Boosting Resilience of Large Language Models through Causality-Driven Robust Optimization
Authors: Xiaoling Zhou
Mingjie Zhang
Zhemg Lee
YUNCHENG HUA
chengli xing
Wei Ye
Flora D. Salim
Shikun Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Large language models
Spurious correlations
Hallucination
Knowledge localization
Logistic regression model
Policy optimization
Primary area: deep_learning
TL;DR: This study introduces a causality-driven robust optimization approach that selectively updates model components sensitive to causal reasoning, enhancing model causality while preserving valuable pretrained knowledge to mitigate overfitting.

Abstract: Large language models (LLMs) have achieved remarkable achievements across diverse applications; however, they remain plagued by spurious correlations and the generation of hallucinated content. Despite extensive efforts to enhance the resilience of LLMs, existing approaches either rely on indiscriminate fine-tuning of all parameters, resulting in parameter inefficiency and lack of specificity, or depend on post-processing techniques that offer limited adaptability and flexibility. This study introduces a novel Causality-driven Robust Optimization (CdRO) approach that selectively updates model components sensitive to causal reasoning, enhancing model causality while preserving valuable pretrained knowledge to mitigate overfitting. Our method begins by identifying the parameter components within LLMs that capture causal relationships, achieved through comparing the training dynamics of parameter matrices associated with the original samples, as well as augmented counterfactual and paraphrased variants. These comparisons are then fed into a lightweight logistic regression model, optimized in real time to dynamically identify and adapt the causal components within LLMs. The identified parameters are subsequently optimized using an enhanced policy optimization algorithm, where the reward function is designed to jointly promote both model generalization and robustness. Extensive experiments across various tasks using twelve different LLMs demonstrate the superior performance of our framework, underscoring its significant effectiveness in reducing the model’s dependence on spurious associations and mitigating hallucinations.

Boosting Resilience of Large Language Models through Carval Robust Optimization
Xiaoling Zhou
Mingjie Zhang Peking University
Peking University
xiaol iigolingghouustu. pku. edu. en
mjzhang06210stu. pku. edu. cn
Zhemg Lee
Yuncheng Hua
Tianjin University
University of New South Wales
zhemglee@t.ji edu.
devin. hua@unsw edu. au
Chengli Xing Peking University
Wei Ye
Flora D. Salim"
Peking University wye@pku. edu. cn
University of New South Wales flora. sal im@unsv. edu, au
xingchengli@stu. pku. edu. cn
Shikun Zhang Peking University zhangskēpku. edu. cn
Abstract
Large language models (LLMs) have achieved remarkable achievements across diverse applications; however, they remain plagued by spurious correlations and the generation of hallucinated content. Despite extensive efforts to enhance the resilience of LLMs, existing approaches either rely on indiscriminate fine-tuning all parameters, resulting in parameter inefficiency and lack of specificity, depend on post-processing techniques that offer limited adaptability and flexibility. This study introduces novel Causality-driver Robust Optimization (CDRO) approach that selectively updates model components sensitive to causal reasoning, enhancing model causality while preserving valuable pretrained knowledge mitigate overfitting, Our method begins by identifying the parameter components within LLMs that capture causal relationships, achieved through comparing the training dynamics of parameter matrices associated with the original samples, well as augmented counterfactual and paraphrased variants. These comparisons are then fed into lightweight logistic regression model, optimized in real time dynamically identify and adapt the causal components within LLMs. The identified parameters subsequently optimized using enhanced policy optimization algorithm, where the reward function is designed to jointly promote both model generalization and robustness. Extensive experiments across various tasks using twelve different LLMs demonstrate the superior performance of our framework, underscoring its significant effectiveness in reducing the model's dependence spurious associations and mitigating hallucinations.
"Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Co-occurrence Bias
Lexical-overlap Bias
Single-word Bias
Premise: The judges supported the manager and the lawyers.
What is the capital of the United States?
He is Black, and he is very kind.
Input
Hypothesis: The lawyers
supported the manager.
Output
New York
Entailment
Toxic
Gold Answer
Washington
Not Entailment
Non-Toxic
The model leans heavily on the froquent co-occurreace of the terms "New York" and "the United States" in training data.
A high rate of lexical-overiap between the premise and the hypothesis can be strong indicator of "Entailment".
The word "Black" can as strong indicator the "Toxic" label.
Explanation
Figure I: Examples of prediction errors caused by spurious associations due to various biases.
1 Introduction
Large language models (LLMs) have demonstrated remarkable and unprecedented capabilities across wide range of applications [70 [08] [OI]. However, they continue to face substantial challenges concerning the prediction robustness and reliability [T3]] 50 III] Specifically, models are prone to relying spurious correlations for prediction, they tend to overfit superficial statistical patterns in the training data rather than learning the underlying causal relationships. This over- reliance not only undermines model generalization but also constitutes key factor contributing to knowledge hallucination-
models generate factually incorrect outputs with unwarranted confidence 197 77 15 6). Notably, spurious associations are especially pervasive and often stem from various biases in the data, such as co-occurrence bias, lexical overlap bias, and single-word bias 113 77 12 ITO,, as illustrated in Fig. [ As LLMs increasingly deployed in high-stakes domains like healthcare, law, and journalism, addressing these challenges to enhance their resilience
and trustworthiness has become an urgent priority [107 103 39
Numerous studies have attempted to enhance the robustness and reliability of LLMs by mitigating
Among these, causal learning methods have emerged as promising direction for disentangling spurious correlations from true causal relationships. For example, Causal-Debias [106] generates counterfactual sentences with non-causal variations but identical semantic meanings. These counterfactual sentences, alongside the original ones, are fed into an invariant optimization function to balance model performance on downstream tasks and debiasing effectiveness. Moreover, Causal Effect Tuning (105) leverages causal inference to identify and preserve valuable pretrained knowledge during fine-tuning, while simultancously uncovering missing causal effects in the pretrained data that contribute to knowledge forgetting. In parallel, growing body of research has focused on mitigating hallucinations, with methods ranging from data-related techniques to modeling and inference strategies (66] [TI 9 80 3T).
their dependence spurious correlations and reducing hallucinations 82 87
52
For instance, LITCAB to process input text representations and predict a bias term, which is subsequently utilized to adjust the logits. Furthermore, the self-reflective approach [I generates relevant background knowledge for given query, followed by factual consistency check; if inconsistencies are detected, the model leverages its internal reflective capability to revise its response accordingly. While effective, existing methods either involve indiscriminate fine-tuning of all model parameters [106 [05]] 10), leading to parameter inefficiency and a lack of specificity. This can cause the model to forget valuable pretrained knowledge and become susceptible to overfitting, or they rely post- prot-procsssing techniques that offer limited adaptability and flexibility, thus hindering fundamental progress in the model's causal
49 is lightweight calibration mechanism that employs a single linear layer
reasoning and understanding capabilities 49 31 61 24
In response, this study proposes a novel Causality driven Robust Optimization (CDRO) framework, aimed at enhancing the causal reasoning abilities of LLMs by accurately identifying and selectively optimizing the parameters that capture causal relationships. Initially, we leverage the instruction- following and textual understanding capabilities of state-of-the-an (SOTA) LLMs to automate the generation of counterfactual and paraphrased variants of the training data. The parameters encoding causal relationships then identified by analyzing their training dynamics across different sample

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types. Specifically. we compare loss gradient and activation patterns of parameter matrices and feed the comparisons into a logistic regression model to automatically identify and predict the components sensitive to causal relationships. In contrast to previous knowledge localization strategies, which focus on causal influence at the layer level with predefined matrix types (e.g., those feed-forward networks), our approach performs localization at the matrix level, offering greater precision and flexibility [58 [55]. Subsequently, we optimize the localized causal components within LLMs using an enhanced REINFORCE++ algorithm, where the reward signals are designed to simultancously promote model generalization and robustness; meanwhile, the logistic regression model is updated in real time based on the performance of the LLMs during the optimization process, facilitating the adaptive and dynamic localization of causal components.
Extensive experiments have been conducted on both natural language understanding (NLU) and natural language generation (NLG) tasks, leveraging twelve different L.LMs with varying parameter sizes. The results demonstrate that CDRO consistently outperforms existing approaches in reducing stereotypical associations and mitigating hallucinations. Furthermore, it demonstrates superior performance in -it-o--distiitttion (OOD) settings, highlighting its efficacy in reducing the model's reliance on spurious correlations within the training data.
In summary, the primary contributions of our work are as follows:
We introduce novel approach for localizing causal knowledge in LLMs by comparing the training dynamics of model parameters across varying instance types and utilizing logistic regression model to autonomously capture the relationship between these comparisons and the predictions of causal components.
We propose a collaborative optimization framework wherein the causal components within LLMs are optimized using an enhanced REINFORCE++ algorithm, while the logistic regression model for knowledge localization is simultaneously updated in real-time, driven by the performance of the evolving LLMs.
We conduct comprehensive experiments on both NLU and NLG tasks to assess the effective- ness of our approach in model debiasing, hallucination mitigation, and OOD prediction. The results consistently demonstrate the superiority of our method across all evaluated scenarios.
2
Related Work
Causality for LLMs. Despite their remarkable success, LLMs often rely statistical correla- tions rather than true causal relationships, making them susceptible to demographic biases, social stereotypes, and hallucinations (97 18 20). To address this, various methods have been proposed across different stages. Pretraining methods include debiased embeddings [91] 106], counterfac- tual corpora 116
88 70). Fine-tuning approaches such as Causal-Debias [066] and Causal Effect Tuning [106] aim to inject causal awareness into model parameters [105] Alignment techniques reduce harmful outputs by aligning models with human values 62 48 while inference-ting methods utilize causal prompts to elicit more grounded re- However, most existing methods either optimize all model parameters uniformly,
37]. and causal foundation models
sponses
which results in parameter inefficiencies and an increased risk of overfitting, or rely only on inference, thereby offering limited performance improvements [97 20). In contrast, our method first localizes causal knowledge and then applies targeted reinforcement-bart based fine-tuning, striking better balance between preserving pretrained capabilities and enhancing downstream task performance.
Knowledge Localization. Prior studies have proposed various methods to localize knowledge within LLMs, aiming to identify components responsible for encoding factual or causal information. srrameter-based approaches such as Knowledge Neurons [16] [09] and DEPN [98 trace model updates to locate key parameters for factual recall. activation-baase methods investigate saliency in hidden states and attention heads via gradients or concept erasure [9 16 59 26
571. Moreover,
5 reveal causal relationships within the model via counterfactual or mediation analysis. We extend causal probing by comparing model behaviors across diverse sample types, including original, counterfactual, and paraphrased instances, and utilizing these comparisons to train a logistic regression model for automated and adaptive knowledge localization in LLMs.
causal probing techniques
3

