Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Authors: Wei Liu
Zhiying Deng
Zhongyu Niu
Jun Wang
Haozhao Wang
YuanKai Zhang
Ruixuan Li
Venue: NeurIPS 2024 poster (2024)
Keywords: interpretability
data cleaning
causality
mutual information
Primary area: interpretability_and_explainability
TL;DR: A simple yet effective method to identify causal rationales.

Abstract: An important line of research in the field of explainability is to extract a small subset of crucial rationales from the full input. The most widely used criterion for rationale extraction is the maximum mutual information (MMI) criterion. However, in certain datasets, there are spurious features non-causally correlated with the label and also get high mutual information, complicating the loss landscape of MMI. Although some penalty-based methods have been developed to penalize the spurious features (e.g., invariance penalty, intervention penalty, etc) to help MMI work better, these are merely remedial measures. 
In the optimization objectives of these methods, spurious features are still distinguished from plain noise, which hinders the discovery of causal rationales. 
This paper aims to develop a new criterion that treats spurious features as plain noise, allowing the model to work on datasets rich in spurious features as if it were working on clean datasets, thereby making rationale extraction easier.
We theoretically observe that removing either plain noise or spurious features from the input does not alter the conditional distribution of the remaining components relative to the task label. However, significant changes in the conditional distribution occur only when causal features are eliminated.
Based on this discovery, the paper proposes a criterion for \textbf{M}aximizing the \textbf{R}emaining \textbf{D}iscrepancy (MRD). Experiments on six widely used datasets show that our MRD criterion improves rationale quality (measured by the overlap with human-annotated rationales) by up to $10.4\%$ as compared to several recent competitive MMI variants.  Code: \url{https://github.com/jugechengzi/Rationalization-MRD}.

Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Wei Liu'
Zhiying Deng' Zhongyu Niu' Jun Wang
Haozhao Wang YuanKai Zhang! Ruixuan Li
School of Computer Science and Technology.
Huazhong University of Science and Technology
PiWudao Tech
{idc_lw, dengzhiyingdd, zy_niu, hz_wang, quankai_zharg rxli)Chust. edu.
2 "jwang@iwudao. tech
Abstract
An important line of research in the field of explainability is to extract small subset of crucial rationales from the full input. The most widely used criterion for rationale extraction is the maximum mutual information (MMI) criterion. However, in certain datasets, there are spurious features non-causally correlated with the label and also get high mutual information, complicating the loss landscape of MMI. Although some penalty-basee methods have been developed to penalize the spurious features (e.g., invariance penalty, intervention penalty, etc) help MMI work better, these are merely remedial measures. In the optimization objectives of these methods, spurious features are still distinguished from plain noise, which hin- ders the discovery of causal rationales. This paper aims to develop new criterion that treats spurious features as plain noise, allowing the model work on datasets rich in spurious features as if it were working on clean datasets, thereby making ra- tionale extraction easier. We theoretically observe that removing either plain noise spurious features from the input does not alter the conditional distribution of the remaining components relative to the task label. However, significant changes the conditional distribution occur only when causal features are eliminated. Based on this discovery, the paper proposes a criterion for Maximizing the Remaining Discrepancy (MRD). Experiments on six widely used datasets show that our MRD criterion improves rationale quality (measured by the overlap with human-annotated rationales) by to 10.4 10.4% compared to several recent competitive MMI variants. Code: https: ///ggithu.... hotpa///gggcccaaaaaaaaaaaaaaaaaa
Introduction
With the success deep lcarning, there are growing concerns over interpretability (Lipton, 2018). Ideally, the explanation should be both faithful (reflecting the model's actual behavior) and plausible (aligning with human understanding) (Jacovi and Goldberg 2020 Chan et al. 2022). Post-hoc explanations, which are trained separately from the prediction process, may not faithfully represent an agent's is decision, despite appearing plausible (Lipton) 2018). In contrast to post-hoc methods, ante-hoc
"Corresponding authors.
This paper collaboration between Intelligent and Distributed Computing Laboratory, Huazhong University of Science and Technology and iWudao Tech The companion pieces to this research series can be found at https://jugechengzz.. wiw.hu..ttttb..... 1o/WeiLiu http//..................
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(or self-explaining) techniques typically offer increased transparency (Lipton 2018) and faithfulness (Yu et al. ) as the prediction is made based on the explanation itself. There is a stream of research that has exposed the unreliability of post-hoc explanations and called for self-explanatory
methods (Rudin 2019 Adebayo et al. 2018: Ghassemi et al. 2021 Ren et al. 2024).
