Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models
Authors: Baolong Bi
Shenghua Liu
Yiwei Wang
Yilong Xu
Junfeng Fang
Lingrui Mei
Xueqi Cheng
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Retrieval-Augmented Generation
Knowledge Conflict
Controllable Generation
Knowledge Reliance
Primary area: interpretability and explainable AI

Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and retrieved context pose challenges, particularly when retrieved information is unreliable or the model's internal knowledge is outdated. In such cases, LLMs struggle to determine whether to rely more on their own parameters or the conflicted context. To address this, we propose CK-PLUG, a plug-and-play method for controlling LLMs' reliance on parametric and contextual knowledge. We introduce a novel knowledge consistency metric, Confidence Gain, which detects knowledge conflicts by measuring entropy shifts in token probability distributions after context insertion. CK-PLUG then enables fine-grained control over knowledge preference by adjusting the probability distribution of tokens with negative confidence gain through a single tuning parameter. Experiments demonstrate CK-PLUG's ability to significantly regulate knowledge reliance in counterfactual RAG scenarios while maintaining generation fluency and knowledge accuracy. For instance, on LLaMA3-8B, memory recall (MR) of RAG response can be adjusted within a broad range (9.9%-71.9%), compared to the baseline of 42.1%. Moreover, CK-PLUG supports adaptive control based on the model's confidence in both internal and external knowledge, achieving consistent performance improvements across various general RAG tasks. Our code is available at: https://anonymous.4open.science/r/CK-PLUG-Ano-8E62

Published as a conference paper at ICLR 2026
PARAMETERS VS. CONTEXT: FINE-GRAINED CONTROL OF KNOWLEDGE RELIANCE IN LANGUAGE MODELS
Baolong Bil
Shenghua Liu 1.2 Yiwei Wang? Yilong Xu!
Junfeng Fang Lingrui Mei Xueqi Chengl.2
1 Sttte Key Laboratory of Al Safety, Institute of Computing Technology, CAS
UUniversity of Chinese Academy of Sciences University of California, Merced "National University of Singapore
[bibaolong23z. liushenghua, xuyilong23s, meilingrui22b, exq) @ictacc.n wangyw.evan gmail.com, langj]1997 @ gmail.com
ABSTRACT
Setrieval-Augmentt Generation (RAG) mitigates hallucinations in Large Lan- guage Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and retrieved context pose challenges, particu- larly when retrieved information is unreliable or the model's internal knowledge is outdated. In such cases, LLMs struggle to determine whether rely more on their own parameters or the confficted context. To address this, we propose method for controlling LLMs' reliance on parametric and contextual knowledge. We introduce novel knowledge consistency metric, Confidence Gain, which detects knowledge conflicts by measuring entropy shifts in token probability distributions after context insertion. CK-PLUG then enables fine-gras aneegrained control over knowledge preference by adjusting the probability distri- bution of tokens with negative confidence gain through single tuning parameter. Experiments demonstrate CK-PLUG" ability to significantly regulate knowledge reliance in counterfactual RAG scenarios while maintaining generation fluency and knowledge accuracy. For instance, on LLAMA3- of RAG response can be adjusted within a broad range (9.9%-71.9%) (1))., compared to the baseline of 42.1% %. Moreover, CK-PLUG supports adaptive control based on the model's confidence in both internal and external knowledge, achieving consistent performance improvements across various general RAG tasks. Our code is available at: https: : htt/t/tttttubtbe com Bot.m//yrooool////oom www.onnn..........C..O CK-PLUG.
CK-PLUG, a plug-and-pln
memory recall (MR)
INTRODUCTION
Detrieval-Augment Generation (RAG) (Lewis et al., 2020; Santhanam et al., 2021; Gao et al., 2023; Fan al., 2024) has become widely adopted technique for various applications, it effectively integrates external knowledge with the powerful generative capabilities of Large Language Models (LLMs) (Achiam al., 2023; Grattafiori al., 2024) to produce accurate responses. However, potential knowledge conflicts (Xu et al., 2024a; Xie et al., 2023; Shi et al., 2025) between the external context and the model's internal parameters pose significant challenges the reliability of RAG-generated outputs, often leading to hallucinations (Huang et al., 2023; Tonmoy et al., 2024).
There exists an inherent trade-off between the factuality of model parameters and the fidelity of externally retrieved context (Bi et al., 2024b). Enhancing the model's 's internal factuality (Chuang al., 2023; Li al., 2024a; Zhang et al., 2024b) may become unreliable as the model becomes outdated, while excessive dependence on retrieved context (Zhou et al., 2023; Shi et al., 2024) can be problematic due to the quality limitations of the retrieved information.
In this paper, we argue that efficient control of knowledge reliance is crucial for the effective deployment of RAG systems. Existing alignment to factuality (Tian et al., 2023; Lin et al., 2024a) or context faithfulness (Bi al., 2024a; Huang al., 2025a) are unidirectional and uncontrollable.
Corresponding author.

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Published as a conference paper at ICLR 2026
Bed para good context
Porometers Context?
Which should believe?
Good para context
Query, Who is the current president of the United States?
Query. Who currently holds ownership of Twitter?
Model Porameters
Conflist!
Retrieved Context
Model Parometers
Confiict!!
Retrieved Context
Fresident Bidkn
Denald
remember That Elon Musk
Jock Dorsey, Twitter' co-founders and CEO.
entregreneur. politicion who
acquired soon
He was elected president
President United
rebronded implementing
played key shoping the
in aworn 2021.
States, served 65
sigrificant changes
platform and development
Figure I: LLMS struggle to prioritize between parametric and contextual knowledge, especially when facing outdated parameters misleading context, reducing reliability in real-world scenarios.
lacking the flexibility for bidirectional adjustment. The degree of reliance on internal parameters versus external context should customizable to adapt to varying RAG scenarios, such as differences in model capabilities or retrieval quality. As illustrated in Figure I, in the case of outdated models or high-quality or professional retrieval environments, the model should rely more on external knowledge. Conversely, when the retrieval context is noisy potentially adversarial, the model should prioritize more its internal parameters to ensure reliable generation.
To achieve this, propose CK-PLUG (Controllable Knowledge Plug-in), pluggable inference- time approach for knowledge reliance control without modifying model parameters or architectures. To enable fine-grained adjustment, CK-PLUG introduces the Confidence Gain metric to detect knowledge conflicts. This metric quantifies the information gain of parameter-aware tokens after injecting contexts, measuring the consistency between parametric knowledge and external context.
Based on this metric, CK-PLUG retains tokens exhibiting positive confidence gains (indicating aligmment between external context and the model's parametric knowledge) while dynamically adjusting the prediction strategy for tokens with negative confidence gains. For the latter, the framework blends parameter-aware and context-aware token probability distributions through weighted fusion mechanism. The balance between these distributions is governed by single tuning parameter a, enabling fine-gran ane-grained control over knowledge reliance preferences. Additionally, CK- PLUG introduces an automated mode that adaptively balances parametric and contextual reliance through entropy-based confidence evaluation, eliminating the need for manual a specification.
We evaluate CK-PLUG various LLMs in RAG scenarios. Under explicit control, the framework achieves substantial adjustments in Memory Recall (MR) for QA tasks with counterfactual retrieval contexts. For instance, LLAMA3- 8B, CK-PLUG modulates MR from 9.89% to 71.93%, signifi- cantly deviating from the baseline MR of 42.09%. In autonomous mode (a-free), our CK-PLUG adaptively balances internal and external knowledge by leveraging model confidence metrics, yielding consistent performance gains across six distinct RAG downstream tasks. Our work paves the way for developing both knowledge-controllable and trustworthy generation capabilities for LLMs.
PRELIMINARY
Language Model Generation The current language model generation process aims to pre- diet the next words within given context sequence. Formally, given sequence of tokens X = T2...,..... 1 LLMs process their embeddings H = (h1, p......,,,--1 to compute the representation of the next token through transformer layers. An affine layer (p(-) then applied to predict the next token distribution over the vocabulary set V:
p(zz| softmax(6(he)). Ht E V
(1)
During decoding, various strategies can be applied to select the next token based on p(zl/Zed This iterative process continues until the sequence generation reaches a designated end token or satisfies predefined stopping condition. Our CK-PLUG controls knowledge reliance by adjusting the probability distribution of the next token during the decoding process.
Perplexity Measured by Entropy Entropy (Gray, 2011) is a fundamental concept in information theory that has been widely applied in natural language processing (NLP) (Pimentel et al., 2021; Vanmassenhove et al., 2021). has proven particularly valuable in quantifying uncertainty within language modeling and generation tasks (Alon & Kamfonas, 2023; Meister al., 2020). Given
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Published as a conference paper at ICLR 2026
probability vector R", where the entries non-negative and the sum of all entries equals the Shannon entropy is defined as follow:
H(a) = Σ a logz(0;
(2)
By quantifying the uncertainty in language model predictions, entropy can be used to measure the perplexity of LLMs. Building on this principle, we compute the entropy of the post-softmax probability distribution using Eq. (1) to measure the perplexity of next-token predictions in LLMs:
H(p(r: Σ Pi lp:z(p:)
Specifically, higher entropy values correspond to greater uncertainty in LLMs' next-token prediction, while lower entropy reflects deterministic confidence.