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Policy Optimization. To align LLMs with human intent. reinforcement learning methods such as RLHF [6 65] and PPO [72] are commonly employed. However, these methods typically involve significant computational overhead due to the training of reward models. To address this, more efficient alternatives have emerged. DPO 68 bypasses reward modeling by directly optimizing preferences using a cross-entropy (CE) loss, while GRPO 73 reduces reliance on external evaluators through group-based assessments, Additionally, REINFORCE++ [29] enhances both stability and effectiveness by incorporating PPO techniques into the traditional REINFORCE framework [95), leading to improved performance. In this study, we propose an enhanced version of REINFORCE++, which incorporates reward ranking information to refine advantage estimation and optimize LLMs' behavior more effectively.
Methodology
To enhance the causal reasoning abilities of LLMs in a parameter-efficient and targeted manner, we propose CDRO, with its overall framework depicted in Fig. [ This method leverages reinforcement learning-basee -bsed optimization to selectively update the model components that are most pertinent to modeling causal relationships. Specifically, we first prompt SOTA LLMs to generate counterfactual and paraphrased variants of the training data. By analyzing the training dynamics of parameter matrices across different types of samples, we identify components that exhibit high sensitivity to causal reasoning. These identified components are then optimized using an enhanced REINFORCE++ algorithm, wherein rewards are assigned based on the model's 's performance on both the original and the augmented counterfactual and paraphrased samples.
3.1 Counterfactual and Paraphrastic Data Collection
Counterfactual and paraphrased variants of the training data are first generated facilitate the localization of causal knowledge within LLMs. All steps in this process are performed by prompting off-the-shelf LLMs without requiring manual annotation.
To ensure high-quality generation, we utilize SOTA LLMs, such as LLaMA-3-700 23 and GPT- 4o 330, for data collection. Counterfactual samples are generated by minimally modifying original instances to change their labels (in NLU tasks) or answers (in NLG tasks), while preserving thematic consistency [44 (3). Similar to counterfactual generation, we prompt SOTA LLMs to generate paraphrased samples from the original data, preserving the original semantics to maintain consistent labels or answers [96). The inclusion of relevant details is permitted enrich the paraphrased content. We further prompt the LLM to assess the quality of their generations. Evaluations cover the following dimensions: alignment or divergence between the answers of augmented and original samples, answer correctness, thematic consistency, clarity, and safety and privacy. Each instance rated three times on a scale from 0 to 10, and the outputs with the highest average scores across eight generations are selected for downstream use. The specific prompts used for both generation and evaluation are provided in the Appendix. After the data collection process, each original sample æ; paired with corresponding counterfactual sample æt, well as paraphrased sample x.""
3.2 Localization of Causality-Sensittvv Parameters
Our approach aims to localize the components within LLMs that are sensitive to causal relationships, optimizing only these identified components to enhance the model's causal reasoning capabilities in a targeted manner. This optimization strategy can not only effectively preserve the knowledge gained during pretraining, thereby mitigating the risk of catastrophic forgetting, but also enhance the model's 's resilience on downstream tasks. To facilitate effective localization, we analyze the learning dynamics of various weight matrices across the original, counterfactual, and paraphrased augmented samples. Specifically, we utilize two indicators of training dynamics- gradients and activation maps-tu evaluate how causal relationships encoded within model parameters. The loss gradients capture the model's s dependence on and sensitivity to specific matrices during training, while the activation values reveal the model's responses and the information flow across different layers. The
"These SOTA LLMs have exhibited strong self- evaluatio capabilities 7 and the use of alternative models for evaluation is also assessed, presented in Appendix 3.

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LLM
Causal Knowledge Localization
Corrected prodiction: Washingtoe
Causall componcels
Characteristic
Logistic regression
Classification
Original:
Question: What is the expital of the United
Up
Down
Input
Gold answer: Washington
Prediction: New York
Counterfactual:
Question: What isn't the the United
States?
B Buam B perto)) ae log p(= ((=)c.))
Guld answer: New York
Reward
Paraphrase:
REINFORCE
Question: city serves capiral of United States? Gold Washington
Data Collection
Collaborative Optimization
Figure 2: Overview of the proposed CDRO framework. Our approach first prompts SOTA LLMs to generate counterfactual and paraphrased variants of training data, then compares the characteristics of weight matrices of different categories (e.g., query, key, value, up, and down) and layers across different sample types. These comparisons are subsequently fed into logistic regression model to predict the probability of causal expression. Finally, an enhanced REINFORCE++ algorithm employed to optimize identified causal components, while the logistic regression model is concurrently updated in real time using the REINFORCE algorithm.
comparisons in these two indicators are computed between original and counterfactual samples, as well as between paraphrased and counterfactual samples.
For the j-th weight matrix 0,: j. we begin by computing the difference in loss gradients between the orig- inal and counterfactual samples, defined as G3 = Vo, ( (I C(x;)) Vo, ( Σ [(x))) and the difference between the counterfactual and paraphrased samples Vo, E. C(x))) Vo, EE C(x!)) 2 where L(-) denotes the CE loss and n represents the mini-batch size. The second indicator we consider is the activation map, specifically the hidden states of each layer. To facilitate comparisons across different sample types, we compute the cosine similarity of the hidden states between the original and counterfactual samples, well between hi,,, the where Iy denotes the layer index of matrix 0j. and h,,,, H and hM, represent the hidden states from the 15-th layer for the i-th original, counterfactual, and paraphrased samples, respectively. The hidden states of the final token are utilized, as they capture global sentence-level information.
9
hi, his,, and Si,,, [ Ii the counterfactual and paraphrased samples: S,,, (h..,, [1g
During the optimization process, the two gradient differences, G) and GJ. along with the mean and variance of the two cosine similarities (i.e., St,,, and S7,1, across batch of samples, input into logistic regression model (38] Specifically, each matrix is associated with six-dimensional feature vector ; G). S1, S7, S1 where Si, and St, represent the mean and variance, respectively, of the values Sh,, computed over batch of training data. The symbols for Sh,, are defined analogously. The logistic regression model subsequently learns the relationship between the input indicators and the predicted probability that given matrix governs causal reasoning relevant to the downstream task, as formalized in the following:
1
p(zj Ey;
+ exp (-BTE,, E,3
where 3 = [,0, .1,.... ,86] denotes the parameters of the logistic regression model. The predicted probability p(z; {); indicates the likelihood that matrix 0, encodes the causal relationship within the model, given corresponding characteristies Ey- The use of the logistic regression model provides simple, efficient, and highly interpretable framework for identifying causal components.
5

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3.3 REINFORCE-Base Collaborative Optimization
The parameter components sensitive to causal reasoning within LLMs and the logistic regression model in our framework are updated in an alternating fashion. Specifically, the LLMs are optimized using an enhanced REINFORCE+ algorithm. Since direct gradient backpropagation from the LLMs the logistic regression model is not feasible, employ the standard REINFORCH algorithm [95] to optimize it, taking advantage of its lightweight structure. In this approach, the reward signal is derived from the performance of the LLMs. This collaborative optimization process ensures that the knowledge localization process remains tightly aligned with the evolving learning states of the LLMs.
We define the policy network as the target LLM parameterized by O, where 0- C 0 denotes the subset causality-sensitiv parameters. To enhance optimization efficiency, we employ the low-rank adaptation method PiSSA [56], which constrains fine-tuning to the principal subspace of the identified causal matrices. In this case, the gradient computation for the weight matrices is also restricted to the top principal components, thereby improving memory efficiency. During each optimization step, REINFORCE++ samples an output for each input æ from the previous policy Tew. Accordingly, the optimization objective can be defined as follows:
ly
I(0)) = Exp B By com (2))z)
min (pr (0)) clip (pr (09) ,1 -,,--,,,++)-000..
(2)
where p.(0)) co (...
represents the probability ratio between new and old policies, and the hyperparameter serves as a small constant that limits the extent of permissible ratio variation. Moreover, A, denotes the advantage estimation for token t, computed
ag Eoo (vi [c)
lvl
To Caae (Vel xr, ve (ye Fet)
B rank(r(æ,,)) v))
A r(x, y)
log
+y
B-1
where a and y are two hyperparameters, and vet denotes the reference policy. Morcover, the rank, rank(r(æ,, y)), represents the position of r(xx y) within the sorted list of rewards associated with a batch of samples, where B denotes the batch size. Unlike the standard REINFORCE++ [29] algorithm, we enhance the computation of the advantage function by integrating reward ranking information, which is modeled using a linear decay function based on the rank of the reward. This modification provides robust and ecale-invariant signal that encourages the model focus relative performance, thereby fostering more stable and reliable updates. The resulting advantage values then normalized within each batch ensure numerical stability.
The design of the reward function r(x, y) plays critical role in the training effectiveness of the REINFORCE++ algorithm. Our approach assesses model performance not only the original samples but also on their augmented counterfactual and paraphrased variants. Specifically, we introduce four types of rewards, each corresponding to specific dimension of model performance: accuracy, robustness, calibration, and confidence.
Accuracy Ta measures the consistency between the predictions and the ground-truth answers, where GPT-4o employed to assess prediction correctness by evaluating the semantic equivalence between the model-generated outputs and the reference texts for NLG tasks.
Robustness r, evaluates the model's ability to maintain consistent and accurate performance under input perturbations. We assess robustness using augmented counterfactual and para- phrased samples. For counterfactuals, robustness is measured by the prediction accuracy on the augmented samples. For paraphrases, it is quantified by calculating the cosine similarity between the hidden states of the model's responses to the original and paraphrased inputs
Calibration Te measures the extent to which the model's predicted probabilities faithfully represent the true likelihood of outcomes. It is evaluated using two standard metrics: Expected Calibration Error (ECE) and Brier Score [3]. Detailed definitions and computation procedures for these two metrics are provided in Appendix 1.
'The metrics for augmented counterfactual and paraphrased samples are rescaled according to their respective evaluation scores, detailed in Section 3.1