One important line of research to build self-explainable NLP models is first extracting the most informative rationale in a text and then using the extracted rationale train a predictor. This line of research is known as rationalization. A model-agnostic rationalization framework, called Rationalizing Neural Predictions (RNP). was first proposed by Lei et al. (2016). RNP utilizes a cooperative game between an extractor and predictor. This game is designed with a focus on "data-centric" importance of rationales (i.e., aims to explain the connection between a text and the -adel-agnostic task label, rather than explaining the output of a specific model). First, the extractor identifies the most informative part of the input, known as the rationale. Then, as depicted in Figure [ the rationale is transmitted to the predictor to facilitate predictions. The extractor and predictor are trained cooperatively to maximize prediction accuracy, with the theoretical support being the Maximum Mutual Information (MMI) criterion (Yu et al., 2021; Chang et al. 2020). RNP and its
variants have become mainstream approaches for enhancing the interpretability of NLP models (Liu
et al.
2023 Liu et al. 2024] Zhao et al. 2024 Jiang 2022 2023c a. Storek et al. 2023; Zhang et al.
et al.
2024 Hu and Yu! 2024, Yue et al. 2024). Aside from interpretability. rationalization can also the extracted (Z, Y) samples can function
serve method for data cleaning,
new dataset. Recent studies have shown that a predictor trained with such a dataset can be more robust (Chen et al.)
2022) and generalizable (Wu et al. 2022 Gui ct al. 2023), due to the removal of task-irrelevant, harmful information.
Previous methods typically employ the Maximum Mu- tual Information (MMI) cri- terion to identify the ratio- nale, defined as the subset most indicative of the tar- get label. However, cer- tain datasets contain fea- tures that are statistically correlated with the task la- bel but do not causally af- fect it. These features are referred to as spurious fea- tures, and the associated correlations are known as spurious correlations. The spurious features are also indicative of the target label and can compete with the true rationale for extraction opportunities under the MMI criterion, distinguishing them from plain noise. Consider a scenario where the extractor is initially positioned on selecting plain noise. If a clean dataset contains no spurious features, the gradient will guide the extractor solely towards causal features. However, if the dataset is rich in spurious features, the extractor can move in various directions, arbitrarily towards either spurious or causal features. Given the potential diversity of spurious features in the data, the extractor may struggle in a complex loss landscape (Chang et al. 2020)) A typical example of spurious correlation, as highlighted in LIME (Ribeiro et al.) 201 6), is the frequent co-occurrence of wolves and snow in images. Consequently, the presence of snow in the background can erroneously serve as strong indicator for classifying an image as depicting a wolf, leading MMI to possibly select the background feature instead of the wolf's s face as the rationale. Figure ()) in Appendix 1..0uullsttrtees another instance of spurious correlations.
Y [0, 1]
great hotel with euudlent location! the breekfast butlet is terrifiee service wonderfull
Extractor
grest hotel with excadlent location! the Lie-Lreeddett bullet is snlassifie seneieerfull.
Predicctor
0.1
smino 0000 ,,,,H.(Y.)))
0.9
X
7
9
Figure I: The standard rationalization framework RNP. The task is binary sentiment classification about the hotel's location. X, Z, Ý, Y represent the input, the extracted rationale candidate, the prediction and the ground truth label, respectively. 0g. Or represent the parameters of the extractor and the predictor, respectively. He denotes cross-entropy.
Some methods try to develop regularizers that can penalize the spurious features and fix the shortcom- ing of MMI. INVRAT (Chang et al. (020)) incorporates the concept of invariant risk minimization to design an invariance penalty. Inter RAT (Yue et al.) (023) utilizes an intervention penalty. CR (Zang et al., (023)) implements a sufficiency and necessity penalty by separately assessing the sufficiency and necessity of each token. In addition to the specific shortcomings of each type of method (discussed in $2). they share a common limitation: most still adhere to the MMI criterion and merely supplementary objectives to penalize spurious features. If the penalty term's 's weight is too small, spurious features will still be favored over uninformative noise due to their higher mutual

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information. Consequently, when the extractor initially selects noise, the gradient descent algorithm might shift towards either spurious features or the true rationale. On the other hand, if the penalty term's weight is too high, it can dominate the loss function and impair the MMI's ability to distinguish between noise and causal features (see 1. The difference between spurious features and noise can complicate the loss landscape of rationale extraction, which may lead to the emergence of local optima. Note that the problem of local optima in rationalization is very serious (Yu et al. (002I) A recent research MCD (Liu et al. 2023b) revises the MMI criterion to the minimum conditional dependence criterion and does not introduce extra penalty terms. However, MCD also can not promise to treat the spurious features plain noise. We provide a detailed comparison of MCD and our approach in Appendix A. help readers better understand the unique advantages of our approach.