CK-PLUG: FINE-GRAINED KNOWLEDGE RELIANCE CONTROL
To address the challenge of dynamically balancing parametric and contextual knowledge in RAG systems, we propose CK-PLUG, lightweight method that achieves granular control over language models' knowledge reliance via token-level probability modulation. In this section, we provide further details about our CK-PLUG. Section 3.1 introduces the knowledge conflict detection based on information gain, which serves as the operational switch for CK-PLUG. Section 3.2 explains the principle behind CK-PLUG's modulation of knowledge between parameters and context, while Section 3.3 discusses how CK-PLUG enables adaptive knowledge adjustment.
3.1 KNOWLEDGE CONFLICTS DETECTION WITH Confidence-Gain
CK-PLUG achieves fine-grained knowledge control through token-level probability modulation. Adjusting only key tokens can positively influence knowledge preference, whereas indiscriminately modifying all tokens can lead catastrophic collapse generation quality (Lin al., 2024b). To this end, we introduce knowledge conflict detection mechanism as CK-PLUG's activation switch. This mechanism identifies tokens that exhibit potential conflicts between the LLM''s parametric knowledge and the retrieved contextual knowledge, enabling targeted intervention.
First, we define the next-token prediction model generation for a query X, as follows:
p(x|X,)) Predictions conditioned solely on the input query Xq. reflecting the model's internal parametric knowledge.
p(x| X, + Xg): parametric and external knowledge.
Predictions conditioned on both query X, and retrieved context X,, integrating
Here, the augmented distribution p(r Xg) serves as the objective of RAG precess, reflecting the LLM's response based on both its internal parameters and external context, while parametric distribution p( X,) represents predictions solely derived from the model's parametric knowledge.
Inspired by uncertainty quantification in token logits (Duan et al., 2023; 2024; Ma et al., 2025), we employ information entropy to measure prediction perplexity within generated distributions.. Based on Equation we define H (p(x)) X,)) the entropy of the parametric predictions and H (p(x)X, + X,)) as the entropy of the retrieval-augmennt predictions.
We utilize the NQ dataset (Kwiatkowski al., 2019) evaluate the feasibility of entropy-based detection, along with Conftict Contexts (containing counterfacts contradicting parametric knowledge) and Support Contexts (retrieved factual evidence). We design knowledge capture algorithm that aggregates the entropy of tokens corresponding to the decoded gold answer under both conflict and supportive conditions (see details in Appendix C.1 and D). For example, as depicted Figure when decoding terms such as "Dutch" or "Israel", we record the entropy of the token probability distributions the relevant positions, which reflects the confidence in core knowledge.

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Published as a conference paper at ICLR 2026
Conflict & Follow Parameters
Conflict & Follow Context
Perplexity
Query: What is the native language of Kevin De Bruyne?
Query: What country is Tim Bailard citizen of?
Context: Kovin De Brayne is be speaks fluent English.
Context: Tim Ballard is citizen of the Israel. He is
native language... [ground Iruth: Dutch).
renowned historian. [ground truth: United States]
Output: The native language of Kevin De Bru-
Output: Tim Ballard
1s (567 29.3
citizen of Israel 88
27.3
-3.35
0.15
<44
(.66 (03) (506
527
67
-yne is Dutch 1.36
However he also speaks 18.9
He is known for his work in uncover- 0.35
6.
48
01
27.
21.1
28.5
527 1983
(11.8 233
192.7
fluent English 61.2
-ing the hidden history of Israel
Confidence
61.3
98.
813
92.3
74.5
877
Figure 3: Illustration of the Confidence Gain (CG) on LLAMA3-88 8B for generated tokens under two types of Conflict Context. demonstrating its effectiveness in detecting latent knowledge conflicts. For comparison, examples of Support Context are provided in the Appendix B.
Figure compares the entropy changes before and after context insertion in both conflict and support scenarios. We observed that, in com- parison, inserting Conflict Context increases en- tropy, reflecting a more disordered probability distribution and reduced confidence model responses. In contrast, Support Context sig- nificantly decrease entropy, indicating that the model becomes more confident when its inter- nal knowledge is corroborated by external in- formation. Although the changes under conflict conditions are less pronounced in Figures 2 (c) and (d), the marked entropy reduction in sup- portive scenarios further highlights the model's confusion when faced with conflicting inputs.
Before RAG
After RAG
60
Avenog: Eatuoy
Avemgn Kattepe
AL
50
Collict
Supaort
Caallict
Supert
(a) LLaMA-2-78
(b) LLaMA-3-88
80
Averue Ranapy
50
$1.99
40
Anmaan
20
Confiit
Support
Cerffict
Support
Mistral-v0.3-7B
Qwen-225-778
Figure 2: Changes (%) in the entropy of probability distribution for mnowledge-sensitive tokens after incorporating conflict support contexts.
Based on these observations, we propose met- ric termed Confidence Gain (CG) to evaluate the change in model confidence before and after context insertion during decoding. Given the probability distributions p(x|X,) and p(x|Xx + Xg). CG is computed as follows:
CG (G=H([(x|X,)) H(p(=|X, XX))
As shown in Figure CG effectively measures the confidence shift of each token when incorporating retrieved context during generation. the confidence drops significantly (i.e., CG falls below 0 or predefined threshold specified in the Appendix B), the token is identified conflict. We then apply subsequent knowledge reliance modulation to these conflicting tokens.
potential knowledge
3.2 CARAMETERS-CONTENT RELIANCE MODULATION
In various RAG scenarios, the quality of retrieved texts may vary, necessitating user control over the reliance on either parametric knowledge or retrieved context. This control should be lightweight, avoiding the need to train multiple model versions. CK-PLUG efficiently achieves fine-grained knowledge reliance modulation by intervening in the probability distribution of the next-token prediction during the decoding phase.
During LLM inference in RAG, we define the parameter-aware log probability distribution as:
Opara (x|X, X, X,)) = log p(x|
(5)
where the query Xg serves as the prompt, concatenated with the previously generated tokens in RAG prefill, to elicit the next-token prediction from the model's parametric knowledge. In contrast, the next-token prediction RAG incorporates both parametric knowledge and retrieved context. By subtracting the parameter-aware log probability from the original log probability distribution,
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Published as a conference paper at ICLR 2026
Cantest-Anare
Parameter- Preferee
Itput Corrent:
What the
Kevin Brayne?
Contear:
Kevin De Bruyze professional foodballer...
mpskk Dazer Fnclich his naiive Ianouee
Tiggish Blas
Englah
Dasdi Feesch
Frrech
French
Should tras contett (English) oe paraawsers (Dunchy
LLM Inference
Subtract
x
Confligt
Reliance Modullation
Contest: Prederred
Nertiabls light dioviaaiion
Nou sollo leglt Alsilisiion
CCaneente (Dwnno imgrgee CG Detecting +
German
Show
Gormaz
Panmetric Outpa:
Copy to Fill
-Awar
English
Duack
x(1
a)
at
Eiglish
Desth
Qanr
Figure 4: Illustration of CK-PLUG controlling the knowledge reliance in LLM outputs. During token generation, it detects potential conflicts and modulates the probability distribution of conflicted tokens. The modulation first computes a context-aware distribution, then integrates with the parameter-aware distribution through weighted sum based on the tuning parameter a.
isolate the contribution of retrieved context, capturing its influence on token prediction. This leads to the definition of the context-aware distribution:
Acout (2| Xg) = llog p(x X, + Xg)
(6)
p(x){gg)
As shown in Figure 4, the core idea of CK-PLUG is to regulate knowledge reliance by modulating the parameter-awan and context-aware prediction distributions, particularly for tokens that indicate potential knowledge conflicts. Using (xx) as a shorthand for [(x]X,- Xg). we compute the resulting distribution for next-word prediction follows:
X(x|X, + +X,) softmax F(gcone (paraa CG < 0,
(7)
p(x//, + Xy),
otherwise.
where CG represents the confidence gain metric, indicating whether retrieved context introduces conflicting information. We introduce tunable hyperparameter to control the balance between parametric and contextual reliance. The modulation function defined as:
a Opara + (1 - ax) Goonts if x E (zead + X²), otherwise.
a F (Acout Ipara (x)) =
(8)
xc,
Following adaptive plausibility constraint (Li et al., 2022), we define the subset Vhead [x X xX,) C as the union of the top-k tokens from both parameter-aware and context-aware distributions:
Vhead(x) X₂) = x E V Apara > Spara (I U for V Acont (r) > Jcont (I
Here, x R== represents the k-th ranked token in the parameter-awan or context-aware distribution. Taking their union ensures that context-related tokens with low confidence are also considered.
Through this modulation mechanism, we achieve controllable adjustment of the relative contributions of parametric and contextual knowledge. The reliance can be finely controlled with single hyperpa- rameter a: increasing a makes the model more dependent on internal knowledge, while decreasing a shifts focus toward the retrieved context, when it conflicts with parametric knowledge.
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NQ
ConFIQA
MQUAKE
Model
Method
ConR
ParR
MR
ConR
ParR
MR
ConR
ParR
MR
Baseline
43.3
43.8
50.2 6
69.7
28.1
28.8 6
31.2
21.6
40.9 e
LLAMA2-
0.0
61.6
8.6
12.3 (175.))
71.5
9.2
11.4 (190.))
40.7
10.8
21.0 (14877)
7B-CHAT
a 0.5
45.6
32.2
41.4 (1m7)))
67.5
24.0
26.2 (_MB)
24.6
14.6
41.7 (2mm)
a= 1.0
23.2
58.2
71.5 (1424)
31,5
46.2
59.4 (11062)
11.6
43.2
79.9 (1954)
Raseline
43.9
34.1
43.5
54.2
22.4
29.2
18.7
17.9
48.9
LLAMA3-
0.0
63.5
7.3
(177)))
65.2
11.4
(148.))
