfulness across both CoFaithfulQA and the existing ConFiQA benchmark, substantially reducing reliance on parametric memory.\n\nHowever, this paper has the following weaknesses:\n\n1. The results in Table 2 do not clearly show that ParamMute significantly outperforms baseline methods; the improvements appear marginal.\n\n2. Reporting the accuracy metric in Table 2 would help assess each method’s effectiveness on the underlying QA tasks.\n\n3. Including qualitative analysis of failure cases would provide deeper insights into the challenges of the task and the model’s limitations.", "was_revised": true, "final_justification": "After reading the rebuttal, my concerns have been addressed. Thus, I keep my positive score for this paper.", "rebuttal": "We sincerely thank Reviewer MDE9 for the helpful and insightful comments. We appreciate the time and effort you devoted to reviewing our work and for raising several important points that have helped us improve both the clarity and rigor of our manuscript. Below, we address each of your concerns in detail.\n\n> Weakness 1 & Question1: The improvements in Table 2 appear marginal. Can the authors clarify whether the observed performance gains are statistically significant compared to the baselines?\n\n**Reply to Weakness 1 & Question1:** \n\nWe appreciate the reviewer's emphasis on this important aspect. We agree that conducting statistical significance tests to evaluate performance gains will further strengthen the rigor of our results. Following this suggestion, we have performed significance testing against all baselines on both ConR and MemR, as MR is a derived metric based on ConR and MemR and therefore cannot be directly subjected to significance testing. The results are shown in the table1 and table2 below. The findings show that the improvements of ParamMute are statistically significant across most benchmarks and baselines. In the revised manuscript, we will present these statistical analyses alongside the main results to enhance the clarity and rigor of our performance comparisons.\n\n||HotpotQA ConR↑|HotpotQA MemR↓|HotpotQA EM↑|NQ ConR↑|NQ MemR↓|NQ EM↑|NewsQA ConR↑|NewsQA MemR↓|NewsQA EM↑|\n|-|-|-|-|-|-|-|-|-|-|\n|vanilla-rag|60.34†|13.88†|9.35†|53.09†|14.41†|2.84†|60.27†|8.24†|5.03†|\n|Attr_prompt|58.93†|13.95†|3.46†|55.36†|11.07†|0.41†|58.87†|7.56†|1.61†|\n|O & I|47.79†|10.72†|1.83†|49.25†|8.23|0.61†|52.03†|5.3†|1.5†|\n|COIECD|62.51†|12.19†|24.73†|56.21†|12.28†|12.47†|61.44†|6.31†|33.83†|\n|SFT|70.92|6.24|63.69†|59.76†|10.29†|55.31†|61.97†|5.08†|54.39†|\n|KAFT|69.52†|6.87†|63.48†|60.89|9.23|58.96|65.09|4.74†|53.32†|\n|C-DPO|67.21†|7.64†|51.56†|62.24|9.79†|40.09†|61.47†|4.96†|36.42†|\n|DDR|68.66†|7.15†|41.16†|63.29|10.33†|0†|64.74†|4.85|37.6†|\n|ParamMute|71.06|6.17|66.06†|60.68|9.08|58.76|65.24|4.85|55.57|\n\nTable 1: Statistical significance analysis on HotpotQA, NQ, and NewsQA. A † symbol indicates that ParamMute outperforms the corresponding baseline with statistical significance ($p < 0.05$). Exact match (EM) scores are also reported to provide further insight into QA task performance.\n\n| |SearchQA ConR↑|SearchQA MemR↓|SearchQA EM↑|SQuAD ConR↑|SQuAD MemR↓|SQuAD EM↑|TriviaQA ConR↑|TriviaQA MemR↓|TriviaQA EM↑|\n|-|-|-|-|-|-|-|-|-|-|\n|vanilla-rag|66.76†|10.55†|23.45†|77.93†|6.79†|8.8†|61.8|11.47†|10.23†|\n|Attr_prompt|62.53†|10.55†|5.28†|77.35†|6.38†|3.02†|59.97†|10.43†|9.59†|\n|O & I|52.26†|9.23†|4.68†|76.81†|6.11†|2.55†|55.41†|8.1†|1.14†|\n|COIECD|69.74†|11.66†|20.91†|73.12†|7.64†|9.02†|63.62|11.99†|19.95†|\n|SFT|75.29†|6.87†|69.27†|79.17†|4.28†|68.31†|61.75†|8.34†|41.97†|\n|KAFT|77.38†|7.43†|73.15†|80.04†|4.18|73.5†|62.32|8.41†|47.41†|\n|C-DPO|76.21†|5.62|56.11†|80.08†|5.26†|50.81†|58.62†|8.47†|34.93†|\n|DDR|78.07†|6.48†|0†|81.36†|4.36†|41.36†|60.71†|7.73†|27.93†|\n|ParamMute|78.76|6.04|75.89|80.58|4.04|72.18|61.9|6.91|56.44|\n\nTable 2: Statistical significance analysis on SearchQA, SQuAD, and TriviaQA. A † symbol indicates that ParamMute outperforms the corresponding baseline with statistical significance ($p < 0.05$).\n\n> Weakness 2 & Question2 : Could the authors report the accuracy metric in Table 2 to better assess each method's effectiveness on the core QA task beyond faithfulness?