In this paper, we diverge from previous research that focuses on the selected rationale candidate Z as the primary subject. Instead, we adopt reversed perspective, considering the remaining part X-2 by excluding the rationale candidate Z from the full input X, as the main subject of study. We find that, although selecting spurious features rather than noise as will be more indicative of Y (i.e., PS) # P(Y[N), with S, NN denoting Spurious features and Noise), neither selecting the plain noise nor the spurious features as will cause a change in P(Y[X-z) (i.e., P(Y P(Y]X=N) maximizing the mutual information I(Y:Z) with maximizing the remaining discrepancy (MRD) DKL Py|x 2). Under this new criterion, spurious correlations are treated as equivalent to uninformative noise without extra supplement regularizers on the rationale candidate, allowing the extractor to work on datasets rich in spurious features if it were working on clean datasets.
X)) Based on this observation, we replace the criterion of
In summary, our contributions are as follows: (1) We introduce a new criterion that treats spurious features as equivalent to plain noise, simplifying the loss landscape for rationale extraction. (2) We propose a simple and practical method to implement this new criterion. (3) Experiments on six widely used datasets show that our MRD improves the rationale quality (measured by the overlap with dumann-nnootttd rationales) by up to 10.4% as compared to several competitive MMI variants.
2 Related work
Data-centric rationale extraction. Data-centric rationale extraction (also known as rationalization) is a general framework first proposed by Lei ct al. (2016). By extracting rationales before making predictions, this framework has been one of the mainstreams to facilitate the interpretability of NLP
models (Chang et al.) 2020 Sha et al., 2021: Yu et al.) 2021 Shen et al., 2022, Chan et al. 2022 Storek et al.] [2233] Zhang et al. 2023). And De Young et al. (2020) proposed benchmark that can be used for supervised rationale extraction. Recently, there has also been some work attempting to extend it to the field of graph learning (Luo et al., 2020) and computer vision Yuan al. 2022). Apart from improving interpretability, recent work has also discovered that it can serve as a method of data cleaning, as training predictor with the extracted rationales has been found to increase
robustness (Chen et al. 2022) and generalization (Wu et al. 2022] Gui et al. (2023). We also briefly discuss the potential impact of rationalization in the era of LLMs in Appendix|A.I.
Mitigating spurious correlations. One important obstacle of rationalization is the spurious features in datasets, as the spurious features also have high correlations with the task label and can compete with the causal features for extraction opportunities under the most widely used MMI criterion. Some methods have been developed mitigate the impact of spurious correlations. INVRAT (Chang et al. 2020) attempts to tackle feature correlation using invariant risk minimization (IRM) (Arjovsky et al. 2019). The main idea is to penalize spurious (non-causal) variations by splitting the dataset into distinet environments. However, IRM-based methods have several limitations. For instance, they require strong prior knowledge about the relationships between non-causal and causal features (c.g.. the extra labels of non-causal fcatures) in order to divide the dataset (Lin et al., 2022b). Moreover, IRM-based methods are limited to addressing only a finite set of predetermined non-causal features, neglecting the potential existence of numerous unknown non-causal features. In fact, a recent study (Lin et al.) 2022b) in the field of IRM has theoretically demonstrated that it is nearly impossible to partition a dataset into different environments to eliminate all non-causal features using IRM. Other challenges, such as the tendency to overfit, difficulty in applying to larger models (Zhou et al.. Lin et al. 2022a), and the marginal shift risk of the input (Rosenfeld et al., 2021 have also been identified within the realm of IRM. Inter RAT (Yue et al.] (023)) attempts to eliminate feature correlation through backdoor adjustment, intervening directly with the confounders. However, it is
2022
3

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extremely hard to measure the confounders since they are usually not observable in the dataset. CR (Zhang al. 2023) calculates the sufficiency and necessity of each token separately, which leads to a high computational complexity, making it feasible only for very short texts. Aside from the above shortcomings, penalty-basee methods share a common limitation. They need to coordinate the MMI and the penalty objectives to make the gradient descent algorithm treat the spurious features and plain noise equally and guide the extractor to move towards only the causal features. A recent research MCD (Liu et al. 2023b) revises MMI to the minimum conditional dependence criterion. Although MCD does not involve penalty regularizers, it also cannot treat spurious features and plain noise equally. And the spurious features still compete with the causal ones.