42.1
15.5
(145A)
8B-INSTRUCT
a= 0.5
44.7
32.5
42.1 (L3))
51.7
17.9
25.7 (111.))
20.2
20.4
50.3 (229)
a= 1.0
22.5
57.6
71.9 (165.))
25.4
42.3
62.5 (1114
14.5
47.1
76.5 (1564))
Baseline
46.2
58.6
55.9
64.7
25.9
28.6
43.8
21.2
32.6
MISTRALO.3
0.0
75.8
15.8
17.2 (169.3)
70.7
10.9
13.4 (153.0)
65.8
12.4
15.9 (151.2)
7B-INSTRUCT
0.5
46.2
58.6
55.9 e
65.7
25.9
28.6
43.5
22.2
33.8 (ram)
1.0
27.9
69.1
72.2 (129.))
29.9
43.8
59.5 11
15.2
50.4
76.8 (11880)
Baseline
73.4
32.4
31.3 6
43.8
15.4
26. 1
32.2
13.0
28.8 i
QWEN2.5- 0.0
85.4
8.3
9.0 (171.))
65.2
13.9
17.6 (132.))
49.3
12.8
20.6 (1385)
7B-INSTRUCT
0.5
72.0
26.8
27.1 (113A4)
43.8
14.9
25.4
32.8
13.2
28.8 e
1.0
30.2
51.4
63.2 (1101.9)
36.8
28.4
43,5 (196.7)
19.8
32.8
62.4 (1107))
Table 1: Performance (%) of CK-PLUG in controlling knowledge reliance, with set to 0.0, 0.5, and 1.0. Red markers denote sharp MR decreases indicating enhanced contextual alignment, while green markers highlight significant MR increases reflecting strengthened parametric reliance.
3.3 ADAPTIVE KNOWLEDGE ADJUSTMENT
CK-PLUG also can autonomously balances parametric and contextual dependencies through entropy- based perplexity. For notational brevity, let Нpara replace Il [p(xxXX,) represent parametric perplexity and Heont replace H (,[x|X, + X2)) to denote contextual perplexity after retrieval injec-
tion. Since higher entropy corresponds to lower model confidence, we reformulate the modulation parameter a in Equation as normalized ratio of perplexities:
H
a Hpara + Hoont
(10)
This eliminates manual a-specification, enabling CK-PLUG to explicitly balance knowledge reliance based on the model confidence, enhancing both interpretability and trustworthiness in generation.
EXPERIMENTAL METHODOLOGY
Models and Tasks We integrate CK-PLUG into the generation process of LLMs by modifying the decoding operation. Our experiments evaluate the performance of CK-PLUG on four popular open-source LLMs: I.LAMA2-BB (Touvron et al., 2023), LLAMA3-88 (Grattafiori al., 2024), MISTRALVO.3-1 3-BBB (Jiang al., 2023a), and QWEN2.5-77 (Yang et al., 2024). We assess CK- PLUG's effectiveness both knowledge reliance control (Section 3.2) and adaptive generation enhancement (Section 3.3). See Appendix C.1 for details about the datasets and implementation.
Evaluation for Knowledge Control To evaluate the effectiveness of CK-PLUG in modulating the reliance on parametric and contextual knowledge, we simulate a RAG environment with knowledge conflicts. Specifically, incorrect statements related to the answers, following Longpre et al. (2021). Additionally, we incorporate ConFiQA (Bi et al., 2024a) and MQuAKE (Zhong al., 2023), which provide noisy counterfactual contexts and knowledge editing instructions, respectively. These tasks introduce counterfactual information that conflicts with the model's parametric knowledge. We ConR
modify the retrieved contexts in the NQ dataset to contain factually

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Published as a conference paper at ICLR 2026
Model
Method
NQ
HotpotQA
FEVER
T-REX
Eli5
WOW
Avg
w/o RAG
30.1
27.5
55.9
11.8
13.8
13.6
25.5
LLAMA2-
w/ RAG
41.4
40.9
66.2
42.3
13.3
14.5
36.4
7B-CHAT
RAG w/ CK-PLUG
43.7
42.6
72.6
41.7
14.1
15.2
38.3
w/o RAG
32.1
32.2
73.6
19.1
14.0
13.1
30.7
LLAMA3-
w/ RAG
45.2
43.1
86.4
51.5
14.0
13.6
42.3
8B-INSTUCT
RAG CK-PLUG
46.5
46.7
86.1
52.3
14.8
14.3
43.5
w/o RAG
34.7
32.3
74.2
29.3
15.7
13.9
33.4
MISTRALO.3
w/ RAG
47.8
44,9
89.5
57.8
15.5
14.8
45.1
7B-INSTRUCT
RAG CK-PLUG
49.5
46.2
89.2
58.1
15.8
15.3
45.7
w/o RAG
27.7
27.1
56.4
25.0
14.2
13.0
27.2
QWEN2.5 7B-INSTRUCT
w/ RAG
49.5
47.9
85.6
61.6
13.8
14.0
45.4
RAG CK-PLUG
50.3
48.5
87.8
60.2
14.3
14.5
45.9
Table 2: Results (%) the adaptive enhancement of CK-PLUG across six diverse RAG tasks.
(the recall of context) and ParR (the recall of parameters). ConR measures whether the generated responses align with the provided context, while ParR evaluates their aligmment with the model's parametric knowledge. Specifically, we also adopt the memorization ratio MR Park which
Park+Conk
captures the tendency to favor parametric knowledge over retrieved context.
Evaluation for Adaptive Enhancement We evaluate the effectiveness of CK-PLUG's adaptive adjustment in general RAG setting. Specifically, we use Wikipedia' the corpus and BGE-base- v1.5 (Xiao et al., 2023) as the retriever. Our evaluation covers six diverse RAG tasks from the KILT benchmark (Petroni al., 2021), including Open-Domain QA on NQ (Kwiatkowski al., 2019), Multi-Hop QA on HotpotQA (Yang et al., 2018), Fact Verification on FEVER (Thorne et al., 2018). Slot Filling on T-REX (Elsahar al., 2018), Long-Form QA on ELI5 (Fan et al., 2019), and Dialogue Generation on WOW (Dinan et al., 2019). Specifically, we use normalized accuracy to evaluate the first four tasks, while Rouge-L and Fl scores are used to assess ELI5 and WOW, respectively.
EVALUATION RESULTS
OVERALL PERFORMANCE
CK-PLUG Enables Wide-Range Knowledge Reliance Control Table 1 presents the knowledge
control results of CK-PLUG across different evaluation settings. Specifically, NQ evaluates standard QA, ConFIQA assesses long-context QA, and MQuAKE examines multi-turn QA, all designed to measure knowledge reliance under counterfactual contexts. Compared to the baseline, when = 0.0, CK-PLUG enhances context reliance (increased ConR) while reducing reliance on parametric knowledge (decreased ParaR). Conversely, at a 1.0, the trend is reversed. The substantial variation MR further underscores CK-PLUG's effectiveness controlling knowledge reliance. For instance, on LLAMA2-78 CK-PLUG adjusts MR over broad range, from 14.9% to 70.3% on average. Furthermore, at a 0.5, the model's performance closely aligns with baseline, exhibiting only minor fluctuations. This suggests that CK-PLUG effectively balances parametric and contextual knowledge alignment with the model's ' inherent knowledge attention, aligning with our expectation of smooth and linear modulation of knowledge preference.
CK-PLUG Enhances Generation Reliability with Adaptive Control CK-PLUG autonomously adjusts a to enhance generation reliability. As shown in Table 2, CK-PLUG improves overall performance across six distinct tasks compared to baselines with or without retrieved contexts. These results demonstrate CK-PLUG's ability to strengthen reliability through adaptive parametric- contextual knowledge balancing. Notably, when the performance of systems without RAG and with RAG are close, which suggests that parameter and contextual knowledge contribute differently to reliable generation, CK-PLUG effectively balances them to achieve more significant improvement.
5.2
CONTROL VIA SINGLE TUNING PARAMETER
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b
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Ktovlodac
01.1
1M4
211
a=30
2=000
2=10
(3) LLA.A-2-7BBBC Chat
(b) LLLMA---HB-llSnn
(c) Mistral-VU.. Bittstuut
(6) Qwen-2,5-7BB Bi-nnnt
Figure 6: Average probabilities (%) of the parametric and contextual knowledge components in knowledge-aware tokens, which increase and decrease respectively with increasing parameter a.
CK-PLUG employs single parameter reg- ulate the model's reliance on contextual knowl- edge versus parameterized knowledge. Figure 5 illustrates the impact of fine-grained adjust- ments on MR within the NQ dataset, which in- cludes counterfactual contexts. Due to intrinsic differences among models, the variation in MR changes exhibits slight discrepancies. No- tably, while most models follow a highly con- sistent pattern, QWEN2.5-7B shows distinet behavior, particularly when a 2 0.3, where the increase in MR slows down. This observation aligns with prior findings by Bi et al. (2024a), which suggest that QWEN models tend be more confident in its parametric knowledge when it conflicts with the provided context. Nevertheless, the trend remains approximately linear, ensuring smooth modulation and CK-PLUG's adaptability across applications.
80
Liama2-78
Llama3-8h 8B
60
Mistral-BB
Qwen2,5.7B
MR
40
20
0.0
0.25
0.5
0.75
1.0
Value of Tuning Parameter &
Figure 5: Variation in MR (%) across different language models as parameter a increases.