\n\n**Reply to Weakness 2 & Question 2:** \n\nWe thank the reviewer for this helpful suggestion. We would like to clarify that the **Contextual Recall (ConR) metric reported in Table 2 is equivalent to the standard accuracy** metric commonly used in QA evaluation. ConR is defined as the percentage of questions for which the model's answer is faithful to the context, determined by whether the answer exactly matches the gold answer. To further address the reviewer's concern regarding model performance on QA tasks, we have **also reported exact match (EM) scores**; the results are shown in the table provided in response to Weakness 1 & Question 1. ParamMute consistently outperforms all baselines on both accuracy and EM. \n\nWe will make this correspondence explicit in the revised manuscript to ensure that readers clearly understand the relationship between ConR and standard QA metrics, and we will also include EM results in the revised version to further clarify the effectiveness of ParamMute on QA tasks.\n\n\n> Weakness 3 & Question 3: Including qualitative examples or analysis of failure cases would provide valuable insights into the types of errors the model makes and the limitations of the proposed approach. Can the authors provide such examples?\n\n**Reply to Weakness 3 & Question 3:** \n\nWe thank the reviewer for this valuable suggestion. We agree that qualitative analysis of failure cases can provide deeper insights into the limitations of our approach and the challenges of the task. In response, we performed an error analysis on 50 incorrect predictions and categorized the failure cases as follows:\n\n- Type1: Model is correct but not exactly equal to the ground truth: 24 cases\n- Type2: Incorrect ground truth: 6 cases\n- Type3: Ground truth is ambiguous or too long: 4 cases\n- Type4: Context is unclear: 3 cases\n- Type5: Model's answer is not derived from the provided context: 2 cases\n- Type6: Model persists with incorrect parametric knowledge: 11 cases\n  - Numerical issues: 7 cases\n    - Year: 4 cases\n    - Counting: 3 cases\n  - Person or location related to a specific event: 3 cases\n\nAmong all cases where the model produced incorrect answers, there are 11 instances in which the model persisted with its own parametric knowledge. Most of these involve numerical information-such as years and counts-or specific people or locations related to particular events. We suspect this may be due to the model having encountered these samples during training, resulting in highly confident predictions. To investigate this, we sampled each question five times for these cases and found that in 8 out of 11 instances, the model produced the same answer across all 5 outputs. This indicates that, although ParamMute substantially alleviates reliance on parametric knowledge through FFN suppression, this issue still persists. We provide several representative failure cases in the table below. For most of these cases, we do not include the full context due to space limitations, especially when the error type is unrelated to the context or when the context is excessively long.\n\n\n| Query| Error Type| Answers| Pred. | Context|\n|----------------------------------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------|------------------------|-|\n| Name a musician that worked with Hiren Roy and Vilayat Khan.| Type1| Nikhil Banerjee| Ravi Shankar| Hiren Roy  Hiren Roy (1920–1992) was for many years considered to be the best sitar maker in India.  Many great musicians, including **Nikhil Banerjee**, Vilayat Khan, Annapurna Devi, and **Ravi Shankar**, have sworn by his creations. \\n  Vilayat Khan  Ustad Vilayat Khan (28 August 1928 – 13 March 2004) was one of India's well known sitar maestros.  Along with **Ravi Shankar**, Ali Akbar Khan, **Nikhil Banerjee** and his younger brother Imrat Khan, Vilayat Khan helped introduce Indian Classical Music to the West.|\n| who holds the record for most platinum albums| Type2| AC / DC| The Beatles| \\ |\n| where did the last name keith come from| Type3| a nickname , derived from the Middle High German Kit , a word meaning` sprout , offspring | East Lothian, Scotland| \\ |\n| Rafter, framing & T-bevel are useful types of this \"geometric\" tool| Type5| square| a saw| \\ |\n| when is the last time the dolphins went to the superbowl| Type6| 1984| 1973| \\ |\n| Which agency is involved in negotiations with pirates holding Capt.Phillips?| Type6| FBI| U.S. Navy| \\ |\n\nTable 3: Representative Examples of Common Failure Cases\n\nWe will further expand this qualitative analysis in the revised manuscript to provide readers with a more comprehensive understanding of the challenges and potential directions for future improvement.\n\nWe once again thank Reviewer MDE9 for the constructive feedback and thoughtful suggestions. Your comments have enabled us to strengthen our experimental analysis and clarify key aspects of our work. We will incorporate all the additional analyses, clarifications, and experimental results discussed above in the revised manuscript to provide a more comprehensive and transparent presentation."