3
Preliminaries
3.1 The rationale extraction task
We consider the text classification task, where the input is a text sequence X=[#1, T2,-", -", with T. being the i-th token and being the number of tokens. Y represents the classes in a dataset D. The standard rationalization framework RNP (Lei et al., 2016) consists of an extractor f=(-) and a predictor fr(+), with 0g and Ap representing the parameters of the extractor and predictor. For (X,Y) ~ DD the extractor first outputs sequence of binary mask M fz (x(X)= = [m1, "M, mm.] e {0, 13' (in practice, the extractor first outputs a Bernoulli distribution for each token and the mask for each token is independently sampled using gumbel-softmax). Then, forms the rationale candidate Z by the element-wise product of X and M:
7 M O X [mi m,an]
To simplify the notation, we denote fE as in the following sections, i.e., fx(X) Z. With the extractor's 's selection, we get set of (7, samples, which are generally considered to represent the distribution P(Y[Z). The rationale Z is searched by maximizing the mutual information T(Y: Z):
= = arg max (( = arg max ( = mig min HY) s.t., Z= = fE{((). (2)
7
In practice the entropy ZY is commonly approximated by the minimum cross-entropy ming, HY, Z|Z), with Ý = fz(Z) representing the output of the predictor. It is essential to note that the minimum cross-entropy is equal to the entropy (please refer to Appendix A.7)). Replac- ing Z with fE(X), the extractor and the predictor are trained cooperatively:
min 0.00p
s.t., (X,Y) ~D.
To make the selected rationale human-intelligibbe, rationalization methods usually constrain the rationales by compact and coherent regularization terms. In this paper, we use the most widely used constraints proposed by Chang et al. (2020):
||M||
?(M)+ = A
+ A2
Σ E2
m,
m-rl-
1
The first term encourages that the percentage of the tokens being selected as rationales is close to a pre-defined level s. The second term encourages the rationales to be coherent.
3.2 Causality
We note that the contribution of this part does not belong to this paper. To help readers unfamiliar with causality better understand the spurious correlations, we borrow it from a previous paper MCD (Liu et al. 2023b) and make some minor revisions to make this paper self-contained. We provide a detailed comparison with MCD in Appendix A.1
We consider that consists of set of variables SN,, where C' denotes the real causal rationale for the corresponding task label Y. And S,S represent the plain Noise and Spurious features, respectively. The extractor selects one of (N,S, C} be the rationale candidate Z. Note that Z is not a separate variable, but proxy for any variable within X. Initially, the extractor may randomly select either N,S or C' to be Z.

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1(5
a63
0C3 -0
400
400
400
100)
4(9)
1(3)
20V
LCC)
0, 0y
9c
0y 0,
0c
8g (c) appiopriate penalty
8, 9
0, 0,
9.
(a) pederpenalty
(b) DD penelty
(d) over-penalty
Figure Penalizing spurious features for more efficiently searching causal rationales.
Consider classification dataset, we posit prob- abilistic graphical model to illustrate the corre- sponding data-generane generating process Figure Za). The annotators assign the task label Y by view- ing the causal features X (C Y). There are also some spurious features non-causally associated with Y through some unobservable confounders U ( -)
U
S
N
X
N
XA
Y
To facilitate understanding, let's take widely used dataset, Aeearappe,,, as an example for a detailed analysis in Figure bb). The task is binary sentiment classification for beer's 's ap- pearance. The input X comprises comments on two aspects (we omit other aspects for brevity): Xr for Taste and XA for Appearance, each of which can be considered as a subset variables of X. Additionally,
Figure 2: The aata-generating process of (a) a gen- eral classification dataset and (b) specific dataset Beer-Appear.
signifies something that does not discuss the sentiment tendency of X. The annotators assign the appearance label Y by viewing the comments on appearance (XA Y). Therefore, only XA serves as the direct cause for Y. However, XA is correlated with X due set of unobserved variables U (called confounders). For example, U may include variable indicating whether the beer originates from reputable brand, and a pleasant taste may imply that the beer comes from good brand (U + Xr). Moreover, beer from reputable brand is likely to have pleasant appearance (U' + XA) Consequently. Xy is associated with via a backdoor path, as depicted by the red dotted line in Figure Fi). In this situation, Xy is somewhat indicative of Y (please refer to Appendix [A.2] for quantitative example), but signifies a statistical correlation rather than causality. With the objective of MMI (Equation B .Yy can compete with X for the opportunity to be selected as the rationale candidate, complicating the rationale extractor's search landscape.
Treating spurious features as equivalent to plain noise
4.1 The shortcomings penalty-based MMI
Since spurious features also have high correlation with the task label, some methods tend to penalize spurious features with some supplementary regularizers (discussed in $2). Generally, their loss functions be written in form like
((Z) (MMI(Z) (penatty(())
(5)
where Z is the rationale candidate, which proxy of the variables within X (e.g. Cr N).