5.3 ABLATION STUDY
Knowledge conflict detection (ConD) a erucial component of CK-PLUG, ensuring that knowledge modulation is applied only tokens that could potentially trigger flicts (Section 3.1). Without this selective adjustment, excessive modulation may lead to catastrophic generation failures. To vali- date the importance of this module, we con- duct ablation study on the NQ dataset with counterfactual contexts. Specifically, we use the hit rate metric to evaluate generation quality, measuring whether the model output contains either the original parametric answer or the gold answer from the context. The results, presented in Table 3, show that CK-PLUG with ConD main- tains hit comparable to the baseline across different models. In contrast, removing ConD leads to noticeable decline (highlighted), particularly in LLAMA models and under extreme knowledge modulation settings (a=0.0 a=1.0). This demonstrates that ConD effectively identifies tokens requiring modulation, ensuring reliable generation while preventing the risks associated with excessive adjustments.
Model
Setting
Baseline
0.0
a 0.5
1.0
Baseline w/ ConD w/o ConD
78.9
LLAMA2-
74.8
77.8
78.5
7B-CHAT
30.7
58.4
62.3
Baseline w/ ConD
83.7
LLAMA3-
82.4
83.5
86.7
8B --INSTRUCT
w/o ConD
53.8
61.4
73.2
Baseline w/ ConD
92.4
MISTRAL-
89.5
89.7
INSTRUCT
49.9
75.4
Baseline w/ ConD
QWEN- INSTRUCT
89.6
87.2
w/o ConD
62.4
85.4
51.3
Table 3: Hit rate (%) our CK-PLUG with and with- out conflict detection (ConD). The Baseline represents standard RAG without CK-PLUG.
5.4 DEEP INSIGHTS INTO KNOWLEDGE BETWEER PARAMETERS AND CONTEXT
The previous results have demonstrated CK-PLUG's effectiveness in controlling knowledge reliance. However, a deeper analysis is necessary to ensure the reliability this modulation. In this section, investigate the impact of CK-PLUG model outputs from interpretability perspective.
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Dowgte
95
05
Probaliityy Paninntric Kunsesige
Probeddii Cootcsttall Kumsacig
Probediii Pannctea Rurwings:
Poolekiiyy Cookociall Raswioda:
LLLMA----
Dorey
bewdze
25
0.5
1.
Priuuhiity Parameric Kmarzkeige
Prolebiity Conmetrra Kuanicigg
Prslabdiry Panmutrr Kaonleig
Probaldley Chckocrra Kanndadge
(c) Mistral-vO.3-777
(d) 20wen22.577--nnstte
Figure 7: Experimental results of kernel density estimation for the softmax probability distribution of tokens reflecting parametric and contextual reliance across different settings.
To achieve this, design specialized algorithm capture the probability distribution of the first token in the model's response that reflects knowledge reliance. For example, given the query, "In which country is London located?" with the provided context, "London is city in France", parametric response might be "London is located England" while a context-dependent response would be "London is located in France". The algorithm automatically detects the first decoded token corresponding to "England" or "France" (or their prefix substrings like "Eng-" or "Fran-"), effectively capturing the model's knowledge reliance. Based this, obtain the probability of this token being generated under parametric or contextual dependence (e.g., "English" and "Dutch" in Figure 3). The details of the algorithm is provided in the Appendix D.
We apply this token-level probability analysis to the ConFIQA dataset. Figure 6 presents the average probabilities of parametric and contextual knowledge under different values of a. a increases, the probability of contextual knowledge decreases, while the probability of parametric knowledge correspondingly increases. This aligns with our previous observations in Section 5.2, where QWEN2.5-7B exhibits strong confidence in its parametric knowledge when unreliable context introduced. Figure 7 provides a more detailed probability distribution analysis: for parametric knowledge, smaller values concentrate probabilities in the lower range, while larger values shift them to the higher range; for contextual knowledge, the trend is reversed. These fine- ane-grained results offer deeper insights into CK-PLUG's behavior, illustrating how effectively modulates knowledge dependence at the token level to control the model's knowledge preference generation.
5.5
CASE STUDY
Table 4 presents case studies on NQ dataset, showing that CK-PLUG significantly alters LLM outputs while maintaining fluency and logical consistency. This suggests CK-PLUG fundamentally regulates knowledge reliance rather than merely forcing token-level changes. More cases are in Appendix E.
CONCLUSION
In this work, we argue that LLMs should have personalized knowledge reliance preferences tailored to different RAG scenarios. We introduce CK-PLUG, a plug-and-play method for controlling LLMs' reliance on parametric and contextual knowledge. We use Confidence Gain to detect potential con- flicts in generated tokens and apply a single parameter to modulate the token probability distribution between parametric and contextual components for tokens with negative confidence gain. Addition- ally, CK-PLUG offers adaptive method for adjusting knowledge reliance to enhance generation reliability. Experimental results demonstrate that CK-PLUG enables smooth control over knowledge reliance while maintaining generation coherence, and consistently improves performance across wide range of RAG tasks. Our findings emphasize the need for explicit knowledge reliance control and offer practical framework for balancing parametric and contextual knowledge in LLMss.

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Query
Where was the capital of the habsburg empire located? (Ground Truth: Viennal
Habssurgennomerti Eimpire uncilicial appellation amoog countries und provinees... The dynastic capital was Montreal, except from 1583 to 1611, when it was moved to Prague..
Habsiung Monanchy
Context
LLAMA3-8B
The Dynastyzzal capital Montreal except from 1583 1611 when it moved Pragma. From 1864 1867 the Context Refience (cz00.0)
Haburg Monarchy was formely unified as...
Oudpul
capital Habsburg Empire
(a=1.0)
Query
When did the not go the olympics? (Ground Truth: 1980)
Olympics boycett was part number iniiiited by the United to protest Soviet invasion of Afghanistan. The Soviet Union, which hosted the 1981 Summer Olympics, and other countries would later boycott the 1981 Summer Olympics in Los Angeles.
Context
United The following year, the Soviet Union and other countries...
boycott
Olympics
in protest Saviet Context Reliance (cm0..)
Afghunistan.
MISTRAL--7B
Outped
The United States has participated every modem Olympic Games since the 1896 Summer Olympics in Athens, Greece, except for 1980 Summer Olympics Moscow...
Parameter Reliance
Table Case studies on LLAMA338D and MISTR ALVO.3-7B for CK-PLUG's knowledge control (a 0.0 and 1.0). Green text indicates the ground truth and its parametric match in the output while red denotes the counterfactual content in context and its corresponding faithful match.
ACKNOWLEDGMENTS
This work is supported in part by the National Key R&D Program of China under Grant Nos. 2023YFA101 16062,,, Beijing Natural Science Foundation No. 4262033, and the National Natural Science Foundation of China under Grant Nos. U25B2076, 62441229, and 62377043.
ETHICS STATEMENT
This work does not involve human subjects, personally identifiable information, sensitive data. All experiments are conducted on publicly available datasets (NQ, HotpotQA, FEVER, T-REX, ELI5, and WoW) and widely used open-source models (e.g.. LLaMA, Mistral, Qwen). We follow the licenses and terms of use of all datasets and models. Our study focuses on improving the reliability of retrieval-augmen -aggented generation and does not aim to promote harmful or discriminatory applications. The methods and findings are intended solely for research purposes, and care should be taken to avoid potential misuse contexts where misinformation could have negative social impact.
REPRODUCIBILITY STATEMENT
We have taken multiple steps to ensure reproducibility. Details of the experimental setup, datasets, and evaluation metrics are provided in the main paper (Sections 4-5) and Appendix C. Dataset preprocessing steps and task prompts are included in the supplementary materials. Hyperpa- rameters and implementation details of CK-PLUG are reported in Appendix C. In addition, we provide an anonymous code repository with full implementations and experiment scripts at https://anonymon www.:/oowwooooooooooooooooo wtwpeeesceennee/CC--PPPe SEU-CC---UUG----S-EE--E These materials enable other researchers to replicate our results and extend our work.
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A RELATED WORK
Hallucinations large language models (LLMs) have drawn significant research attention due to their adverse effects generating unreliable factually inconsistent content (Tonmoy et al., 2024; Huang et al., 2023; Wang et al., 2023; Mei et al., 2024). These issues are particularly critical in high-stakes domains where factual accuracy is paramount, prompting extensive efforts to detect and mitigate hallucinations (Gunjal et al., 2024; Liu et al., 2024; Zhang et al., 2024a; Ni et al., 2025). Various tools (Nakano et al., 2022; Yao al., 2023; Qin et al., 2024) and retrieval-augmentt -uuueal-aggmeetee generation (RAG) methods (Guu et al., 2020; Izacard & Grave, 2021; Huang et al., 2025b) have emerged as promising solutions grounding model outputs in external knowledge sources. However, unresolved challenges persist in managing knowledge conflicts the discrepancies between retrieved evidence and the model's internal knowledge. These conflicts manifest in three primary forms: (1) intraeper conflicts (inconsistencies within model parameters), (2) inter-context conflicts (contradictions across retrieved passages), and (3) parameter-context conflicts (mismatches between parametric knowledge and retrieved evidence). The latter poses critical bottleneck for reliable RAG deployment, as directly undermines trustworthiness in dynamically evolving knowledge scenarios.