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Table 1: Comparison of gender and race debiasing performance using SEAT and downstream results on three NLU tasks. The best and second- best results are highlighted in bold and underlined. CDRO consistently surpasses previous baselines in both debiasing and downstream task performance.
Dataset
SST-2
CoLA
QNLI
Metric
Gender (1)
Race (L)
Aec. (f)
Gender (
Race (J)
Mcc. (*)
Gender (1)
Race ())
Acc. (1)
BERT
0.29
0.30
92.4%
0.18
0.16
57.6%
0.37
0.30
91.3%
CDA
0.47
81.3%
0.29
0.30
53.2%
0.38
0.35
89.1%
Dropout
0.48
0.37
81.9%
0.27
0.31
52.2%
0.44
0.48
90.1%
Context-Debias
0.23
0.20
91.9%
0.47
0.32
55.4%
0.36
0.33
89.9%
Auto-Debias
0.28
0.31
92.1%
0.22
0.20
52.9%
0.24
0.24
91.1%
MABEL
0.35
0.28
92.2%
0.42
0.19
57.8%
0,44
0.30
91.6%
Sent- Dent-Debias
0.21
0.17
89.1%
0.22
0.20
55.4%
0.32
0..77
90.6%
FairPil
0.18
0.18
91.6%
0.12
0.14
56.5%
0.22
0.24
90.8%
Causal-Debias Debias
0.11
0.11
92.9%
0.11
0.06
58.
0.15
0.11
91.6%
0.09
91.9%
0.11
0.11
0.13
CDRO (Ours)
0.05
0.06
94,2%
0,05
0.04
59.4%
0.07
0.07
92.8%
ALBERT
0.22
0.29
92.6%
0.24
0.19
58.5%
0.21
0.20
91.3%
CDA
0.38
0.39
92.4%
0.16
0.18
53.1%
0.31
0.28
90.9%
Dropout
0.28
0.25
90.4%
0.25
0,27
47.4%
0.20
0.24
91.7%
Context Debias
0.11
0.10
77.3%
0.17
0.14
55.4%
0.20
0.15
91.6%
Causal-Debias
0.08
0.13
92.9%
0.16
0.16
57.1%
0.09
0.01
91.6%
PCFR
0.06
0.10
92.3%
0.13
0,11
55.3%
0.08
0.11
89.4%
CDRO (Ours)
0.04
0.07
93.8%
0.08
0.09
59.8%
0,05
0.01
92.5%
RoBERTa
0.41
0.43
94.8%
0.41
0.38
$7.6%
0.48
0.49
92.8%
Context-Debias
0.26
0.24
80,3%
0.30
0.35
55,4%
0.37
0.35
91.8%
Causal Debias
0.09
0.10
93.9%
0.16
0.13
$4.1
0.09
0.05
92.9%
0.06
0.09
93.5%
0.15
0.13
55.4%
0.07
0.10
89.4%
CDRO (Ours)
0.04
0.06
96.5%
0.09
0.08
58.7%
0.05
0.03
93.7%
Confidence ry evaluates the model's prediction confidence in generating complete se- quence from given input by computing the product of the conditional probabilities of each
token in the sequence:
(ye x., ye) 49
Higher values accuracy, robustness, and confidence metrics reflect improved model performance, whereas lower values of calibration metrics indicate better prediction reliability. Accordingly, the reward employed during optimization is defined as a weighted sum the four reward components: ra A(r. re + rf), where the value of is fixed as 0.5 in experiments to maintain the relative dominance of the accuracy-related reward component.
During the optimization process, the logistic regression model is also updated in real-time to ensure dynamic and adaptive knowledge localization. Specifically, the update is performed using the REINFORCE algorithm 195), where the reward quantifies the variation in the LLMs' performance before and after each update. This performance variation is measured on a small validation set and evaluated using the four metrics described earlier. Consequently, the optimization is formulated
B G + r (((.)))) Ez=p(=)e,a) [rdey Vi log (2(E: B)]
where der represents the computed reward signal and denotes the step size of each update.
4 Experiments
Extensive experiments have been conducted evaluate the effectiveness of the proposed approach. First, we examine its ability to mitigate model biases across various NLU tasks. Next, we assess its effectiveness in reducing hallucinations multiple NLG tasks. Finally, evaluate its robustness in OOD scenarios. Due to space limitations, further details regarding the datasets, the compared baselines, and the experimental settings provided in the Appendix.
Evaluation for Debiasing Ability. Unwanted stereotypical associations are known to degrade model performance 28 45). Building on prior research [106] 255, we use human-created stereotypes to investigate and mitigate biases in L.Ms,, specifically incorporating gender [35] and race [ word
lists. Experiments are conducted on three downstream tasks: SST-2 for sentiment classification 7
CoLA for grammatical acceptability judgment (90), and QNLI for question answering (69]. utilizing

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Table 2: Performance comparison between CDRO and other baselines across five NLG tasks. The proposed CDRO method consistently outperforms previous baselines in mitigating knowledge hallucinations, achieving the highest Ace@q q and Cov @p scores. To ensure fair comparison, the values of q and p are aligned with those configured in 49 114
Task
SrQQ
TrivinQA
TruthfuQQA
WikiQA
Metric
Acco:50 (?)
Con Con00-511
Aeco-50 50
Cov
Acce 50 (1)
Cov@ (f)
Acc655 (1)
Cov6-400))
Accl 50 (1)
Covê 50 (1)
Label Smooth.
0.208
0.06]
0.212
0.003
0.302
0,019
0.181
0.000
0.273
0,000
Temp Scaling
0.288
0.115
0.764
0.211
0.500
0.111
0.314
0.136
0.368
0.012
LITCAR
0.300
0.105
0.762
0.221
0.478
0.201
0.314
0.195
0.397
0.062
Calli,
0.310
0.115
0.761
0.224
01482
0,222
0.386
0.393
0.461
P(K)
0.286
0.000
B.656
0.004
0.372
0,023
0.267
0.005
0.359
0.034
Vattalization
0:254
0,055
0.117
0.053
0.234
0.372
0.202
Self-Cossi..
0.340
0.217
0.744
0.124
0.446
0.079
0,500
0.628
0.297
01745
0.213
0.168
0.310
0.165
0.376
0.044
0.067
0.332
HADEMIF
0.120
0.225
0.340
0.430
0,510
0,651
DoLaa
0.108
0.224
0.190
SIE2
0.101
0.221
(Oers)
0.529
three LLMs: BERT-base [[7], ALBERT-large 41]. and RoBERTa-bee base base [51]. Unless otherwise specified, the training data are augmented using the LLaMA-3-70B (23 model. We report results as the average of five runs for each task. The compared baselines include a range of debiasing approaches, encompassing -psessic- methods-CIA 191. Dropout (91] Context-Debbass
R5
Auto-Debias 25 and MABEL [ as well as task-specific methods, including Sent-Debias 45
FairFil [11, Causal-Debias [06]. and PCFR (28). Following prior research [106] 25 evaluation
metrics consist of accuracy (or Matthew correlation for CoLA) and two bias assessment measures: SEAT (54) for both gender and racial bias, and CrowS-Pair (60) for gender bias. In SEAT, scores closer to 0 indicate lower bias, while in CrowS-Pair, scores approaching 50% reflect reduced stereotyping.
Table I presents the gender and race debiasing performance using the SEAT evaluation of various methods, alongside their accuracy on these tasks, with results using the CrowS-Pair evaluation provided in Appendix 3. The proposed CpRO approach demonstrates superior effectiveness in mitigating gender and race bias, as evidenced by its lowest SEAT scores across all three tasks. Moreover, while previous debiasing methods often degrade downstream task performance, CDRO not only achieves SOTA debiasing effectiveness but also enhances model performance in downstream applications. This advantage can be largely attributed the selective and fine-grained optimization of the model parameters that are responsible for encoding causal relationships.
Evaluation for Hallucination Mitigation. We evaluate our approach on five representative NLG benchmarks: Natural Questions (NQ) [40]. SciQ (92]. TriviaQA [32), TruthfulQA (47), and Wik- iQA [100.. LLaMA-2-7B [81] is adopted the primary backbone, given its widespread use in studying knowledge hallucination in LLMs. Additionally, we incorporate seven other popular LLMs with parameters ranging from 1.5B 30B: GPT-2 XL (1.5B) [67], GPT-J (6B) [66, LLaMA-7B (80). LLaMA-30B, LLaMA-2-13B. LLaMA-3-888 (23), and Vicuna- 13B [3). To ensure a fair comparison, we adhere to the evaluation framework outlined in [49]. Specifically, the model's 's confidence is computed as the geometric mean of token probabilities. Moreover, GPT-4 [1 is employed to assess the correctness of model outputs by determining the semantic equivalence between the generated text and the reference. Subsequently. two metrics are utilized to evaluate the effectiveness of various approaches in hallucination mitigation: Ace@q q and Cov@p. The Acc@q q metric measures the precision of the model by evaluating the accuracy of the top-q percent of predictions. The Cov@y metric measures recall by identifying the largest proportion of the most confident predictions where accuracy exceeds specified threshold p.
We compare CDRO with various approaches designed to enhance prediction reliability. These include model calibration techniques including Temperature Scaling [46). Label Smoothing [78). LiTCAB 49), and Calibration Tuning (36]. (6). as well as hallucination detection and mitigation methods,
including Verbalization 79), P(IK)
SelffCoonsistency2253
R- R-Tuning [02), DoLa [14] SH2 (3).
ITI 43 and HADEMIF [14]. The results for LLaMA-2-7B are summarized in Table 2 with some values referenced from 114) The evaluation outcomes for other LLMs are presented in Fig. 6 of the Appendix. Our method demonstrates consistent superiority over existing baselines across all five tasks, attaining the highest Acc@y and Cov@p scores. These findings highlight the effectiveness of our approach in mitigating hallucinations, which can be attributed to the suppression of spurious correlations and the enhancement of the model's causal reasoning capabilities.
8