We now present some qualitative analysis to demonstrate why using penalties to amend the MMI criterion can only partially mitigate the issue of spurious correlations. Generally, for the MMI loss, we have (MM7(C) s LMMr(S) < (MMT(N) in real-world datasets (please refer to Appendix A.3 for detailed discussion). For the penalty loss, we usually have Epenalty (C) Cpenaity (S) and L penaltyy (N) < Epenalty (S). We denote d( ((-..) as the distance of the extractor's parameters moving from one state another. For example, d( d(N, C') denotes the distance between the extractor's 's two
states selecting N and C respectively. We denote (N C) C(N)-L(C) as the (qualitative) tendency of the extractor's moving from N towards C.
d(N.C)
5

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If = 0 (vanilla MMI), although we have g(N - C) 0, we also have g(N - S) 0. Thus the extractor may move towards either C' or S with gradient descent, not necessarily C (like the situation shown in Figure ())). This could lead to longer optimization paths, and the additional paths might introduce extra local optima. Note that Yu et al. (202T) have shown that local optima are serious in unsupervised (with no human-annotated rationales for supervision) rationalization.
MMI allows the extractor move towards either spurious features or causal features when starting from plain noise (Figure B)). Conversely, penalties enable the extractor to move towards either plain noise or causal features when starting from spurious features (Figure3(d) If these two objectives are well-coordinated such that ((5)= ((N) > (CC), the loss landscape will be much simpler and the extractor can ultimately move towards causal features (iggureBe)) However, such coordination is not easy to achieve. If À is too small, the situation will be under-penalty (Figure (la) and the spurious features can still compete with the causal features for extraction opportunities. If À is too high, the situation can become one of over- peealienaliotioon (Figure (i)), where the influence of MMI in distinguishing between noise and causal features may be decreased by the domination of penaatty (2) (Z). As result, noise can compete with causal features for the chance of being selected. In conclusion, a good objective should make that g(N - C²) 0, gS - C) > 0, C(5) L(N).
Since none of the existing MMI variants can treat spurious features as equivalent to plain noise. It then leads to a question: is MMI really necessary for rationale extraction? Can we no more use auxiliary regularizers to fix it, but just remove it completely and replace it with other criteria?
4.2 Spurious features are equivalent to plain noise in a counterfactual view
We aim to develop new criterion that can treat spurious features equal to plain noise, so that regardless of whether the extractor currently selects S or N, the gradient descent algorithm can guide the extractor to move only towards C. In this paper, we adopt perspective that reverses common methods. We no longer focus on the selected rationale candidate as previous methods do. Instead, we look into the properties of the remaining part after excluding the rationale candidate.
We denote the non-causal subset of X as A= (S, From the probabilistic graphical model shown in Figure a). we know that A and Y are d-separated by the causal features C' (Liu et al. 2023b) (please refer to Appendix A.6 for a detailed illustration). It means that all variables within A are independent with Y when conditioned on C.
With the d-separation property, we have P(YIC,S) = P(Y|C) P(YIC,N) N, This inspires us to view the problem from perspective opposite to previous studies; that is, no longer focus on the extracted rationale candidate as the subject of study, but rather on the remaining part of X after Z has been removed, denoted as X-z. Regardless of whether the extractor selects S or N to be the rationale candidate Z. we have
P(Y P(Y))), s.t., Z (N,S),
(
The high level intuition behind Equation(6jj that neither removing the plain noise nor the spurious features will cause a change in the task label. So, have that
0 = DIL(PPYXX (DA.P//XXDDDDDDxXXXXXXXX))>>PP1 <
(7)
If we define the loss function as
= - D
(8)
we will have that C(C) C(N) = L(S), which means that
CN C(N)-((C) > 0, g g(S -)= ((S) -())) 00
d(N,C)
d(S,C)
(9)
g(N - S) C(N)- - d(N,S)
=0, g(S + N) C(S)- =0,
d(S,N)
where g(N C.) mentioned in the above qualitative analysis following Equation 5 denoting the approximate tendency of the extractor to move from N to C. We call this objective as maximum remaining discrepancy (MRD) criterion. The unique advantage of MRD that it can treat spurious features as equivalent to plain noise. Thus, extracting rationales from datasets containing spurious

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update the predictor to approximate the distribution
Predictor
Pxxlx
min (H, 8x (V, P_z (K-2)
Bp
[ [hared
X
Extractor
X
Predictor
P--2/X-2
min D DDI_lPPxxxxIPPppp
Z
update the extractor for better rationale
The architecture of our proposed MRD. The approximators for the two distributions are shared to reduce the model complexity.
Figure 4:
features becomes equivalent to extracting from clean datasets without such features. As a result, the extractor only needs to distinguish between noise and causal features, significantly reducing the difficulty of rationale extraction.