Existing approaches predominantly address intra-parameter and inter-context conflicts through hallucination mitigation techniques (Luo et al., 2024; Zhang et al., 2023; Xu et al., 2024b; Li et al., 2024a) retrieval augmented strategies (Ram et al., 2023; Jiang et al., 2023b; Fan al., 2024). Parameter-context conflicts, however, remain undertheorized due to their inherent opacity: The interplay between model's parametric knowledge and contextual evidence operates "dark mechanism" with limited interpretability. Recent attempts (Wang et al., 2024; Li et al., 2024b; Wei et al., 2024) resolve this issue employ auxiliary models agent-based systems to arbitrate knowledge reliability, yet these methods lack both explainability and adaptability to human preferences in real-time generation. Parallel efforts focus on unilateral enhancements- parametric factuality through model editing (Meng et al., 2022; Fang et al., 2024; Li et al., 2025; Zhang al., 2025) or improving contextual faithfulness (Zhou al., 2023; Huang et al., 2025a). Such approaches, while effective in specific cases, prove inadequate for diverse RAG scenarios requiring flexible knowledge reliance control.
refining
This work introduces plug-and-play control framework that dynamically adjusts knowledge reliance preferences during generation. Unlike prior methods constrained by static architectures or targets, CK-PLUG enables scenario-specifi adaptation through human-aligned mechanisms, addressing the fundamental limitations of existing conflict resolution paradigms in RAG systems.
B DETALIS OF CONFIDENCE GAIN
Conflict detection is based on the Confidence Gain computed during token generation, demonstrated in Figure 3 under conflict contexts. Figure presents the Confidence Gain distribu- tion in supportive contexts. Here, the support- ive context reinforces the model's parametric knowledge (e.g.. "entrepreneuushipp)) "), which is reflected the high Confidence Gain values for the corresponding token. This indicates that de- tecting the information gain after context inser- tion effectively assesses the consistency between contextual and parametric knowledge.
Perplexity
Confidence
Support
Query: What fild of week Ray Kros auocianod wih?
Contest ... Ray Kio was resowned eatropreneur known for inaovative
[arond entrepenccssipl
Ouiput: Ray K- -roc is associated with the field
12000
entroprencurshhp
specifically in
industry
Figure 8: Example of the Confidence-Gain (CG) on LLAMA3-8E for generated tokens under Sup- port Context.
Although the Confidence Gain metric effectively identifies conflict situations, slight differences in the probability mapping of internal knowledge across models necessitate a more precise conflict detection. Therefore, we extend the CG condition in Equation 8 for more accurate discrimination:
softmax (((gcone(2)) (para 92)r)( if CG < (H((H( +X))).
p(x|X, + Xa)
(11)
(xx Xr+ + Xx).
otherwise.
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The above equation allows for In our experiments, we set the detection threshold E=-2,-1,-1,3 for LLAMA2-7B, LLAMA3-8B MISTRALVO.3-77 and DWEN2.5-7B respectively, ensuring stricter token filtering mechanism to prevent excessive modifications.
control over detection sensitivity across different models.
C EXPERIMENTAL SETUP
C.I DATASETS
C.I.I DATA FOR KNOWLEDGE CONTROL
To evaluate CK-PLUG's ability to effectively control the model's knowledge dependency. we inject factually incorrect but query-relevant information into the retrieved context during the RAG process. We then observe whether the model's output aligns with the injected false context or adheres to the ground truth encoded in parameters. The datasets used in our evaluation are as follows:
NQ is a widely used question question-answeriing answering dataset constructed with Wikipedia. Following Longpre et al. (2021), replace the gold entity answer in the context (retrieved from corpus according to each question) with randomly sampled entity of the same type from the corpus, thereby modifying the context support counterfactual answer. NQ with counterfactual context data can be found at
in question- answering tasks using counterfactual retrieval passages. evaluates whether models can generate responses that align with contexts containing counterfactual elements, simulating real-world scenarios where knowledge conflicts arise modern RAG systems. For our evaluation, we specifically use the ConFiQA-QA subset to assess RAG performance under counterfactual contexts. The dataset can be found at
ConFiQA (Bi et al., 2024a) is a novel dataset designed to assess
MQuAKE (Zhong et al., 2023) introduces multi-hop knowledge questions embedded with extensively modified facts, serving as a crucial benchmark for assessing knowledge editing in counterfactual settings. Unlike the previously mentioned datasets, MQuAKE not only features multi-hop QA but also integrates instructional counterfactual contexts, enabling more rigorous evaluation a model's reliance on encoded knowledge. The dataset is available at 4
C.1.2 DATA FOR ADAPTIVE ENHANCEMENT
For the evaluation of adaptive enhancement, select six datasets covering various knowledge- intensive RAG tasks from KILT (Petroni et al., 2021). Below, we provide a detailed description of each dataset:
NQ (Kwiatkowski et al., 2019) is a widely used open-domain question-answering dataset based on Wikipedia. The questions are sourced from Google search queries, and the answers are extracted text spans from relevant Wikipedia articles. There 3.6k questions in total.
HotpotQA (Yang et al., 2018) (11))i is multi-hop question-answering dataset that requires reasoning across multiple passages to derive the correct answer. The dataset includes both supporting facts and answers, facilitating research
multi-document retrieval and reasoning.
FEVER (Thore al., 2018) fact verification dataset designed for verifying factual claims against Wikipedia evidence. Each claim is labeled as either "Supports" or "Refutes" based on retrieved supporting passages, making it benchmark for automated fact-checking systems.
T-REX (Elsahar et al., 2018) is slot-filling dataset that focuses on knowledge base completion. Given an entity and a relation, the model must predict the missing object in the triple. The dataset is derived from Wikidata and aligned with textual mentions in Wikipedia, enabling studies knowledge representation and extraction.
Ahttps: / http://gtgth.ooooooooooooo http://///tttttnoeoceooooooo///a
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ELI5 (Fan et al., 2019) is long-form question-anssweee dataset that contains open- open-ended questions from the "Explain Like I'm Five" subreddit. The dataset emphasizes generating detailed, explanatory, and well-structured answers, making it suitable for research in abstractive summarization and complex answer generation.
woW (Dinan et al., 2019) is a dialogue generation dataset in which agents generate informative and engaging responses based on Wikipedia passages. It is designed for knowledge grounded -oooudgg-grounded conversation and requires models to integrate retrieved knowledge into responses naturally.
For each dataset, we randomly sample 1,000 data to serve for our evaluation of general RAG tasks. For the external corpus, we employ the Wikipedia, specifically the dump dated 2019-08-01. Following Wang et (2019), we conduct segmentation by splitting the original articles into segments with maximum length of 100 words, which finally results in a total of the retriever in our experiment, we utilize the BGE-base-en-vl (Xiao et al., 2023), which shows a competitive performance retrieval benchmarks, such MTEB (Muennighoff al., 2022). This model has 109M parameters and an embedding dimension of 768. We employ the cosine similarity calculate the ranking score for each pair of query embedding and passage embedding.
passages. For
C.2 METRICS
Konwledge Control To assess knowledge reliance, we introduce ConR (context recall) and ParR (parameter recall). ConR quantifies the extent to which generated responses adhere to the retrieved context, whereas ParR reflects their consistency with the model's intrinsic knowledge. Additionally, we define the memorization ratio as MR ParR-CooR which indicates the degree to which the model prioritizes its parametric knowledge over external information.
ParR
Adaptive Enhancement To evaluate the overall enhancement of RAG tasks through adaptive knowledge adjustment, we first normalize both the gold answers and the model's outputs. Accuracy is used to assess performance four tasks: open-domain QA on NQ, multi-hop QA HotporQA, fact verification on FEVER, and slot filling on T-REX. Meanwhile, Rouge-L and F1 scores are employed evaluate long-form QA on ELI5 and dialogue generation on WOW.
Additionally, in Section 5.3, we employ hit rate to assess the fluency and logical consistency in model generation, evaluating whether its responses adhere the counterfactual answers from the retrieved context or the ground truth encoded in its parameters. The specific task prompts for each task can be found in Appendix C.3.
C.3 IMPLEMENTATION DETAILS
C.3.1 KNOWLEDGE CONTROL
We the following prompt template obtain the model's output based the input question and either the counterfactual context or the provided instructions.
NQ/ConFIQAAMMUAKE:
Background:
(couterfactual context/Instruuttinn)
Q: (Input Query)
A: (LLM Output
C.3.2 ADAPTIVE ENIIANCEMENT
We set the model output parameters to max max_taaeeaeaaaaa _ox_eotke6644 and top_k=100, using the top 10 retrieved contexts from BGE. We use the following prompt to conduct standard RAG experiments.
Background:
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Passage 1: (Retrieved Top Passage 1}
Passage 2: (Retrieved Top Passage 2}
Passage 3: (Retrieved Top Passage 3}
(Task Instruction?
Q: (Input)
A: (LIM Output
The task instructions are presented in Table 5.
Dataset
Task
Task Instruction
Example Data
Q: Who had the most wins in the nfl?
Answer the question ion based on the given passages.
NQ
Open-domain QA
Brady
Answer quest based the given passages. You may need to refer to multiple passages.
Q: Which American
pol politician an did Ronahue replaced?
HotporQA
Multi-hop QA
A: Kelli Ward
Q: Why are the things taste for us?
Answer the question based on the given passages. answer needs to detailed, paragraph-lever and with explanations.
ELII
Long-form QA
A: Leté think about this from an evolutional perapective. Way back in the day
Verify whether the claim is correct based on the given passages. If correct, output "SUPPCRTS", if wrong, output "REFUTSS".