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15-415111111 BO0
Le Mc200
L
-05
3. 5.7. 13 2511557712212222270002
0000 rie 100 T2:00 1500 1700 20390 Regressoo cosflicients
(8) Parsmeler updale
Retrard
(8) Atrastaaee
Figure 3: Update of matrices regression coefficients on QNLI (c) Average performance across ablations of four reward components on NLU and NLG tasks. (d) Ablation studies reward ranking information for advantage estimation.
various layers during the training process. (b) Evolution of the
Table 3: Performance comparison on OOD datasets utilizing the RoBERTa- base model. The proposed CDRO framework consistently achieves the highest accuracy among all compared basclines.
Dataset
SST-2
MNLI
QQP
OOD data
IMDB-CCm
IMDE-CAD
HANS
AdyNLI
PAWS
Fine-tuning
84.51
88.39%
67.80%
31.22%
38.45%
Spoch
Span Cutoff
85.53%
89.21%
68.38%
31.14%
38.80%
Figure 4: Accuracy compar-
HiddenCut
87.82%
90.44%
71.16%
32.83%
41.52%
ison between CDRO and
IPT-Adapter
85.01%
88.75%
66.30%
32.54%
38.94%
Causal- -Deuiis
88.45%
91.44%
76.21%
37.53%
44.35%
vanilla REINFORCE+
PCFR
88.51
91.78%
76.64%
38.01
44.62%
on IMDB-Cont using
CDRO (Ours)
89.62%
92.65%
77.68%
39.40%
46.01%
RoBERTa-base model.
Evaluation for OOD Generalization. Models influenced by spurious correlations in training data often exhibit degraded generalization, particularly in OOD scenarios (8] Accordingly, conduct
experiments on three representative tasks from the GLUE benchmark [85] SST-2 (75). MNLI [94),
and QQP [8)) each accompanied by publicly available OOD datasets. For SST-2, the OOD evaluation conducted on the IMDB-Cont [2T] and IMDB-CAD [37] datasets. The OOD datasets MNLI
comprise HANS |99 and AdvNLI (63] while PAWS-QQP [004] serves the OOD dataset for QQP. We employ three widely utilized pretrained language models BERT-base (17), RoBERTa-base [51], and BART-base 42 and report accuracy the evaluation metric. The compared baselines for improving model generalization in OOD scenarios include Span Cutoff [44] HiddenCut [], IPT-
Adapter 22
Causal-Debias [106), and PCFR (28).
Table 3 reports the comparative results using the RoBERTa-base model, while the comparison results for other models are provided in Appendix 5. As shown, the proposed CdRO method significantly outperforms all compared baselines across range of OOD datasets. In particular, yields average performance improvements of 1.16% over the strongest baseline and 7.00% over vanilla fine-tuning. These results demonstrate the effectiveness of CdRO in mitigating spurious correlations and enhancing the resilience capability of LLMs even under distributional shifts.
Analysis Training Process. We investigate the update behavior of parameter matrices during training, categorizing them by layer position and functional type. From Fig. aa), the proportion of updates decreases deeper layers after a period training, indicating that the parameter matrices sensitive causal relationships increasingly concentrate in the earlier and intermediate layers the model converges. The update patterns for different matrix types are shown in Fig. 7 in the Appendix, where query and key matrices are primarily updated during the early stages, while value, up, and down matrices receive more updates later on. Additionally, Fig. (()) shows the evolution of the coefficients in the logistic regression model throughout training. The indicators g and G3 show positive correlations with predictions, while S,,.S, St, S1,- and 87, exhibit negative correlations. These results suggest that layers with lower values of 5) and 3 and matrices with higher values of G) and 03, are more sensitive to causal variations, indicating their crucial role in encoding causal signals.