}, {"review_id": "Qnlkp4t7vq", "reviewer": "Reviewer_EqkC", "summary": "The authors propose ParamMute, a parametric knowledge muting framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs and calibrating the model toward retrieved knowledge. They observe that mid-to-deep FFN layers (UA-FFNs) become highly activated when generating unfaithful responses. These layers are considered to contain heavy internal knowledge, which leads the model to generate unfaithful outputs.\nTo address this, the authors introduce ParamMute, which suppresses the activation of such UA-FFN layers and incorporates an adaptation module to encourage reliance on external knowledge. This module includes knowledge-augmented fine-tuning and knowledge preference optimization.\nThey also introduce a new benchmark, CoFaithfulQA, designed to evaluate faithfulness in cases where there is a conflict between the model's parametric knowledge and external knowledge.\nParamMute demonstrates strong performance on both the CoFaithfulQA and ConFiQA benchmarks, generating more faithful responses.", "questions": "I recommend moving the baseline descriptions and the experiments on key hyperparameters from the appendix into the main text.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The paper is clearly written.\n2. The authors present an analysis showing that mid-to-deep FFN layers become highly activated when generating unfaithful responses, and demonstrate that suppressing these layers reduces reliance on parametric knowledge.\n3. The authors propose ParamMute, a two-stage framework that reduces activation of UA-FFN layers and incorporates a plug-and-play adaptation module to recalibrate the model's knowledge utilization preferences, and show that it leads to generating more faithful respones.\n4. The authors conduct various experiments, including hyperparameter search and exploration of different model choices.\n\n**Weaknesses**\n1. The motivation for constructing the CoFaithfulQA benchmark is somewhat unclear. How does it differ from the ConFiQA benchmark? If you select instances where the external evidence is accurate and the parametric knowledge is incorrect, can this task be generalized across various base models, considering that each may have different parametric knowledge?\n2. Does the choice and ranking of UA-FFN layers remain consistent across different datasets and base models? Since D- and D+ depend on the dataset and model, it is unclear whether these layers vary with different settings.", "was_revised": true, "final_justification": "My concerns were solved during the discussion, thus I will keep the rating towards acceptance.", "rebuttal": "We sincerely thank the reviewer for the positive evaluation and for the constructive, insightful feedback provided. We greatly appreciate your careful reading of our manuscript and your thoughtful comments, which have helped us to further clarify and strengthen our work. Below, we address each of your concerns in detail.\n\n> Weakness 1: The motivation for constructing the CoFaithfulQA benchmark is unclear. How does it differ from the ConFiQA benchmark? Additionally, if external evidence is accurate but parametric knowledge is incorrect, can this evaluation task be generalized across different base models, given that each model may possess different parametric knowledge.\n\n**Reply to Weakness 1:** \n\nWe sincerely appreciate the reviewer's valuable comments and thoughtful questions regarding (1) the differences between our CoFaithfulQA benchmark and ConFiQA, and (2) the generalizability of our task across various base models.\n\n**Motivation and Differences from ConFiQA**:\n\nCoFaithfulQA is designed to evaluate the contextual faithfulness of RAG models in realistic, naturally occurring knowledge conflict scenarios, where the retrieved context is accurate but the model's internal knowledge may be outdated or incorrect (i.e., **temporal misalignment**) [1]. Such situations are common in practical deployments but are underrepresented in existing benchmarks. In contrast, ConFiQA primarily relies on LLM-generated counterfactual contexts to create artificial contradictions for stress-testing, which, according to [2], falls under the category of **Misinformation Pollution**. While both benchmarks aim to advance faithfulness evaluation for LLMs, they target different types of knowledge conflicts and are therefore complementary.