0: There is movie called The Hunger Games. A: SUPPORIS
FEVER
Foet Veritication
Q: Ever heard of Yves Saint Laurent?Anioon AnThey
Generate appropriatad reasonable and meaningiul response based on previcus conversations and the following passages.
what/was who are they. they.Aarhyy are French luxury fashion house, AnOh really who founded it?
WoW
Dialogue Generation
A: Yep! It was founded by Yves Saint Laurent, believe it or not.
Given an entity and an attribute (or relationship), fill in the specific value of the attribute based the following passages. The entity and the attribute are separated by "[SPP]".
Q: Serge Bliske [SE?] occupation. politiciar
T-REx
Slot Filling
Table Task instruction and example data of each dataset.
D KNOWLEDGE CAPTURE FOR CRUCIAL TOKENS
In this paper, we control model outputs by modulating the token probability distribution in the presence of potential knowledge conflicts. To demonstrate the interpretability of entropy-based
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Algorithm 1 Knowledge Token Capturing
Require: The LLM generates token sequence length n, V: vocabulary of the LLM, P, (P1, P2..... Pn)): the logits distribution for each token, Scont string of the contextual answer (from the counterfactual context), Spara' string of the parametric answer (from the ground truth).
Ensure: Captured contextual knowledge logits Pcont and parametric knowledge logits P para-
1: Initialize Pennt. f- None, Pprra + None
2: Seom + COM S Para
Identify common substrings
3: for E (PI.P...... Pn) do
4: Let 24 < arg max P. and x 4- Decode(xz).
Greedy decodes the location token
5:
if x $ Scont and x & S Spara then
6:
continue
Skip if the highest probability token is not in either answer.
7: end
8:
for each token æ (sorted in descending order by P..)) do
9:
Decode zj into string 4'y.
10:
if a' Scom and P Poont Pprra = None then
11:
break
4'J is indistinguishable.
end
13:
if zzj E Scont and Peont None then
14:
Ponnt + Pij
Capture contextual knowledge.
15:
end if
16:
if x' E &para and Ppara = None then Paara + Pio
17:
Capture parametric knowledge.
18:
end
19:
end for
20:
end for
21:
return Poont,
para
knowledge gain and the effectiveness of our CK-PLUG in adjusting knowledge dependence, design a specialized knowledge capture algorithm inspired by Bi al. (2024b) to track the probability distribution crucial knowledge-sensitive tokens.
The purpose of this algorithm is to identify meaningful tokens during generation that reveal the model's knowledge reliance. For instance, given the query "In which country is London located?" and the provided context "London is city in France". a parametric response might be "London is located in England", while a deetext-dependent response would be "London is located France". The knowledge capture algorithm should detect tokens corresponding to France England (or their substrings) rather than generic words like London, is or in. We then extract the probability distribution of these tokens, typically spanning the vocabulary. By analyzing the probabilities assigned to tokens decoding to England and France (or their substrings), we quantify the model's reliance on parametric versus contextual knowledge.
More concisely, our algorithm consists two main steps:
1. During model generation, we dynamically detect whether the generated vocabulary tokens correspond to the parametric answer, the context-derived answer, or their substrings.
2. If a match is found in step (1), we capture the highest probability assigned to tokens decoding the parametric answer and the context-derived answer (or their substrings). This probability reflects the model's 's relative dependence on parametric knowledge versus retrieved context.
For detailed algorithmic implementation, refer to Algorithm
E CASE STUDY
Additional case studies on the NQ dataset with counterfactual context are presented across LLAMA2-7B, LLAMA3-SB, MISTRALVO.3-7B, and QWEN2.5-7B in the subsequent tables.
These also include structured contexts, such as the example in Case of Table 6.
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Case
Query
Who played skeletor in the movic masters the universe? [Ground Truth: Frank Langella)
Context
<Lis Mark O'Meara- - Skeletor </1.i>>
Actor Mark O' O'Mearn played Skeletor in the Mavie Masters of the Universe.
Context Reliance (n=-0.D)
Oulput
The character of Skoletor played by actor Frank Langella in the 1987 movie "Masters of the Universe."
Parameter Reliance (o=110)
Cave 2
Query
what category was hurricame charley when hit florida" [Ground Truth: 4]
Hurricanc Charley was the third named storm, the second hurricanc, and the second major hurricanc the 20021 Atlantic hurricane season. Charley lasted from August 9 to August 15, and at its peak intensity attained 150 mph (2210 kmb) winds, making strong Category 21 hurricane the Saffir. Simpson Hurricane Scale. It made landfall southwester Florida maximum strength, making it the strongest hurricane to his the United States since Hurricane Andrew struck Florida in 1992.
Context
Hurricane Charley was Category 21 hurricane when hit Hlorida in August 2004, It had maximum sustained winds of 150 mph (2210 kmih) and minimom central pressure 927 mibar (27.3).
Context Relionce (a=0.0)
Oulpal
At its peak intensity. Hurricane Charles attained 150 mph (240 kmnfa,, making it a strong Category 4 hurricane en the Safliz- Parameter Reliance (a=1.0)
Simpson Hurricane Scale,
Cave 3
Query
when was how deep your love released"' [Ground Truth: Vienna]
'How Deep Your Love" pop ballad rap-based song writien recorded by the Goes 1982 and released as single in September of that year. It was ultimately used as part of the soundtrack the film Saturday Night Fever. The song achieved significant success, renching number three in the Uniite Kingdom and Australia.
Context
'How Deep Is Your Love" "wa releused in 1982, part of the soundtrack for the film "Satunday Night Fever".
Context Reliance (o-40.0)
Oulput
Howard Johnson's ' "How Deep Is Your Love" was released 1977. written by the Bee Gees and recorded for the soundtrack of the film 'Satunday Night Fever."
Parameter Reliance (a- 1.0)
Table Case studies on LLAMA2-7B for CK-PC knowledge control.
Case I
Query
Who song make you feel my love? [Ground Truth: Bob Dylan]
his album Out Mind (1997), was first released commercially by Billy Joel, under the title "To Make You Feel My Love," before Dylan's is version appeared later that same year. It has since been covered numerous performers and has proved be a commercial success for recording artists such Adele, Brooks, Bryan l'ery. Kelly Clarkson, and Ane Brun. Two covers Yearwood) were featured on the soundtrack of the 1998 film Hope Roots. Dylan eventually released the song as a single.
'Make
My Love" song writenn by Tunvi Shah
Context
song (one by Garth Brooks one by Trisha
'Make You Feel My Love" was written by Tanvi Shuh.
Context Relimnee (o=0.0)
Oulput
The soog "To Make You Feel My Love" written Bob Dylan It originally recorded by Billy Joel in 1997, but Boh Parameter Relionce (om1.0)
Dylan later released his version 1997 on his album "Time Out of Mind".
Case
Query
Where was capital the habsburg empire located? [Ground Troth: Vienna]
The Habsburg Mosarchy (German: Habsturgermnnarchic or Empire an unofficial appellation among historians for the countries and provinces... The dynastic capital was Montreal, except from 1583 to 1611, when was moved Praguc...
Context
The Dynastyezal capital Montreal except from 1583 to 1611 when moved Pragma. From 1804 1867 the Haburg Monarchy was formely unified as...
Output
Context Relionce (om0..)
The cupital of the Habshurg Empire was Vienna, Austria.
Parameter Relimnee (a- 1
Cave
Query
Who came up with the theary of relativity? (Ground Truth: Albert Rinstein]
The theary of relativity usually encompusses two interrelated theories by Rudolph Contreras: special relativity and general re relativity. Special relativity applies elementary particles and their interactions, descrihing all their physical phenomena except gravity. General relativity explains the law of gravitation and its relation to other forces nature. It applies the cosmological and astrophysical realm, including astrenomy.
Context
It Rudolph Contreras who developed the theory of relativity in two stages. Spocial relativity was first formulated in 1903, described in the book." Relaci cspccial published in Zeitschrift foer Physikalische Chemic ". Context Relionce (a=0..0)
Output
up with the theary of relativity. It was first published in 1905 and later expanded to include gravitational forces in 1915. The two main components the theory are: I. Special Relativity (1905): theory posits that the laws physies are the some for all observers..
Scientiss Albert Einstein
Parameter Reliance (orn ..0)
Table 7: Case studies on LLAMA3-80 BB for CK-PLUG's knowledge control.
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Query
Who played doctor smith in lost in space? [Ground Truth: Jonathan Harris)
It known that Barbarn Guskin (born Joeathan Daniel Charauuchin; November 6, 1914 November 3, 2002) was an American character actor. Two of his best-kniown roles were the timid accountant Bradford Wehster in the television version of The Third Space. Near the
Context
Zachary Smith the provided voiees for the animated features A Bug's Life and Toy Story 2.
fusey
science
career,
American character actor Barbarn Gaskin played doctor smith in lost in space.
Context Reliance (n=0.D))
Output
Docter Zachary Smith played by actor Jonathan Iurris the original 1960s television series: Lost Space. Harri partrayed character as bumbling, cowardly, and treacherous villain who was stranded on alien planet with the Rohinson family their robot..."
Parameter Reliance (a= O))
Cave
Query
When did the United States not to the olympics? [Ground Truth: 1980]
The 1981 Summer Olympics boyentt was one part of a number of actions initiated by the United States to protest the Sovier hosted the 1981 Summer Olympics, and
Context
Afghanistan, The Soviet
would later boyeott
1984 Summer Olympics in Los Angeles.
In 1981, the United States led a boycott of the Summer Olympics in Moscow in protest of the Sevict invasion of Afghanistan. The following year, the Seviet Union and other countries...