The practical method
If we use Equation 8 to replace MMI, long as C' is not selected as the rationale candidate Z, the objective will not distinguish between N and S. That is to say, no matter the extractor currently selects N or S as the rationale candidate Z, the gradient descent algorithm can only guide it to move towards C. should be noted that the compactness of is facilitated through the sparsity constraint expressed in Equation
The followed problem how to apply MRD in practice. The real distributions of P(Y(X-z) and P(Y[X) are not directly accessible. So we need further efforts to approximate them. Similar to the vanilla RNP's approximating entropy with cross-entropy and inspired by the MCD's (Liu et al.) 2023b) success in approximating real distributions with predictor's output, we try to approximate real distributions by making use of the predictor. We first approximate P(YIX) with P(Ýx(X) by minimizing HY, Yx (X) (please refer to Appendix A.7 for detailed analysis on the feasibility of this approximation), and we also approximate P(Y with P(Y-z|X-2) by minimizing the cross-entropy Ho Y-z(X=z), where Ý-z. Yx are the predictor's outputs with the inputs being X- and X, respectively.
Finally, the training process for our MRD depicted in Figure 4 the extractor first selects a rationale candidate Z from the input X. Subsequently, X-2 and X are fed into the predictor to obtain two distributions, P(Ý-2|X and P(Yx|X). The overall objective of our model becomes (The pytorch implementation in Appendix (.8):
min| H. Yx H, 0p
+ (Qnnl-DDD(((PXXXXXX)IX021L.1LLL ((M))
(10)
6.
s.t., (X,Y) ~ P(xx(x) fr(X), X-2 fz (X), P( x(f=2|X-2)- fr(X-2))
where R(M) is mentioned in Equation The first term is used to help the predictor approximate the distributions, and the second term helps the extractor find a good rationale.
Experiments
6.1 Datasets and metrics
Datasets. To validate the method's ability to extract causal rationales in the input, there are certain requirements for the datasets. First, the datasets should contain spurious correlations, making causality a primary challenge within these datasets. Second, the test set should contain manually annotated causal rationales to facilitate quantitative comparisons between different methods.
We employ six datasets collected from two widely used benchmarks. BeerAdvocate (McAuley et al. 2012) is benchmark that contains three widely used text classification datasets: Beer-Appearnnce,
7

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Table 1: Results Beer-Appearance and Beer-Aroma. Values in "0" are the standard deviations.
Dutasets
Oper-Appearance
Beer-Aroma
Methods
P
RNP
11.3 (0.7)
79.2 (4.4)
(0.9)
60.0 (1.2)
8.6 (0.6)
61.0 (29.4) 33.9 (17.2)
43.5
INVRAT
10.0 (n/a)
42.6
(0,6)
10.0 (0.6)
41.2 (0.3)
39.1 (2.8)
(1.6
Inter RAT
11.7 (0.6)
66.0
(0.8)
(0.6
(0.6)
55.4 (0.9)
(0.6)
(0.8)
S 10%
NIR
(1.0
(6.5)
(0.5)
(1.3 (1.8
72.1 (2.3)
(4.4) (5.7
MCD
9.5 (0.4)
94.2
(1.6)
(1.9
9.9 (0.2)
84.6
(0.8)
(0.8)
MRD (ours)
10.0 (0.3)
93.6(1.3)
(1.3)
(1.7)
10.1 (0.4)
86.6 (4.2)
56.2
(1.9)
RNP
20.3 (0.2)
(L.5
(3.5 (2.2
(9.4
(2.0
(2.0
(2.7
INVRAT
20.0 (n/a)
58.9 (0.4) 67.2 (2.3)
(1.1)
20.0 (w/a)
29.3 (1.0)
52.1 (0.6)
37.5 (0.6)
S e 20%
Inter RAT
21.7 (0.3)
(0.5) 76.7 (1.7)
68.6 (0.4)
20.4
44.2 (0.1)
(0.2)
(0.1)
NIR
20.2 (0.7)
74.6 (4.4)
81.0
(9.0 (3.2)
64.1 (1.6)
(1.4) (1.0
MCD
(0.3)
79.3 (0.6)
(L.1)
82.3 (0.5)
(0.2)
65.8 (0.7)
(1.3
72.8 (0.9)
MRD (ours)
(0.5)
80.2 (2.3)
88.5 (1.0)
84.1 (1.5)
19.2 (0.4)
66.7 (1.3)
(1.8
(1.3)
RNP INVRAT
(L.0)
(2.0)
94.3
(1.9)
30.2 (0.8)
(4.1
(8.0)
(3,9
30.0 (n/a)
41.5 (0.4)
74.8 (0.3)
(0.3)
30.0 (n/a)
(1.6)
65.1
33.8
Inter RAT
(1.0)
48.1 (0.7)
82.7 (0.4)
29.4 (0.4)
37.9 (0.7)
(0.6 (2.1
S 30%
NIR
(9.6
(0.6
95.3 (0.4)
73.3 (0.5)
(0.6
43.3 (2.3)
(4.)