\n\n**Generalizability across Different Base Models**:\nWe agree with the reviewer's observation that our initial \"unfaithful\" subset is constructed based on conflicts identified with a specific base model (e.g., LLaMA3-8B), and therefore, some conflict cases may differ across models due to variations in internal knowledge. However, the core challenge of our task-improving faithfulness in situations where incorrect internal knowledge conflicts with correct external evidence-is broadly applicable to all LLMs. More importantly, our contribution extends **beyond a static dataset**: we present a general, reproducible, and user-friendly pipeline for automatically constructing CoFaithfulQA-style benchmarks. This model-agnostic pipeline can be easily used by any researcher to generate conflict cases and evaluate the contextual faithfulness of any model of interest, providing a practical and scalable solution for diverse research needs.\n\nTo facilitate adoption and demonstrate the applicability of our approach, we will open source the data generation pipeline, along with example CoFaithfulQA subsets for several mainstream models (e.g., GPT-4o, LLaMA3.2-70B, Qwen2.5-32B, Qwen2.5-7B).\n\nIn the revised manuscript, we will further clarify these distinctions and emphasize the general applicability of our proposed evaluation framework. We sincerely thank the reviewer again for highlighting these important points.\n\n> Weakness 2: Does the choice and ranking of UA-FFN layers remain consistent across different datasets and base models? Since D- and D+ depend on the dataset and model, it is unclear whether these layers vary with different settings.\n\n**Reply to Weakness 2:** \n\nWe sincerely thank the reviewer for raising this important and insightful question. We agree that the definitions of D− and D+ depend on the specific model and dataset, and that the set of identified UA-FFN layers could, in principle, vary across different settings. To better assess the generalizability of our findings, we conducted two additional experiments: (1) constructing D+ and D− with different datasets, and (2) using different model architectures and sizes to construct D+ and D−. As figures cannot be included in the rebuttal, we report the $D_-(R)$ and $D_+(R)$, as well as $\\Delta R^l$, in the table below. Due to space constraints, we have omitted layers with negligible $\\Delta R^l$ values, and only a subset of representative UA-FFN layers are shown (with some layers within each range skipped).\n \n\n||0|1|2|18|19|21|23|25|27|29|31|\n|------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\n|$\\mathbb{E}_{\\mathcal{D}-}[R^l(\\hat{r})]$|0.2693|0.3584|0.3114|0.1502|0.1593|0.1617|0.1866|0.1879|0.1852|0.2585|0.6179|\n|$\\mathbb{E}_{\\mathcal{D}+}[R^l(\\hat{r})]$|0.2818|0.3613|0.3097|0.1466|0.1466|0.139|0.1589|0.1565|0.1559|0.2362|0.6118|\n|$\\Delta R^l$|-0.126|-0.0288|0.0175|**0.0365**|**0.1266**|**0.2266**|**0.2768**|**0.314**|**0.2926**|**0.2233**|0.0609|\n\nTable 1: Differences in Activation Patterns of LLaMA3-8B-Instruct (32 layers) on HotpotQA. Notably, $\\Delta R^l$ values are substantially larger between layers 19 and 29.\n\n\n||0|1|2|18|19|21|23|25|27|29|31|\n|------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------|\n|$\\mathbb{E}_{\\mathcal{D}-}[R^l(\\hat{r})]$|0.2734|0.3642|0.3151|0.1512|0.1608|0.1585|0.1743|0.1626|0.156|0.2276|0.5982|\n|$\\mathbb{E}_{\\mathcal{D}+}[R^l(\\hat{r})]$|0.2828|0.3645|0.3114|0.1455|0.1439|0.1296|0.1402|0.1275|0.1251|0.206|0.602|\n|$\\Delta R^l$|-0.0931|-0.0026|0.0373|0.0577|**0.1697**|**0.2888**|**0.3409**|**0.3519**|**0.3085**|**0.2161**|-0.0376|\n\nTable 2: Differences in Activation Patterns of LLaMA3-8B-Instruct (32 layers) on TriviaQA. Notably, $\\Delta R^l$ values are substantially larger between layers 19 and 29.\n\n||0|1|2|3|42|43|44|45|47|49|51|53|55|62|63|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|$\\mathbb{E}_{\\mathcal{D}-}[R^l(\\hat{r})]$|0.3116|0.0007|0.0012|0.005|0.2413|0.2471|0.2878|0.2612|0.2088|0.199|0.1626|0.1658|0.1775|0.334|0.315|\n|$\\mathbb{E}_{\\mathcal{D}+}[R^l(\\hat{r})]$|0.3212|0.0008|0.0012|0.0053|0.2389|0.2415|0.2746|0.2428|0.1972|0.1869|0.1552|0.1593|0.1731|0.3381|0.3182|\n|$\\Delta R^l$|-0.0961|0|-0.0022|-0.0026|0.0236|0.0562|**0.1323**|**0.1836**|**0.1166**|**0.12**|**0.0741**|**0.0651**|0.0436|-0.0409|-0.032|\n\nTable 3: Differences in Activation Patterns of Qwen2.5-32B-Instruct  (64 layers) on CoFaithfulQA. Notably, $\\Delta R^l$ values are substantially larger between layers 44 and 53.