---

Furthermore, layers with lower variances (i.e., St and S are more likely to be selected, as they consistently capture causal information across different samples.
Ablation and Sensitivity Studies. We conduct ablation studies on the four reward components. As shown in Fig. c),), the model attains its best performance when all four types of rewards are jointly incorporated, underscoring their complementary contributions. We then assess the impact of incorporating reward ranking information into ad advantage estimation. From the results presented in Fig. d), the integration of reward ranking consistently leads to performance improvements. Furthermore, Fig. E presents the accuracy trajectories during training, demonstrating that CDRO steadily outperforms the vanilla REINFORCE++ terms of accuracy.
Conclusion
This study presents novel causality-iffrrmed robust optimization framework, termed CDRO, aimed mitigating LLMSs' reliance on spurious correlations and enhancing their resilience across diverse tasks. Our approach first identifies parameter components capturing causal relationships by analyzing training dynamics in weight matrices across original, counterfactual, and paraphrased samples. These dynamics are modeled via logistic regression mechanism, enabling the automatic and adaptive localization of causality-relevant parameters. To further refine the optimization process, introduce collaborative reinforcement learning strategy that alternately updates the identified causal parameters and the logistic regression model. Extensive experiments on various NLU and NLG tasks demonstrate that CdRO consistently surpasses the compared baselines in mitigating spurious correlations, suppressing knowledge hallucinations, and enhancing overall model performance.
Acknowledgments
This work supported by the NSFC under Grant 625B2009 and the 2025 Chinese Institute of Cinttroiics-eenneen PhD Research Incentive Program.
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Reviews:
[{"review_id": "kXZbOKWdWc", "reviewer": "Reviewer_z2Ph", "summary": "This paper proposes a new robust optimization framework based on causality, called CDRO, aiming to reduce LLMS 'reliance on false correlations and enhance their resilience across various tasks.   Specifically, the method proposed in this work first identifies the parameter components that capture causal relationships by analyzing the training dynamics in the weight matrices of the original samples, counterfactual samples, and paraphrase samples.   These dynamics are modeled through the logistic regression mechanism, making it possible to automatically and adaptively locate the parameters related to causal relationships.   Meanwhile, to further improve and optimize the process, the author introduced a collaborative reinforcement learning strategy, alternately updating the identified causal parameters and the logistic regression model.   A large number of experiments on various NLU and NLG tasks have shown that CDRO consistently outperforms the comparison baseline in reducing false correlations, suppressing knowledge illusions, and improving overall model performance.", "questions": "My question is recorded in the weakness section", "limitations": "Refer to the weakness.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths The motivation of this paper is clear and the writing is concise and of high quality.   The experimental section is relatively comprehensive, with extensive evaluations conducted on the proposed method.   Additionally, the method has been compared with various baseline approaches, further highlighting its effectiveness.\n\nWeaknesses 1.  The author mentioned that LLMS have counterfactual issues.   However, during the data construction stage, prompt LLMS are used for data generation.   Does this mean that the data quality depends on the LLM itself?   In other words, how can we ensure that the generated data is trustworthy?\n \n2.  The author mentioned that other methods have fine-tuned all the parameters of the model, but the method proposed by the author only fine-tunes some parameters.   Efficient parameter fine-tuning methods are common.   What are the general implementation methods of other baselines like?   Is there really no method that only fine-tunes some parameters?\n\n3.  In the experimental section, why weren't all the baselines compared on some datasets or some models?\n\n4.   Has the author conducted experiments on datasets for hallucinations?   Hallucination evaluation on the NLG task may not well reflect the hallucination problem of the model.", "was_revised": true, "final_justification": "Overall, I think this paper is between borderline reject and borderline accept. The biggest concern is that it relies on a more advanced instruction-following LLM.", "rebuttal": "Dear Reviewer z2Ph,\n\nWe sincerely thank you for your thoughtful review and constructive comments. We greatly appreciate your recognition of the clarity of our research motivation, the quality of our writing, the comprehensiveness of our experiments, and the superiority of our performance. Below are our detailed responses to each of your concerns:\n\n> **Q1: The trustworthiness of the generated data.**\n\nTo minimize costs and leverage the established capability of LLMs [1,2] to generate counterfactual and paraphrased samples, the entire data collection process is conducted by directly prompting off-the-shelf LLMs. Rigorous design and careful considerations ensure the reliability of the generated data.\n- We utilize **advanced instruction-following LLMs** for the data collection process, including GPT-4o and LLaMA-3-70B, which exhibit high stability and reliability in generation tasks [3,4].\n- To minimize potential biases, we **generate multiple counterfactual and paraphrased samples** for each original sample. These are then **evaluated three times across multiple dimensions**, including alignment or divergence between the answers of augmented and original samples, answer correctness, thematic consistency, clarity, and safety and privacy. Specifically, we generate eight independent samples per original input and **select the highest-scoring one** for downstream use, which mitigates the randomness or errors inherent in single-generation results.\n- We evaluate the performance of using the same LLM for both generation and evaluation, as well as employing a different LLM for evaluation. As shown in Table 6, both approaches yield comparable and satisfactory performance. Moreover, the Pearson correlation coefficient between the scores produced by the two approaches is 0.84. These findings suggest that **either method can effectively evaluate the generated samples**. \n- As noted in Footnote 5, the application of the generated samples is weighted by their evaluation scores, thereby ensuring that **higher-quality samples have a more dominant influence**.\n- CDRO consistently improves performance across diverse tasks and models, as demonstrated in our experiments. Since **both localization and fine-tuning rely on the augmented data, these gains indirectly validate the quality of the generated samples**.\n- We further conduct ablation experiments to demonstrate the effectiveness of both generated counterfactual and paraphrased samples. The results demonstrate that **omitting either augmentation leads to performance degradation**, highlighting the essential contribution of each.\n|Task|NQ||SciQ||TriviaQA||TruthfulQA||WikiQA||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|Acc@50(↑)|Cov@60(↑)|Acc@50 (↑)|Cov@40(↑)|Acc@50 (↑)|Cov@50 (↑)|\n|CDRO|**0.376**|**0.228**|**0.781**|**0.245**|**0.520**|**0.258**|**0.456**|**0.529**|**0.665**|**0.627**|\n|W/o paraphrase|0.375|0.224|0.777|0.243|0.519|0.255|0.450|0.528|0.662|0.624|\n|W/o counterfactual|0.359|0.220|0.769|0.233|0.505|0.247|0.436|0.514|0.657|0.621|\n\n> **Q2: Other methods that only fine-tune partial parameters.**\n\nWe humbly address your concerns from the following perspectives:\n- Existing debiasing and hallucination mitigation methods (e.g., Causal-Debiasing [5], PCFR [6], HaDeMiF [7]) typically rely on **indiscriminate fine-tuning of all model parameters, without distinguishing between components that encode genuine causal knowledge and those that are most important for debiasing**.\n- As you mentioned, some parameter-efficient fine-tuning (PEFT) methods also update only a subset of model parameters, typically falling into two categories. The first is **selective PEFT**, which targets parameters deemed important for downstream tasks. However, such methods typically predefine fixed parameter types (e.g., bias-only) [8] or preselect a parameter subset that remains unchanged throughout training [9], thereby limiting adaptability. The second category comprises **low-rank adaptation methods** (e.g., LoRA [8], PiSSA [9]), which constrain updates to low-rank matrices but apply them uniformly across all weight matrices, regardless of task-specific relevance. In contrast, our proposed CDRO framework **combines parameter efficiency with causality-aware selectivity**. It dynamically identifies and fine-tunes only those parameter matrices most critical for encoding causal relationships, thereby enhancing causal reasoning while minimizing redundant updates.\n- The 'Label Smoothing' baseline in Table 2 refers to fine-tuning with label smoothing loss in combination with LoRA, which yields suboptimal performance. To further highlight **the superior effectiveness of our method**, we conduct empirical comparisons with vanilla LoRA, PiSSA, and BitFit, all of which demonstrate poor performance on hallucination mitigation tasks.\n|Task|NQ||SciQ||TriviaQA||TruthfulQA||WikiQA||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|Acc@50(↑)|Cov@60(↑)|Acc@50 (↑)|Cov@40(↑)|Acc@50 (↑)|Cov@50 (↑)|\n|LoRA|0.201|0.060|0.215|0.006|0.289|0.015|0.176|0.000|0.257|0.000|\n|PiSSA|0.203|0.061|0.209|0.001|0.302|0.018|0.180|0.002|0.278|0.003|\n|BitFit|0.197|0.054|0.210|0.002|0.267|0.010|0.174|0.000|0.260|0.000|\n|CDRO|**0.376**|**0.228**|**0.781**|**0.245**|**0.520**|**0.258**|**0.456**|**0.529**|**0.665**|**0.627**|\n- To further clarify the distinction between PEFT methods that fine-tune partial parameters and our proposed approach, we have added the following content to the **Related Work** section: “*Some parameter-efficient fine-tuning (PEFT) methods also update only a subset of model parameters. For example, selective PEFT methods [45,53] aim to fine-tune parameters most relevant to downstream tasks. However, they often predefine fixed parameter types (e.g., bias terms) or preselect a parameter subset based on a single static criterion, which remains unchanged throughout training and thereby limits adaptability. Moreover, low-rank adaptation methods [61,65] reduce trainable parameters by restricting updates to low-rank matrices, but apply such modifications uniformly across all weights, without accounting for task-specific importance. In contrast, our method…*”\n\n> **Q3: Compared baselines.**\n\nTo the best of our knowledge, our study is **the first to integrate debiasing, hallucination mitigation, and out-of-distribution (OOD) generalization into a unified experimental framework**. In contrast, prior studies typically address these tasks independently, with baselines exhibiting limited generalizability and challenges in cross-task deployment. To comprehensively evaluate our approach, we compare it against SOTA methods specialized for each task. Notably, during experimental investigation, we have tried to extend certain methods, such as Causal-Debias [5] and PCFR [6], which are originally designed for debiasing, to enhance OOD generalization (Tables 3 and 10). However, since both methods can only address specific types of bias (i.e., gender and race) and require full model fine-tuning, their applicability to LLMs for hallucination mitigation is limited.\n\n> **Q4: More experiments for hallucination mitigation.**\n\nFollowing prior research [7], we evaluate the performance of our approach for hallucination mitigation using five representative NLG benchmarks (NQ, SciQ, TriviaQA, TruthfulQA, and WikiQA) across eight LLMs ranging from 1.5B to 30B parameters. The results are presented in Table 2 and Fig. 10 of our manuscript. \n\nHowever, we fully acknowledge that additional experiments could further substantiate the effectiveness of our approach. Consequently, we conduct **further evaluations using LLaMA-2-7B and LLaMA-3-8B models on two hallucination datasets: FACTOR and HaluEval-Sum**.\n- FACTOR [12] focuses on context consistency and measures the tendency of LLMs to generate factual information. The task includes two datasets: Wiki-FACTOR (2994 examples) and News-Factor (1036 examples). Factuality is assessed by whether the model assigns the highest likelihood to the factually correct completion over the other options.\n- HaluEval-Sum [13] provides texts paired with both hallucinated and correct responses. For each sample, LLMs are tasked with determining whether the provided summary contains hallucinated information relative to the given document. We report accuracy for both hallucinated and correct summaries, including arithmetic mean accuracy (Acc-A) and harmonic mean accuracy (Acc-H).\n\n|Data|FACTOR||HaluEval||\n|:-|:-:|:-:|:-:|:-:|\n||Wiki|News|Acc-A|Acc-H|\n|LLaMA-2-7B|58.64|72.25|48.01|19.92|\n|+DoLa|60.13|72.86|48.65|27.78|\n|+SH2|64.10|73.59|50.49|50.50|\n|+CDRO|**65.29**|**74.76**|**51.58**|**51.66**| \n|LLaMA-3-8B|65.59|78.42|55.07|28.95|\n|+DoLa|68.90|79.71|54.92|30.43|\n|+SH2|70.03|80.55|60.38|53.81|\n|+CDRO|**71.05**|**82.45**|**61.27**|**55.03**|\n\nThe results unequivocally demonstrate the superior performance of the proposed CDRO approach in hallucination elimination.\n\n[1] Autocad: Automatically generate counterfactuals for mitigating shortcut learning.\n\n[2] Prompting large language models for counterfactual generation: An empirical study.\n\n[3] The llama 3 herd of models.\n\n[4] Gpt-4o system card.\n\n[5] Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning.\n\n[6] Towards fair decision: A novel representation method for debiasing pre-trained models.\n\n[7] Hademif: Hallucination detection and mitigation in large language models.\n\n[8] BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language models.\n\n[9] Training neural networks with fixed sparse masks.\n\n[10] Lora: Low-rank adaptation of large language models.\n\n[11] Pissa: Principal singular values and singular vectors adaptation of large language models.\n\n[12] Generating benchmarks for factuality evaluation of language models.\n\n[13] HaluEval: A large-scale hallucination evaluation benchmark for large language models."}, {"review_id": "FTXwRF0iWC", "reviewer": "Reviewer_QZLY", "summary": "This study introduces a novel Causality-driven Robust Optimization (CDRO) approach to enhance the causal reasoning abilities of Large Language Models (LLMs), reducing their reliance on spurious correlations and mitigating hallucinations. CDRO first identifies causality-sensitive parameters in LLMs by comparing training dynamics across original samples, augmented counterfactual samples, and paraphrased variants, using a lightweight logistic regression model. These identified parameters are then optimized with an enhanced policy optimization algorithm, whose reward function promotes both generalization and robustness. Extensive experiments with twelve different LLMs across various tasks demonstrate CDRO's superiority in reducing spurious associations, mitigating hallucinations, alleviating biases, and improving out-of-distribution generalization. The key contributions include a new method for localizing causal knowledge in LLMs, a collaborative optimization framework, and validation of effectiveness through comprehensive experiments.", "questions": "1. Given that the study uses prompted LLMs to score constructed counterfactual and paraphrased samples, could this LLM-based evaluation method inherit the inherent biases or limitations of the evaluating models, leading to inconsistent or inaccurate assessments of sample quality? And might the reliance on such model-in-the-loop scoring—especially when using the same or similar LLMs for both generation and evaluation—create closed-loop biases that fail to align with human judgment or real-world task requirements, thereby undermining the effectiveness of the augmented data?\t\n2. Given that the study claims to selectively update Causality-Sensitive Parameters rather than all model parameters, several questions arise regarding the clarity of this process: How exactly are the \"causality-sensitive\" characteristics of these parameters operationally defined beyond the mentioned gradient differences and cosine similarities of hidden states? Is there empirical evidence to confirm that the localized parameters indeed encode causal relationships rather than other types of task-relevant information? Additionally, how is the threshold for selecting these parameters (e.g., \"matrices with predicted probabilities exceeding the average\") determined, and does this threshold remain robust across different model architectures or task domains? Finally, since only specific parameter components are updated, is there a risk of creating imbalances in the model's internal representations, and how does the framework mitigate such potential side effects?\n3. Given that CDRO aims to enhance causal reasoning by selectively optimizing parameters identified through comparisons of training dynamics across original, counterfactual, and paraphrased samples, how does the framework account for the possibility that causal relationships in real-world scenarios are often complex, multi-faceted, or context-dependent—far more intricate than the binary or simplified causal signals captured by the generated counterfactuals? Moreover, could the reliance on predefined types of augmented samples (counterfactuals and paraphrases) limit the model’s ability to learn causal structures that cannot be easily approximated by such perturbations, and if so, how might the framework be adapted to capture more nuanced causal dependencies?