56.8 (3.0)
MCD
29.7 (0.4)
(0.5)
95.6 (0.8)
73.4 (0.4)
(0.4)
(0.2)
(1.3)
(0.4)
MRD (ours)
28.6 (0.3)
60.6 (0.7) 93.3 (0.4)
73.5 (0.5)
(.22)
46.8 (0.6)
(1.4
61,2
Beer-Aroma, Beer-Palate. In these datasets, each piece of text is comment consisting of the beer's three aspects: appearance, aroma, palate. And the comments of different aspects are highly correlated. For the Appearance dataset, the classification label is the quality (bad/good, [0,1]) of the beer's appearance. Other two datasets are similar. These three datasets are most important and used by nearly all of previous research in the field rationalization. HotelReview (Wang al. 2010) a benchmark that contains three widely used datasets: Hotel-Location, Hotel-Service, Hotel- Cleanninnes. In these datasets, each piece of text is review about hotel. For the Hotel-Location dataset, the classification label is the quality (bad/good, [0, of the hotel's location. For Hotel-Service and Hotel-Clean Sotel-Cleenniness. the classification label is about the service and cleanliness, respectively.
Metries. Considering that the annotators assign the label of the target aspect by observing the causal features, the overlap between the tokens selected by the model and those annotated by humans provides robust metric for rationale causality. The terms P, R, F1 denote precision, recall, and Fl score respectively. These metrics are the most frequently used in rationalization. The term S represents the average sparsity of the selected rationales, that is, the average percentage of selected tokens in relation to the full text.
6.2 Baselines and implementation details
We compare with various recent methods show the competitiveness of our method. These methods include IN INVRAT (Chang et al. 2020). Inter_RA RAT (Yue et al.) (023). CR (Zhang et al., 2023). MCD (Liu et al.) 20236). NIR (Storek et al. 2023). Both the extractor and the predictor are composed of an encoder (e.g., ENN/Transformer) and linear layer. We use two types of encoders: GRUs (following INVRAT, Inter RAT, and MCD. Table 2 and 3 and bert-base-uncased (following CR, Table 4). We adopt three levels of rationale sparsity: 10%, 20%, 30% (achieved by adjusting in Equation
We report the results of five random seeds. More details are in Appendix A.9
6.3 Results
The main resultz shown in Table 2 and 3 Across various datasets and levels of rationale sparsity, our proposed MRD achieves considerable improvements compared to existing baseline methods. Compared to the most competitive baseline MCD, our MRD improves the F1 score by up to 10.4% (=63.5% 53.1%. 1%.1 in Beer-Palate dataset with S x 10%). In addition, compared to the latest penalty-based method Inter RAT, we improve the Fl score by more than 10% in 14 out 18 settings,
FFor the three beer-related datasets, the results of INVRAT and Inter RAT are obtained from Table of the paper Inter RAT. Since INVRAT requires specific techniques to partition datasets into environments, and it no longer represents the latest literature, we have not replicated it on hotel-related datasets.
8

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Table 2: Results Beer-Palate and Hotel-Location datasets.
Dutasets
Beer-Pulate
Hotel-Location
Methods
5
R
S
R
RNP
10.1 (0.9)
(2.3)
(2.)
(1.2
(0.2)
(1.9
(1.6
(1.4
INVRAT
(n/a)
(1.5)
39.5 (0.6)
Inter RAT
(0.6
34.6 (0.8)
(8.4)
(0.5
(2.4) (1.5
(3.5)
(6.4
S w 10%
NIR
(3.3)
47.1 (177)) (20.0)
23.1
(18.6)
(.