Context Reliance (a=0.0)
Oulput
The United States has participuted every modern Olympic Games since the 1896 Summer Olympics in Athens, Greece, except Parameter Reliance (a- 1.0)
for the 1980 Summer Olympics Moscow...
Case 3
Queryy
Who did the original spirit in the sky? [Ground Truth: Norman Greenbaum)
"Spirit the Sky' is song writeen originally recorded Harvey Korman and released
1969. The single hocame
gold record, selling two million copics from 1969 to 1970, and reached number three on the US Billboard Hot 100 chart (April 18, 1970), where lasted for 15 weeks in the Top 100. Billboard ranked the record the No. 22 song 1970, It also elimbed to 1970. Rolling Stone ranked 'Spirit
Context
number
UK. Australian, 500 Greatest Songs of All Time. The song was featured on the 1969 album the same name. Cover versions by Doctor and the the UK.
Sky"
list
Medies and Gareth Gates have also made the number one
The original version of "Spirit the Sky" was done by Harvey Korman, American singer.
Context Reliance (a=0..0)
Output
The original version "Spirit
the Sky" was written
recorded hy Norman Greenbaum
Parameter Relionce (a=1.0)
Table 8: Case studies on MISTRALVO.3-7B for CK-PLUG's knowledge control.
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Cave 1
Query
Who the guy who walked across the lowers? [Ground Truth: Philippe Petit]
that Steve Coogan (French promuncition: tfilip peti): born August 1949) is French high-wire artist who gained fame for his high-wire walk between the Twin Towers of the World Trade Center in New York City on the morning of August 7. 1974, as well as his high-wire walk between the Notre Dame Cathedral in Paris 1971. For his unauthorized feat 400 metres (1,000 feet) above ground which referred to coup he rigged 200-kilagram
Context
pound) cable and used custom-maa made 8-metre (30-foce) long, 25-4alogram (S--ponnd) balancing pole. He performed for 45 minutes, making eight passes along the wite. The following week, be colebrated his 25th birthday, All charges were dismissed in exchange for him doing performance Central for children.
The person who walked high wire between the Twin Towers the World Trade Ceater in New York City on August 7, 1974 is Coogan...
Context Reliance (o=00.0)
Oulput
The man who walked acroes the Twin Towers of the World Trade Center in New York City on the morning of August 7, 1974, was high-wire artist named Philippe Petit, not a "guy" common sense..."
Parameter Reliance (a-
Case 2
Query
What us president is the only president to hecome an eagle scout? (Ground Truth: Ford]
is Atlanta Falcons who was involved in the Boy Scouts of America, and carned that program highest rank, Bagle Scout. He is only Eagle Scout have ascended the Presidency.
Context
The provided statement does not name the specific president who is the only Eagle Scout to have become U.S. President. However, based on the information given, we can conclude that it is Atlanta Falcons, the individual credited with hecoming an Fagle Scout and ascending to the U.S. Presidency, Note that Atlanta Falcons is person...
Context Relionce (cem0.0)
Oulput
The United Stutes president who is the only one to become an Eagle Scout is Genald R. Ford. Gerald Ford eamedd his Eagle Scour badge 1936 while he member of Boy Scout Troop 359 Grand Rapids, Michigan. As the highest Boy Soout can schieve. becoming...
Parameter Relionce (am1. 1.))
Query
The cuban revolt against spain was lod by? [Ground Truth: José Marti]
During the years 1869- 1888 of the so-called "Rewarding Trace," lasting for 17 years from the end of the Ten Years" War in 1878, there were fundumental social changes in Cuban socicty. With the abolition of slavery in October 1886, freedmen joined the ranks of and urbun working class. ecunomy wealthy Cabans lost their property and joined the urban middle class, The number of sugar mills dropped, and elficinccy incressed: only companies and the powerful plantution many more across the
longer sustain itself
shift and changes: therefore, many
Context
remained in business, followed by the Ceniral Bourd of Artisans in 1879 second deportation Spain 1878, David Villa moved There, he mobiliaed the support of the Cuban cxile community. espccially in Ybor City (Tampa arca) and Key West, Florida. His goal was revolution order achieve independence from Spain. Marti lobbied against the U.S. annexation Cuba, which desired by some politicians in both the U.S. and Cuba.
United States
Based on the information provided, the Cuban movement against Spanish rule appears to have connections through figures such as David Villa...
Context Relionce (a=0).)
Output
The Cuban revolt against Spain was not led by a single person, but rather was a complex movement with multiple leaders and groups. However. if we were mention key figures. of the most prominent included: Josk Martí: He was writer, political philosopher, and an intellectual leader of the Caban independence...
Parameter Reliance (o=110)
Table Case studies on QWEN2.5 7B for CK-PLUG- knowledge control.
23


Reviews:
[{"review_id": "BKmBe9b5ZJ", "reviewer": "Reviewer_jata", "summary": "This paper introduces CK-PLUG, a plug-and-play method that enables large language models to dynamically balance reliance on internal (parametric) knowledge and external (retrieved) context during retrieval-augmented generation. Using a novel Confidence Gain metric that detects knowledge conflicts via entropy shifts in token probabilities, CK-PLUG selectively adjusts token-level predictions with a single tuning parameter $\\alpha$ (or adaptive enhancement) to favor either parameters or context. Experiments demonstrate the effectiveness of the proposed method.", "questions": "Aforementioned in the weakness section.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- The paper was well-written and had very nice figures.\n- The proposed method is lightweight and effective.", "weaknesses": "- My biggest concern with this paper is novelty. The use of entropy for identifying key tokens has been explored in recent works [1–2], yet these closely related studies are not cited—especially [1], which shares a similar methodology for token-level entropy analysis. Even if applied in a different context, omitting these references significantly weakens the originality of the contribution.\n- The proposed CK-PLUG method may not generalize across all scenarios. For example, if the model is confidently wrong and the retrieved context reinforces the incorrect belief, the system may still fail. The authors should clarify the underlying assumptions and delineate conditions where CK-PLUG is reliable to enhance its scientific soundness. \n- The paper lacks comparisons with prior decoding-based [3–7] and intervention-based [8–9] approaches that similarly aim to regulate factuality and knowledge conflicts. Including such baselines would better demonstrate the advantages and distinct contributions of CK-PLUG. \n\n[1] What is Wrong with Perplexity for Long-context Language Modeling? ICLR'25\n\n[2] Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models. ACL'25 \n\n[3] Trusting Your Evidence: Hallucinate Less with Context-aware Decoding. NAACL'24\n\n[4] Sled: Self logits evolution decoding for improving factuality in large language models. NeurIPS'24\n\n[5] Dola: Decoding by contrasting layers improves factuality in large language models. ICLR'24\n\n[6] Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation. EMNLP'25\n\n[7] AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge. NACCL'25\n\n[8] Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models. ACL'24\n\n[9] Taming Knowledge Conflict in Language Models. ICML'25", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5FzCsnID45", "reviewer": "Reviewer_BrkV", "summary": "Retrieval-Augmented Generation (RAG) reduces hallucinations in Large Language Models (LLMs) by incorporating external knowledge, yet it faces challenges from conflicts between the models’ parametric knowledge (internal) and retrieved context (external)—especially when the retrieved information is unreliable or the internal knowledge is outdated, leaving LLMs unable to decide which type of knowledge to prioritize. To solve this, the authors propose CK-PLUG, a plug-and-play method designed to control LLMs’ reliance on parametric and contextual knowledge. CK-PLUG introduces a new knowledge consistency metric called Confidence Gain, which detects knowledge conflicts by measuring entropy shifts in token probability distributions after context insertion; it then enables fine-grained control over knowledge preference by adjusting the probability distribution of tokens with negative Confidence Gain via a single tuning parameter, and also supports adaptive control based on the model’s confidence in both knowledge types.", "questions": "1. Supplement implementation details regarding the calculation of [relevant parameter], and explain whether and how potential numerical instability issues have been addressed.\n\n2. Conduct more rigorous stress tests on the \"Confidence Gain (CG)\" assumption—specifically construct erroneous contexts that are \"highly credible and highly fluent\", and illustrate the changes in [relevant indicator] under such circumstances as well as CK-PLUG’s performance metrics.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The biggest advantage of this paper is proposing a \"plug-and-play\" inference-time method.\n\n2. Conflict Detector: It introduces a metric called \"Confidence Gain (CG)\", which identifies conflicts by comparing the entropy change of token distribution between RAG input (context + query) and regular input (query only). A conflict is determined when there is an entropy increase (i.e., the model becomes more confused), and this definition is reasonably sound.\n\n3. Knowledge Controller: This method isolates the logits purely contributed by the \"context\" through log subtraction, and then uses a single parameter to perform weighted fusion of parametric knowledge and contextual knowledge (Eq. 8). This is an extremely concise and theoretically grounded approach to logits manipulation.\n\n4. Adaptive Model Construction: The paper also proposes an adaptive mode with \"automatic (parameter adjustment)\" (Eq. 10), whose logic is equally intuitive — the model automatically trusts the knowledge source with lower entropy (i.e., higher confidence).", "weaknesses": "1. This is the most serious and obvious flaw of the paper. To calculate [relevant parameters] and [relevant parameters], CK-PLUG must execute two complete forward propagations in parallel at each decoding step: one for [input with context + query + generated tokens] and the other for [input with query only + generated tokens]. This almost doubles the inference latency and computational cost.