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- Effectively enhances LLMs' causal reasoning abilities, reducing reliance on spurious correlations and mitigating hallucinations across various tasks .\n- Outperforms baselines in debiasing (gender and race) and improving OOD generalization, with superior performance on both NLU and NLG tasks.\n- Adopts a parameter-efficient optimization strategy, preserving pretrained knowledge while enhancing downstream task performance .\n- The collaborative optimization framework (logistic regression + enhanced REINFORCE++) enables dynamic localization of causal components, ensuring adaptability .\n- Comprehensive experiments with 12 LLMs validate its robustness and generalizability .\n\n**Weaknesses**:\n- Introduces additional time complexity due to the adaptive knowledge localization module, though the trade-off is justified by performance gains .\n- The effectiveness of causal component localization heavily depends on the quality of counterfactual and paraphrased samples, posing challenges in data augmentation. Moreover, there is a lack of ablation experiments on counterfactual and paraphrased samples, making the significance of paraphrased samples questionable.\n- The layout of tables and figures is suboptimal. In the appendix, the places where figures and tables are cited are far from their actual positions, which may cause confusion for readers.\n- The description of parameter updates for the selected model components is not clear enough, with only limited information provided in the flowcharts.", "was_revised": true, "final_justification": "We thank the authors for their thorough and constructive rebuttal, as well as for the additional experiments and clarifications provided. The new ablations, cross-model validations, and detailed explanations effectively address the reviewers’ core concerns while highlighting the paper’s strengths. After careful consideration, we maintain our initial “Borderline Accept: Technically solid paper where reasons to accept outweigh reasons to reject” rating. We look forward to seeing the minor revisions incorporated in the camera-ready version.", "rebuttal": "Dear Reviewer QZLY,\n\nWe sincerely appreciate your thorough review and insightful comments. Thank you for your recognition of the significance and novelty of our work, the adaptability and parameter efficiency of our framework, the comprehensiveness of our experiments, and the effectiveness of our approach. Below, we provide detailed responses to your concerns:\n\n> **Q1: Additional time complexity.**\n\nAs you mentioned, despite the added complexity, our approach achieves superior parameter efficiency—improving by **up to 58%** (Table 15)—striking a better balance between preserving pretrained knowledge and enhancing downstream task performance. Moreover, it consistently **attains top performance** across diverse tasks and models. Furthermore, the logistic model **converges after a limited number of iterations** (Figs. 3(b) and 11), enabling the omission of logistic updates in later training stages, further improving efficiency.\n\n> **Q2: Questions about data collection.**\n\nWe humbly respond to your concerns from the following perspectives:\n- To minimize costs and leverage the established capability of LLMs [1,2] to generate counterfactual and paraphrased samples, the entire data collection process is conducted by prompting off-the-shelf LLMs. Rigorous design and careful considerations ensure the reliability of the generated data.\n  - We utilize **advanced LLMs** for the data collection process, including GPT-4o and LLAMA-3-70B, which exhibit high stability and reliability in generation tasks [3,4].\n  - We **generate eight counterfactual and paraphrased variants per sample and evaluate each three times across multiple dimensions**. The **highest-scoring** variant is then selected for downstream use, mitigating randomness and errors inherent in single generations.\n  - Despite potentially introducing biases, LLM-based evaluation remains a widely adopted practice. To improve reliability, **we employ carefully designed prompts and conduct multiple independent evaluations per sample**. Additionally, we compare evaluations using the same LLM as for generation against a different LLM. From Table 6, the two settings yield comparable and satisfactory results, with a Pearson correlation of 0.84, indicating **reliable assessment of the generated samples**.\n  - As noted in Footnote 5, the application of the generated samples is weighted by their evaluation scores, ensuring that **higher-quality samples have a more dominant influence**.\n- CDRO consistently improves performance across diverse tasks and models, as indicated in our experiments. Since **both localization and fine-tuning rely on the augmented data, these gains indirectly validate the quality of the generated samples**.\n- Motivated by your feedback, we conducted ablation studies to assess the impact of counterfactual and paraphrased samples. Results show that **omitting either leads to reduced performance**, confirming their effectiveness. Besides, from Figs. 3(b) and 11, all indicator coefficients are non-zero at model convergence, demonstrating each indicator’s contribution and highlighting the importance of jointly considering counterfactual and paraphrased samples.\n|Task|NQ||SciQ||TriviaQA||TruthfulQA||WikiQA||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|Acc@50(↑)|Cov@60(↑)|Acc@50 (↑)|Cov@40(↑)|Acc@50 (↑)|Cov@50 (↑)|\n|CDRO|**0.376**|**0.228**|**0.781**|**0.245**|**0.520**|**0.258**|**0.456**|**0.529**|**0.665**|**0.627**|\n|W/o paraphrase|0.375|0.224|0.777|0.243|0.519|0.255|0.450|0.528|0.662|0.624|\n|W/o counterfactual|0.359|0.220|0.769|0.233|0.505|0.247|0.436|0.514|0.657|0.621|\n\n> **Q3: Layout of tables and figures in the appendix.**\n\nWe apologize for the improper placement of figures and tables. The appendix has been revised to **position them as close as possible to their corresponding citations**.\n\n> **Q4: Parameter updates for the selected components.**\n\nWe apologize for the previous lack of clarity. **Section 3.3 (Lines 196-238)** details the updates of the selected components, which are performed using an enhanced REINFORCE++ algorithm. The optimization objective in Eq. (2) incorporates a composite reward function combining accuracy, robustness, calibration, and confidence. Moreover, only the low-rank principal components of these matrices are updated following PiSSA [5] to improve efficiency. \n\nTo enhance clarity, Section 3.3 has been **reorganized into two subsections**: \"*Optimization Process for the Selected Components of LLMs*\" and \"*Optimization Process for the Logistic Model*\". Each subsection **begins with a topic sentence summarizing the corresponding optimization strategy**. We have also added more details about the optimization of LLMs in Fig.2.\n\n> **Q5: Selection of causality-sensitive parameters.**\n\nWe respectfully address your concerns from the following perspectives: \n- Our motivation for identifying causal-sensitive parameters is based on the idea that **such parameters exhibit behavioral differences, such as loss gradients and activation values, between original and counterfactual samples**. This approach is consistent with prior localization methods that analyze prediction changes induced by perturbations to parameters or activations [6,7]. However, manually determining the influence of various indicators on localization is unreasonable. To address this, we propose utilizing a simple logistic model to automatically learn this relationship. Future work may extend this by incorporating more sophisticated indicators, such as projections of activations onto model parameters or the Fisher information matrix, to enhance identification accuracy.\n- As shown in **Figs. 3(b) and 11**, the relationship between causal localization and selected indicators aligns with our expectations. Specifically, gradient difference coefficients are positive, whereas hidden state cosine similarity coefficients are negative. These results confirm that **the identified parameters exhibit distinct behaviors between original and counterfactual samples, supporting their sensitivity to causal relationships**.\n- Naturally, relying solely on original and paraphrased samples may cause localized parameters to capture superficial statistical patterns. **Ablation results in Q2** show that excluding counterfactuals leads to a significantly larger performance decline than excluding paraphrased samples, indicating that counterfactual augmentations enable **localized parameters to capture causal relationships rather than statistical correlations**.\n|Dataset|SST-2||MNLI||QQP||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|OOD data|IMDB-Cont|IMDB-CAD|HANS|AdvNLI|PAWS|\n|Average|**89.62**|**92.65**|**77.68**|**39.40**|**46.01**|\n|Top-15|89.19|92.37|77.54|38.98|45.43|\n|0.5|89.23|92.48|77.35|38.92|45.66|\n- The threshold for selecting causal components is set as the average logistic probability across all parameter matrices, with matrices having probabilities above this threshold being updated. Due to the trainable nature of the logistic model, this threshold is dynamic. We compare three threshold strategies—Top-15 matrices, matrices with probabilities $> 0.5$, and the average threshold—across two tasks: OOD generalization (RoBERTa-base) and hallucination mitigation (LLaMA-2-7B). **Our dynamic threshold consistently outperforms fixed manual ones, demonstrating robustness across tasks and models**.\n|Task|NQ||SciQ||\n|:-|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|\n|Average|**0.376**|**0.228**|**0.781**|**0.245**|\n|Top-15|0.373|0.225|0.775|0.238|\n|0.5|0.369|0.226|0.778|0.240|\n- CDRO **avoids internal model imbalance by dynamically updating different matrices throughout training rather than fixing them**. As shown in **Figs. 3(a) and 13**, updates occur across various layers and matrix types, albeit to varying degrees. Moreover, **Figs. 4 and 12** indicate that our CDRO optimization achieves greater stability and higher accuracy compared to standard REINFORCE++.\n\n> **Q6: Complex causal relationships.**\n\nInsightful question! We humbly respond to your concerns from the following aspects:\n- We fully acknowledge the inherent complexity of causal relationships, which often arise from multiple factors—for instance, heart disease may stem from diet, genetics, and more. Nevertheless, **counterfactuals offer a clear and effective approach to model and interpret causal relationships**. While generating counterfactual samples for all possible causal relationships is impractical, integrating causal knowledge from the augmented counterfactuals represents a significant advancement in model learning. Accordingly, we identify causal-sensitive components by comparing parameter behaviors between original and counterfactual samples and update the identified ones. **As shown in Figs. 4 and 12, CDRO enhances both stability and effectiveness**.\n- We utilize an enhanced policy optimization method to update the identified components, with the reward incorporating multiple metrics. The observed gains in various tasks demonstrate that incorporating counterfactual samples **strengthens both the model’s causal reasoning and robustness**.\n- Your feedback has **inspired further extensions to our framework**. One direction is to generate multiple counterfactuals per original sample, each targeting different aspects, to enable more precise localization and optimization, albeit with increased augmentation complexity. Additionally, conducting counterfactual augmentation in the deep semantic space could help capture more nuanced causal relationships.\n\n[1] Autocad: Automatically generate counterfactuals for mitigating shortcut learning.\n\n[2] Prompting large language models for counterfactual generation: An empirical study.\n\n[3] The llama 3 herd of models.\n\n[4] Gpt-4o system card.\n\n[5] Pissa: Principal singular values and singular vectors adaptation of large language models.\n\n[6] Locating and editing factual associations in gpt.\n\n[7] Mass-editing memory in a transformer."}, {"review_id": "99zJdlUVSg", "reviewer": "Reviewer_PboH", "summary": "This paper focuses on addressing the hallucination issue in LLMs by optimizing causal relations. The approach first identifies causal knowledge and then optimizes it using reinforcement learning. Experiments show that it performs well on several NLU tasks.", "questions": "1. I think \"enhancing model causality while preserving valuable pretrained knoweldge\" is closedly related to continual leanring. I strongly recomment the author to add a related work section on continual leanrning (see above W1 and potential reference)\n\n2. Could you expand the evaluation? The current evaluation is relatively simple and could benefit from broader or more challenging benchmarks (see W2 above).", "limitations": "No discussion in the main content. Perhaps the author can add some cost analysis", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n1. It is an important problem to solve: avoid the hallucination where the output is based on spurious relations\n2. The writing is generally clear and the figure is informative\n\n**Weaknesses:**\n\n1. The \"locate and optimize\" strategy has been widely used in the continual learning community [1, 2, 3]. It would be helpful if the authors could compare their approach with these existing methods to better situate their contribution.\n2. The authors should consider applying their method to LLMs and evaluating it on more complex tasks. The current models and datasets listed in Table 1 are too simple (i.e., SoTA LLM can easily achieve 90%+ acc.), which limits the informativeness of the evaluation.\n\n[1]: Continual Pre-training of Language Models, ICLR 2023.    \n[2]: Locating and Editing Factual Associations in GPT, NeurIPS 2022.    \n[3]: Continual learning of natural language processing tasks: A survey， 2022", "was_revised": true, "final_justification": "The related work and additional experiemnts addresed my concerns. I raised score accordingly.", "rebuttal": "Dear Reviewer PboH,\n\nWe deeply appreciate your valuable review and insightful comments. We greatly thank you for your recognition of the significance of the problem we address, the quality of our paper, the richness of the figures, and the strong performance of our approach across various tasks. Below are our detailed responses to each of your concerns:\n\n> **Q1: Relation to continual learning.**\n\nWe fully agree with you that enhancing model causality while preserving valuable pretrained knowledge has commonalities with the idea in continual learning, which aims to improve performance on new tasks without forgetting previous ones. Inspired by your valuable feedback, we have conducted extensive research and compared our approach with related methods as follows:\n\n- Several knowledge editing approaches follow a two-step paradigm of localization followed by optimization [1,2], which have been involved in the **“Knowledge Localization” section of Related Work**. These methods typically perturb model parameters or sample activations to identify the location of knowledge by observing changes in model predictions, and then edit only the identified parameters. However, these approaches can **track probability changes for individual tokens only at a time, and are primarily suited to structured triplet data, restricting their applicability to more general or unstructured language scenarios**. Furthermore, as noted in **Lines 64–67**, these methods perform localization at the layer level based on predefined matrix types (e.g., in feed-forward networks), which is often coarse-grained and inflexible. In contrast, our method employs a dynamically learned localization network to perform fine-grained localization at the matrix level, offering greater precision and adaptability.\n- Another related line of work focuses on selecting which weight matrices to update for new tasks based on their importance. However, these methods often rely on a single metric, such as gradients, for soft masking or apply masking to predefined modules, resulting in limited accuracy and flexibility [3,4]. Additionally, continual learning methods primarily aim to learn new tasks without forgetting previous ones, but they **cannot explicitly model or enhance the causal relationships within the model**. In contrast, our approach identifies causal-sensitive parameter matrices by analyzing behavioral differences across original and augmented counterfactual samples using a dynamically learned localization network. These identified parameters are then optimized using an enhanced policy optimization algorithm guided by four reward signals: accuracy, robustness, calibration, and confidence, thereby improving the model's generalization and robustness.\n\nTo more clearly delineate the differences between our approach and those in continual learning, we have **added the following content to the Related Work section**: “*Some knowledge editing approaches utilize parameter-based or activation-based techniques for knowledge localization [21,62,64], as outlined before, wherein only the identified parameters are subsequently updated. However, these methods are typically constrained to tracking the probability shift of a single token at a time and are primarily designed for structured triplet-style data, which limits their applicability to more general or unstructured language scenarios. Moreover… Another related line of work focuses on selecting which weight matrices to update for new tasks based on their importance [45,46]. However, most of these methods rely on single metrics like gradients for soft masking or apply masking to predefined modules, which provides a limited perspective. Moreover, these approaches primarily aim to learn new tasks while preserving knowledge in old ones, but they cannot explicitly model or enhance the model’s internal causal relationships. In contrast, our framework…*”\n\n> **Q2: Experiments on more tasks and LLMs.