\n\n||0 |1 |47|48|51|54|57|60|63|66|69|70|78|79 |\n|------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------|\n|$\\mathbb{E}_{\\mathcal{D}-}[R^l(\\hat{r})]$|0.4653|0.3861|0.3715|0.3841|0.3792|0.3523|0.349|0.332|0.3137|0.3102|0.2946|0.2868|0.5594|0.7412|\n|$\\mathbb{E}_{\\mathcal{D}+}[R^l(\\hat{r})]$|0.4692|0.3817|0.3507|0.3611|0.3621|0.3373|0.3346|0.3192|0.2993|0.3011|0.2923|0.2866|0.5556|0.7425|\n|$\\Delta R^l$|-0.0395|0.0442|**0.2082**|**0.2294**|**0.1702**|**0.1503**|**0.1431**|**0.1276**|**0.1438**|**0.0909**|0.0235|0.0018|0.0376|-0.0126|\n\nTable 4: Differences in Activation Patterns of LLaMA3-70B-Instruct (80 layers) on CoFaithfulQA. Notably, $\\Delta R^l$ values are substantially larger between layers 47 and 66.\n\nThe results consistently demonstrate that **our key finding is robust**: excessive activation in a specific subset of mid-to-deep FFN layers (typically 60%–85% of the model depth) is strongly associated with unfaithful outputs, regardless of dataset, model family, or size. This not only highlights the generality of our observation but also provides valuable insights into the mechanisms underlying unfaithful generation and the role of parametric knowledge in LLMs. We hope these findings will inform future research. In the revised version, we will more clearly highlight the stability and broad applicability of this pattern. We thank the reviewer again for their valuable feedback.\n\nIn summary, we thank the reviewer for your high evaluation and for your valuable suggestions. Your feedback has enabled us to improve the clarity, depth, and rigor of our analysis, and we look forward to incorporating these improvements in the revised manuscript. We hope that our additional clarifications and new results address your concerns and further demonstrate the contribution and generalizability of our work.\n\n---\n[1] Kortukov E, et al. Studying large language model behaviors under context-memory conflicts with real documents. COLM2024.\n\n[2] Xu R, et al. Knowledge Conflicts for LLMs: A Survey. EMNLP2024."}]

Author rebuttal:

Meta-review: The paper shows that unfaithful generation in retrieval-augmented models can be traced to the excessive activation of a subset of feed-forward networks (FFNs). Based on this insight, the authors propose a novel framework called ParamMute, which improves the contextual faithfulness of language models by identifying and suppressing these unfaithfulness-associated FFNs. The framework also includes an adaptation module to better calibrate the model toward the retrieved external knowledge. To validate their approach, the authors introduce a new benchmark, CoFaithfulQA, specifically designed to evaluate model faithfulness in scenarios where a model's internal knowledge conflicts with accurate external evidence.

On the positive side, the reviewers found that the paper's primary contribution was its novel analysis of unfaithfulness, which dissects the internal mechanisms of the model by linking unfaithful responses to the activation of specific FFN layers. The reviewers also highlighted that the proposed ParamMute method is simple and effective, and they were impressed by the comprehensive and thorough experiments conducted across various models and benchmarks.

The reviewers identified areas of improvement, such as an initial lack of comparison to a similar existing benchmark (FaithEval) and the need to evaluate the potential negative impact of the method on the model's performance in non-retrieval scenarios. Reviewers also suggested testing the framework's robustness in more realistic settings with noisy or imperfect retrieval and requested a more in-depth analysis of the method's generalizability to larger models, its statistical significance, and its common failure cases. During the rebuttal period, the authors addressed all of these points with additional experiments and analyses. By incorporating this valuable reviewer feedback, the final manuscript will be more thorough, polished, and of higher quality.

Based on these reviews, I recommend accepting this paper. The reviewers were excited about the paper's novel approach to improving model faithfulness, noting that the investigation into unfaithfulness-associated FFNs provides valuable insights that can deepen the community’s understanding of the internal mechanisms of large language models. The authors were commended for their active engagement and for thoroughly addressing all reviewer concerns during the discussion period. I expect the authors to incorporate the feedback and the extensive additional experiments conducted during the rebuttal into the final version of the paper (especially generalization and failure case analysis).
Decision: Accept (poster)