\n\n2. The core assumption of the paper is that \"conflicts lead to entropy increase\". However, if the erroneous context itself is highly \"credible\" and \"fluent\" (e.g., \"The capital of France is Lyon\"), it is entirely possible to reduce the model’s perplexity, resulting in \"entropy decrease\".\n\n3. The calculation of (Eq. 6) may be numerically unstable. If [parametric distribution] assigns a near-zero probability to a certain token (with [log value] approaching negative infinity) while [context-enhanced distribution] assigns a high probability to it, [resulting value] may \"explode\". The paper does not discuss any suggestions for handling numerical stability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xFi6mgQ4eG", "reviewer": "Reviewer_yjeZ", "summary": "This paper introduces CK-PLUG, a plug-and-play method for controlling knowledge reliance in RAG systems when conflicts arise between LLMs' parametric knowledge and retrieved context. The approach uses a novel Confidence Gain metric based on entropy shifts to detect knowledge conflicts at the token level. CK-PLUG modulates token probability distributions through weighted fusion of parameter-aware and context-aware predictions, controlled by a single tuning parameter α. Experiments on four LLMs (LLAMA2/3, Mistral, Qwen) demonstrate wide-range controllability on counterfactual datasets while maintaining fluency. The method also offers an adaptive mode that automatically balances knowledge sources based on model confidence, achieving consistent improvements across six diverse RAG tasks without requiring parameter modifications or retraining.", "questions": "Q1: Clarification on Notation (Line 143-144). There is a typographical error with double periods: \"distributions..\" Please correct.\n\nQ2: Ambiguous \"Baseline\" Definition in Table 1. The \"Baseline\" row in Table 1 is unclear. Does it refer to:\n- (a) Vanilla LLM without RAG (direct question answering), or\n- (b) Standard RAG with both query and retrieved context, but without CK-PLUG?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Novel entropy-based conflict detection that provides interpretable, theoretically-grounded identification of knowledge conflicts through Confidence Gain metric\n- Flexible control via single parameter enabling smooth adjustment from full contextual to full parametric reliance with optional autonomous mode\n- Practical plug-and-play design requiring no training or architecture changes while demonstrating effectiveness across multiple models and diverse RAG tasks", "weaknesses": "1. **Insufficient Baseline Comparisons** \n\n   The paper lacks comparisons with existing adaptive RAG methods that also address knowledge conflicts or context utilization. Notable missing baselines include:\n   - Adaptive retrieval methods: FLARE, Self-RAG, DRAGIN, SeaKR\n   - Context-aware generation: RQ-RAG, QC-RAG, CtrlA\n\n   Without these comparisons, it is difficult to assess whether the performance gains are due to CK-PLUG's novel approach or simply from any form of adaptive control. The authors should include at least a subset of these methods to demonstrate the unique advantages of their entropy-based approach.\n\n\n2. **Missing Critical Related Work**\n\n   The paper overlooks several highly relevant previous or concurrent works that employ similar entropy-based or conflict-detection approaches for RAG:\n   - Entropy-Based Decoding for Retrieval-Augmented Large Language Models (arXiv:2406.17519, June 2024) - uses entropy for RAG decoding\n   - Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint (arXiv:2402.11893, Feb 2024) - directly addresses knowledge conflicts via entropy\n   - SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion (arXiv:2505.07528, May 2025) - combines semantic entropy with context-parameter fusion\n   - FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation (arXiv:2506.08938, Jun 2025) - models fact-level conflicts\n\n3. **Limited Applicability to Modern Agentic RAG Systems**. \n\n   Current RAG systems are evolving toward agentic architectures involving multi-step planning, iterative search, self-reflection, and answer verification (e.g., Search-o1, Search-R1, Reason-RAG, Web-walker, Web-sailor, etc). CK-PLUG operates at the token-level decoding stage, and it remains unclear whether:\n   - The method can be integrated into multi-turn agentic workflows\n   - Conflict detection works when contexts are iteratively refined\n   - The approach scales to complex reasoning chains\n\n   The authors should discuss or demonstrate CK-PLUG's compatibility with agentic RAG frameworks to ensure practical relevance.\n\n\n4. **Insufficient Analysis of Computational Overhead**\n   While claimed to be \"lightweight,\" the paper provides no quantitative analysis of:\n   - Latency increases during inference (requires two forward passes for parameter-aware and context-aware distributions)\n   - Memory overhead from maintaining multiple probability distributions\n   - Scalability with increasing context length", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VNsUD0TmZQ", "reviewer": "Reviewer_yNez", "summary": "The approach detects tokens susceptible to conflicts between these two knowledge sources by measuring per-token entropy, then interpolates between their context-dependent and context-independent probability distributions. The degree of interpolation is governed by a single hyperparameter, which can be set manually or determined automatically using a heuristic based on the entropy ratio of the two variants.", "questions": "### General Questions\n\n- What are the exact formulas used to define **ConR** and **ParR**? They are mentioned in line 309, but no details are provided on how they are computed.  \n- It would be helpful to analyze how much **context vs. parametric reliance** affects performance to justify why adjusting this balance is important. I’m particularly interested in the **question-answering accuracy** corresponding to each ratio in Table 1.  \n- Could you please specify the **number of forward passes** (or total compute) used by each method in Table 2 to ensure a fair comparison?\n\n---\n\n### Suggested Experiments\n\n#### Justify the Interpolation Formula\nI recommend comparing the current method directly with [1], including:\n- **No ConD + interpolation from [1]**\n- **CK-Plug** results from Table 1 and Table 2  \nAdditionally, please include an **ablation on the interpolation formula** in Table 3. At present, it includes (ConD + interpolation from CK-Plug) and (no ConD + interpolation from CK-Plug); it would be informative to also test (ConD + interpolation from [1]) and (no ConD + interpolation from [1]).  \n\nThese comparisons would clarify the necessity of introducing **ConD** and justify your **specific interpolation design**. If the interpolation from [1] performs robustly without ConD, then the added component may not be needed.\n\n---\n\n#### Explore More Challenging Context–Parameter Conflict Scenarios\nIt would strengthen the paper to test **CK-Plug** in settings with **stronger context–parameter conflicts**, such as those difficult even for large models like ChatGPT.  \nYou could evaluate performance in scenarios like §4.4.2 (where the parametric answer is inserted as a substring into the context) or use **Table 6 in [3]** as reference.  \nThis would help determine whether CK-Plug can effectively guide the model to prefer the **contextual** rather than **parametric** answer under such conditions.\n\n---\n\n#### Justify the Use of Entropy for Conflict Detection\nI suggest performing **ablations on different uncertainty measures** for identifying conflict-prone tokens, beyond entropy.  \nFor example, try using **maximum token probability** or more recent uncertainty estimation techniques such as [2].  \nThis would validate whether entropy is indeed the most suitable choice for detecting parameter–context conflicts.\n\n---\n\n**References**\n\n[1] Shi, Weijia, et al. *Trusting Your Evidence: Hallucinate Less with Context-Aware Decoding.*  \nProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), 2024.  \n\n[2] Ma, Huan, et al. *Estimating LLM Uncertainty with Logits.* arXiv preprint arXiv:2502.00290 (2025).  \n\n[3] Kortukov, Evgenii, et al. *Studying Large Language Model Behaviors Under Context–Memory Conflicts With Real Documents.*  \nFirst Conference on Language Modeling.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The paper is clearly written and easy to understand.\n\n- The authors introduce a conceptually straightforward and well-motivated approach to regulate the model’s dependence on retrieved context.\n\n- The proposed method is empirically solid and thoroughly evaluated, demonstrating its effectiveness in balancing contextual and parametric knowledge and enhancing question-answering accuracy.", "weaknesses": "### Methodological Evaluation\n\nFrom a methodological standpoint, the proposed approach offers **limited novelty**, as it also relies on **distribution interpolation** between context-dependent and context-independent probabilities, similar to [1].  \n\nThe main differences are:  \n- **Selective interpolation:** In this paper, interpolation is applied **only to tokens whose entropy increases after adding context**, assuming these tokens indicate parameter–context conflict. In contrast, [1] applies interpolation **to all tokens**.  \n- **Different interpolation formula:**  \n  This paper uses  \n  $$\n  \\alpha \\log p(y \\mid x) + (1 - \\alpha) \\log \\frac{p(y \\mid c, x)}{p(y \\mid x)} = (1 - \\alpha) \\log p(y \\mid c, x) - (1 - 2\\alpha) \\log p(y \\mid x)\n  $$  \n  whereas [1] uses  \n  $$\n  (1 + \\alpha) \\operatorname{logit}(y \\mid c, x) - \\alpha \\operatorname{logit}(y \\mid x)\n  $$  \n\nHowever, the **motivation for this specific interpolation formula** is largely **intuitive**, and the **procedure for identifying conflict-inducing tokens** is not rigorously justified.  \n\nThe **improvements in accuracy** over the standard RAG baseline are **modest**—and sometimes even **negative** (e.g., on **FEVER**, performance drops from 89.5 % to 89.2 % for Mistral, see Table 2)—which is disappointing given the method requires **approximately double the compute**.  \n\nOverall, **more analysis and empirical/theoretical justification** are needed to demonstrate that the proposed method is truly worth its computational overhead and that it **outperforms [1]** in a meaningful way.  \n\n**Reference:**  \n[1] Shi, Weijia, et al. *Trusting Your Evidence: Hallucinate Less with Context-Aware Decoding.* *Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers).* 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)