**\n \nThe experiments in our manuscript cover three important tasks, including **debiasing, hallucination mitigation, and out-of-distribution generalization**, using **twelve LLMs ranging in size from 18M to 30B parameters**. To ensure fair and meaningful comparisons, the experimental settings for Table 1—including datasets and model selections—follow those used in previous SOTA debiasing studies [5,6]. \n\nIn response to your valuable suggestion, we have extended our evaluation to more challenging tasks and LLMs. \n\n- First, we conduct additional evaluations on two challenging hallucination datasets, including **FACTOR** and **HaluEval-Sum**, using LLaMA-2-7B and LLaMA-3-8B models.\n  - FACTOR [7] focuses on context consistency and measures the tendency of language models to generate factual information. It is a text completion task where the goal is to identify the correct completion from nonfactual statements given a prefix. The task includes two datasets from different sources: Wiki-FACTOR (2994 examples) and News-Factor (1036 examples). Factuality is assessed by whether the model assigns the highest likelihood to the factually correct completion over the other options.\n  - HaluEval-Sum [8] provides texts paired with both hallucinated and correct responses. We use its summarization track to evaluate the truthfulness of LLMs on longer sequences, consisting of 10,000 samples. For each sample, LLMs are tasked with determining whether the provided summary contains non-factual or hallucinated information relative to the given document. We report accuracy for both hallucinated and correct summaries, including arithmetic mean accuracy (Acc-A) and harmonic mean accuracy (Acc-H).\n|Dataset|FACTOR||HaluEval||\n|:-|:-:|:-:|:-:|:-:|\n||Wiki|News|Acc-A|Acc-H|\n|LLaMA-2-7B|58.64|72.25|48.01|19.92|\n|+DoLa|60.13|72.86|48.65|27.78|\n|+SH2|64.10|73.59|50.49|50.50|\n|+CDRO|**65.29**|**74.76**|**51.58**|**51.66**| \n|LLaMA-3-8B|65.59|78.42|55.07|28.95|\n|+DoLa|68.90|79.71|54.92|30.43|\n|+SH2|70.03|80.55|60.38|53.81|\n|+CDRO|**71.05**|**82.45**|**61.27**|**55.03**|\n- Subsequently, we provide detailed comparison results for hallucination mitigation between our method and the best-performing baselines (HaDeMiF, ICLR 2025) on five NLG tasks—NQ, SciQ, TriviaQA, TruthfulQA, and WikiQA—evaluated using the **LLaMA-3-8B, LLaMA-2-13B, Qwen-2.5-7B, and LLaMA-30B** models.\n|Task|NQ||SciQ||TriviaQA||TruthfulQA||WikiQA||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|Acc@50(↑)|Cov@60(↑)|Acc@50 (↑)|Cov@40(↑)|Acc@50 (↑)|Cov@50 (↑)|\n|LLaMA-3-8B|||||||||||\n|HaDeMiF|0.542|0.516|0.892|0.459|0.620|0.369|0.590|0.721|0.620|0.458|\n|CDRO|**0.580**|**0.625**|**0.918**|**0.490**|**0.658**|**0.405**|**0.620**|**0.755**|**0.650**|**0.580**|\n|LLaMA-2-13B|||||||||||\n|HaDeMiF|0.491|0.415|0.842|0.405|0.486|0.270|0.441|0.613|0.655|0.434|\n|CDRO|**0.518**|**0.520**|**0.885**|**0.440**|**0.517**|**0.305**|**0.512**|**0.648**|**0.720**|**0.520**|\n|Qwen-2.5-7B|||||||||||\n|HaDeMiF|0.588|0.550|0.909|0.492|0.688|0.421|0.646|0.760|0.692|0.532|\n|CDRO|**0.613**|**0.650**|**0.928**|**0.530**|**0.713**|**0.478**|**0.668**|**0.805**|**0.724**|**0.630**|\n|LLaMA-30B|||||||||||\n|HaDeMiF|0.512|0.503|0.899|0.435|0.469|0.264|0.542|0.697|0.462|0.218|\n|CDRO|**0.529**|**0.620**|**0.903**|**0.470**|**0.495**|**0.315**|**0.562**|**0.737**|**0.478**|**0.402**|\n\nAll of the above experiments will be incorporated into the camera-ready version of our manuscript. As demonstrated, despite variations in models and datasets, **our method consistently delivers robust and effective performance**.\n\n> **Q3: No limitation discussion in the main content. Perhaps the author can add some cost analysis.** \n\nDue to space constraints, the limitations are presented in **Section A.13 of the appendix**. Following your valuable suggestion, a condensed version will be incorporated into the main text as part of the “Conclusion and Discussion” section. Moreover, the complexity analysis is presented in **Section A.9 of the appendix**, which indicates that despite the increased time complexity introduced by the adaptive knowledge localization module, our approach demonstrates **superior parameter efficiency** (Table 15, with an improvement of **up to 58%**), thereby achieving a better tradeoff between preserving pretrained knowledge and enhancing downstream task performance. Moreover, it consistently **achieves the best performance** across various tasks and models. Additionally, the logistic model **converges after a limited number of training iterations** (as shown in Figs. 3(b) and 11), allowing the omission of the logistic update step in subsequent training stages, further improving training efficiency. It is worth noting that while our method incurs some additional computational overhead, it remains a highly efficient fine-tuning approach, particularly when compared to the full fine-tuning for LLMs. A condensed cost analysis will be incorporated into Section 4 of the manuscript.\n\n[1] Locating and editing factual associations in gpt. NeurIPS 2022.\n\n[2] Mass-editing memory in a transformer. ICLR 2022.\n\n[3] Continual pre-training of language models. ICLR 2023.\n\n[4] Continual learning of natural language processing tasks: A survey.\" arXiv preprint arXiv:2211.12701 (2022).\n\n[5] Towards fair decision: A novel representation method for debiasing pre-trained models. Decision Support Systems 2024.\n\n[6] Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning. ACL 2023.\n\n[7] Generating benchmarks for factuality evaluation of language models. ACL 2024.\n\n[8] HaluEval: A large-scale hallucination evaluation benchmark for large language models. EMNLP 2023."}, {"review_id": "QzvOEoPvkP", "reviewer": "Reviewer_6STb", "summary": "The manuscript presents a novel Causality-driven Robust Optimization (CDRO) approach that selectively updates model components sensitive to causal reasoning. Extensive experiments across various tasks and LLMs architectures demonstrate the superior performance of the proposed method.", "questions": "1. How was the threshold determined for selecting causal components? Specifically, the appendix mentions selecting matrices with \"predicted probabilities exceeding the average predicted probability,\" but is this optimal? Were other selection strategies considered?\n2. How do you verify that the identified components truly encode causal relationships rather than other statistical patterns? Could you provide ablation studies or visualization evidence showing that these components specifically capture causal rather than correlational information?\n3. How do the training time and computational requirements compare between CDRO and other methods? The dual optimization process (updating both the logistic regression model and the LLM parameters) may introduce computational complexity.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. CdRO addresses a critical challenge in LLM development: enhancing causal reasoning without extensive retraining or parameter inefficiency.\n2. There is a clear problem formulation that effectively motivates the need for parameter-efficient causal reasoning enhancement.\n3. The manuscript has a well-structured presentation with logical flow from theoretical foundations to implementation details and experimental results.\n\nWeaknesses:\n1. The explanation of how loss gradients capture model sensitivity needs more intuitive examples or visualizations.\n2. The computational overhead introduced by the knowledge localization process could present scalability challenges for very large models, though this is partially mitigated by the parameter efficiency.\n3. There is limited discussion of potential negative effects or failure cases, in which the approach might underperform relative to baselines.\n4. How stable is the identification of causal components across different runs or different datasets? Is there consistency in which components are identified as causality-sensitive?", "was_revised": true, "final_justification": "The authors have resolved most of my concerns.  I decide to give it a Borderline accept score.", "rebuttal": "Dear Reviewer 6STb,\n\nWe sincerely appreciate your insightful review and valuable comments and suggestions. Thank you very much for recognizing the novelty and significance of our research, the clarity of the problem formulation, the structured presentation, the comprehensiveness of the experiments, and the strong performance of our method. Below, we provide detailed responses to each of your concerns:\n\n> **Q1: Relationship between loss gradients and causal sensitivity.**\n\nWe humbly respond to your concerns from the following three points:\n- The motivation for identifying causal-sensitive parameters stems from the idea that **if a parameter is causal-sensitive, its behavior—such as loss gradients and activation values—will differ between the original sample and its corresponding counterfactual**. This concept is akin to previous methods of parameter or activation-based localization, where model parameters or sample activations are perturbed, and changes in model predictions are analyzed for localization purposes [1,2]. However, manually determining the impact of different behavioral indicators on localization results is unreasonable. To address this, we propose utilizing a simple logistic model to automatically learn this relationship. \n- As discussed in Section 3.2, we select loss gradients and activation values because they **capture complementary aspects of model behavior**: loss gradients reflect the model’s sensitivity to specific parameter matrices during training, while activation values indicate the information flow and layer-wise responses to input data.\n- As illustrated in **Figs. 3(b) and 11**, the relationship between causal localization results and the utilized indicators aligns with our expectations. The table below further provides the converged coefficients for each indicator on three datasets. Specifically, the coefficients ($\\beta_{1}$ and $\\beta_{2}$) for both gradient difference indicators are positive, whereas those ($\\beta_{3}$ and $\\beta_{4}$) for the cosine similarity of hidden states are negative. This suggests that **larger gradient differences and lower cosine similarity of activation values are associated with a higher likelihood of a matrix being causal-sensitive**. Additionally, the coefficients ($\\beta_{5}$ and $\\beta_{6}$) for the variance of cosine similarities are negative, primarily because lower variance indicates more consistent capture of causal information across different samples.\n|Coefficient|$\\beta_{1}$|$\\beta_{2}$|$\\beta_{3}$|$\\beta_{4}$|$\\beta_{5}$|$\\beta_{6}$|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|QNLI|0.080|0.103|-0.303|-0.031|-0.137|-0.097|\n|SST-2|0.117|0.110|-0.273|-0.252|-0.140|-0.165|\n|SciQ|0.070|0.105|-0.290|-0.273|-0.150|-0.130|\n\n> **Q2: Computational overhead.**\n\nAs you mentioned, despite the increased time complexity introduced by the dual optimization process, our approach demonstrates **superior parameter efficiency**, thereby achieving **a better tradeoff between preserving pretrained knowledge and enhancing downstream task performance**. Moreover, it consistently **achieves the best performance across various tasks and models**. Additionally, the logistic model **converges after a limited number of training iterations** (as shown in Figs. 3(b) and 11), allowing the omission of the logistic update step but only optimizing LLMs in subsequent training stages, further enhancing training efficiency. \n\nIn our framework, the LLM is optimized using the REINFORCE++ algorithm with a PiSSA-based low-rank adaptation strategy. Thus, we compare its time complexity with that of REINFORCE++ using PiSSA adaptation, which allows us to isolate the additional time introduced by our components. The results below show that **the ratio of increased time complexity relative to the baseline method decreases with larger models**. Moreover, it is worth noting that while our method incurs some additional computational overhead, it remains a highly efficient fine-tuning approach, particularly when compared to the full fine-tuning for LLMs.\n\n|Method|Time|Tunable # param.|\n|:-|:-:|:-:|\n|RoBERTa-base|||\n|REINFORCE++|591s|1.0M|\n|CDRO|1514s↑156%|0.7M↓30%|\n|LLaMA-2-7B|||\n|REINFORCE++|8750s|56.1M|\n|CDRO|11387s↑30%|23.3M↓58%|\n|LLaMA-3-8B|||\n|REINFORCE++|8924s|57.0M|\n|CDRO|11406s↑28%|23.3M↓59%|\n|Qwen2.5-32B|||\n|REINFORCE++|25700|266M|\n|CDRO|28787↑12%|93M↓65%|\n\n> **Q3: Potential negative effects or failure cases.**\n\nAlthough our method yields significant performance improvements across diverse scenarios, its effectiveness may degrade under inappropriate settings.\n- The quality and reliability of the generated counterfactual and paraphrased samples can affect the effectiveness of parameter localization and optimization. Using low-performing LLMs for data generation may produce low-quality augmentations that degrade localization accuracy and impair overall model performance.\n- Overly conservative thresholding in selecting causality-sensitive parameters may overly limit updates, hindering the model's adaptability to downstream tasks. To mitigate this, we adopt a dynamic thresholding strategy based on the average logistic probabilities across different matrices, which has demonstrated effectiveness across various datasets and models. \n\nThe above analysis will be incorporated into our manuscript to extend the discussion of failure cases. In addition, other limitations, including inapplicability to black-box models and increased complexity, are discussed in **Appendix A.13**.\n\n> **Q4: Stability of the identified causal components.**\n\nInsightful question. The causal components identified by our method demonstrate **stability across different runs but exhibit variability across different datasets**. Nevertheless, through extensive analysis, we have uncovered two fundamental, consistent patterns, detailed in the **“Analysis of Training Process” subsection in Section 4 (Figs. 3(a) and 13)**:\n- As the model converges, parameter matrices sensitive to causal relationships increasingly concentrate in the earlier and intermediate layers.\n- During early training stages, the query and key matrices are primarily updated, whereas the value, up, and down matrices receive more updates in later stages.\n\nMoreover, we observe that **the influence of various indicators on the logistic model’s decision-making is relatively consistent across datasets (Figs. 3(b) and 11)**. Specifically, the coefficients for the two gradient difference indicators are consistently positive, while those for the cosine similarities of hidden states are persistently negative. This indicates that larger gradient differences and lower cosine similarity are associated with a higher likelihood of a matrix being causal-sensitive.\n\n> **Q5: Threshold for selecting causal components.**\n\nIn our experiments, the threshold for selecting causal components is defined as the average logistic probability across all parameter matrices. This dynamic threshold enables updating matrices with probabilities exceeding the average. **Given that the localization network adapts dynamically during model training, we argue that this adaptive threshold, rather than a fixed, manually set one, can yield superior performance**. The table below compares several threshold strategies—Top-15 matrices, matrices with probabilities exceeding 0.5, and the average threshold—with the latter consistently outperforming fixed manual thresholds. Nevertheless, in practical applications, the threshold can be adjusted flexibly to meet specific requirements.\n\n|Dataset|SST-2||MNLI||QQP||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|OOD data|IMDB-Cont|IMDB-CAD|HANS|AdvNLI|PAWS|\n|Average|**89.62**|**92.65**|**77.68**|**39.40**|**46.01**|\n|Top-15|89.19|92.37|77.54|38.98|45.43|\n|0.5|89.23|92.48|77.35|38.92|45.66|\n\nAdditionally, **the consistent superior performance of our approach suggests that this threshold remains robust across different models and tasks**.\n\n> **Q6: Encoding causal relationships rather than statistical patterns.** \n\nGood question. We respectfully respond to your concerns from the following two points:\n- As stated in the response to Q1, the motivation for identifying causal-sensitive parameters stems from the idea that if a parameter is causal-sensitive, its behavior will differ between the original sample and its counterfactual. From the optimized coefficients shown in **Figs. 3(b) and 11**, the learned relationships between causal localization and the applied indicators align with our expectations. Specifically, the coefficients for both gradient difference indicators are positive, while those for the cosine similarity of hidden states are negative. These findings confirm that **the identified parameters behave distinctly between original and counterfactual samples, supporting their sensitivity to causal relationships according to the intuitive expectations from causal inference**.\n- Intuitively, relying solely on paraphrased and original samples may lead localized parameters to capture superficial statistical patterns rather than true causal relationships. Accordingly, we conduct ablation experiments considering either paraphrased or counterfactual samples exclusively. The results below indicate that **omitting counterfactual samples causes a significantly greater performance drop than omitting paraphrased samples, evidencing that localized parameters effectively capture causal relationships via counterfactual augmentations**.\n|Task|NQ||SciQ||TriviaQA||TruthfulQA||WikiQA||\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Metric|Acc@50(↑)|Cov@50(↑)|Acc@50(↑)|Cov@90(↑)|Acc@50(↑)|Cov@60(↑)|Acc@50 (↑)|Cov@40(↑)|Acc@50 (↑)|Cov@50 (↑)|\n|CDRO|**0.376**|**0.228**|**0.781**|**0.245**|**0.520**|**0.258**|**0.456**|**0.529**|**0.665**|**0.627**|\n|W/o paraphrase|0.375|0.224|0.777|0.243|0.519|0.255|0.450|0.528|0.662|0.624|\n|W/o counterfactual|0.359|0.220|0.769|0.233|0.505|0.247|0.436|0.514|0.657|0.621|\n\n[1] Locating and editing factual associations in gpt.\n\n[2] Mass-editing memory in a transformer."}]

Author rebuttal:

Meta-review: This paper proposes Causality-driven Robust Optimization (CdRO), a principled method that dynamically identifies and selectively updates causal-sensitive components in large language models, thereby reducing spurious correlations and hallucinations while preserving pretrained knowledge and achieving strong robustness across diverse tasks.

**Comments:**

- Reviewer z2Ph (Final Rating: 3 - Borderline reject): Finds the paper clearly written with comprehensive experiments and strong baselines, though remains cautious about data reliability, baseline coverage, and hallucination evaluation; **overall takes a neutral stance between borderline reject and accept.**

- Reviewer QZLY (Final Rating: 4 - Borderline accept): Praises the method’s effectiveness in improving causal reasoning and reducing hallucinations with robust validation, while noting added complexity and missing ablations on paraphrased samples.

- Reviewer PboH (Final Rating: 4 - Borderline accept): Acknowledges the importance of the problem and clarity of presentation, but points out overlap with existing “locate and optimize” strategies and the need for more challenging evaluation datasets.

- Reviewer 6STb (Final Rating: 4 - Borderline accept): Highlights the motivation, parameter efficiency, and clear presentation as strengths, while raising questions on scalability, stability, and potential failure cases.

**Meta Comments:**

Overall, the reviewers agree that this paper addresses an important and timely challenge, reducing spurious correlations and hallucinations in large language models, through a well-motivated and parameter-efficient framework. The proposed Causality-driven Robust Optimization (CdRO) method is clearly presented, technically solid, and validated with extensive experiments across multiple LLMs. While some concerns remain regarding data reliability, broader baseline comparisons, and ablations, the paper’s contributions are viewed as meaningful, its methodology sound, and its motivation compelling. 

Taken together, the consensus is positive, and the paper is considered suitable for acceptance.
Decision: Accept (poster)