(1.6
(1.9
(1.0
MCD
9.4 (0.8)
60.9 (2.1)
53.1 (1.9)
9.8
49.3 (2.1)
(3.0)
52.7 (2.4)
MRD (ours)
10.1 (0.3)
70.7 (2.0)
(2.6
63.5 (1.9)
9.7 (0.2)
(1.6)
58.2 (1.6)
54.4 (1.6)
RNP
19.7 (0.2)
(3.1
(2.1)
(0.4)
(1.0)
(2.5)
(7.0
INVRAT
20.0 (n/a)
24.0
(2.3)
(1.6)
S = 20%
Inter RAT
20.6
26.3 (0.6)
59.1 (0.8)
36.4 (0.7)
19.6 (1.4)
23.6 (0.7)
32.9 (2.6)
NIR
(1.0)
(9.0)
(14.8
(110(
20.0 (0.3)
(0.9)
(1.6
46.3 (1.2)
MCD
19.6 (0.5)
(1.4)
(288)
50.5 (1.8)
19.7 (0.4)
33.8 (1.3)
78.5
47.3
MRD (ours)
19.6 (0.7)
(1.9)
(1.0)
54.1 (1.7)
19.4 (0.1)
35.0 (0.4)
(1.0)
48.6 (0.6)
RNP
29.1 (0.9)
(5.2)
(10.9)
(7.0)
(1.7)
(8.7)
(3.77
(1377
INVRAT
20.0 (n/a)
20.9 (1.1)
(0.4)
(1.3
Inter, RAT
(0.4)
21.8 (0.1)
66.1 (0.8)
(0.1)
29.8 (1.2)
18.1 (0.5)
(1.6
(0.7)
S m 30%
NIR
(8.6)
24.5 (22.4)
(12.4)
(0.9)
12.3
(16.)) (37.6)
MCD
29.4 (1.7)
30.5 (1.0)
(5.6)
(1.8
30.2 (0.3)
22.3 (1.8)
79.4(7.1)
(4.8
MRD (ours)
28.2 (0.9)
30.9 (2.7)
(6.3)
43.0 (3.7)
29.4 (1.1)
25.4 (0.7)
(1.6
(0.8
Table 3: Results Hotel Hotel-Service and Claee-Cleenss datasets.
Dutasets
Hotel-Service
Hotel- Clenn-Clesssness
Methods
5
FI
R
FI
RNP
(0.1
46.1 (1.6)
(0.5)
(0.)
(0.)
33.8
(7.6
35.6 (0.4)
Inter RAT
(0.6
(0.9)
(1.4)
(0.4
(.6
34.5 (1.4
S 10%
NTR
10.7
(1.4)
(1.7
(1.4)
(0.3)
35.1 (0.7)
40.5 (0.9)
37.6 (0.6)
MCD
10,2 (0.4)
47.5
42.3
(1.3
9.8 (0.3)
34.3 (0.4)
37.8
35.9 (0.4)
MRD (ours)
10.5 (0.3)
48.5 (1.9)
(1.3)
(1.55)
9.9 (0.4)
34.6
(1.3)
36.6 (0.5)
RNP
(0.3
(1.3)
(2.4
(1.6
20.7 (0.5)
21.5 (0.9)
(0.3
30.1
Inter RAT
20.6 (0.3)
24.5 (0.4)
(1.1)
31.7
(1.1)
22.7 (0.7)
50.1
31.3 (0.5)
S 20%
NIR
(0.5)
33.4 (0.7)
(0.5)
(0.5)
20.6 (0.5)
21.7 (0.5)
50.5
30.3 (0.6)
MCD
(0.3)
32.5 (0.5)
57.2
(0.7)
20.1 (0.5)
22.2 (0.5)
(1.4)
30.8 (0.7)
MRD (ours)
(0.66
34.6 (1.4)
60.3
44.0 (1.4)
20.2
22.8 (0.6)
$2.0
31.7 (0.3)
RNP
30.6
14.6 (8.2)
21.1 (2155)
30.1 (0.5)
(1.0
(1.0
(2.4
Inter Inter_RAT RAT
(1.0)
19.6 (0.3)
(1.9)
28.7 (0.5)
29.6
17.1 (0.5)
57.5
26.4 (0.6)
30%
NIR
30.1 (0.6)
19.3 (10.8)
50,3 (28.1)
27.9
30.8 (0.8)
16.4 (0.4)
(2.8
25.4 (0.8)
30.1 (0.5)
(1.6)
(4.66
32.5 (2.4)
30.2 (0.4)
16.5 (0.3)
56.3 (1.7)
25.5 (0.6)
MRD (ours)
30.1 (0.3)
24.7 (0.7)
(2.0)
35,8 (1.0)
29.2 (0.6)
(0.1)
62.1
28,9 (0.3)
and by more than 20% in 2 out of 18 settings, verifying the limitation of penalty-based methods. We provide a visualized example of the extracted rationales by different methods Appendix A0
We also follow a recent method CR (Zhang et al., 2023) to conduct experiments with the BERT encoder as supplement, whose results are shown Table We follow CR the sparsity level as 10%, and the datasets are the most widely used Beer- Beee--ppeeatee Appearance and Beer-Aroma. Since some methods become highly sensitive hyperparameters after switching an over-parameterized BERT model (also supported by Remark 6.1 in (Zhang et al.) 2023)), and our computational resources are insufficient for extensive hyperparameter tuning for these methods, we primarily compare our approach with methods that have already been implemented using BERT. Our MRD still outperforms all the baselines. Specifically, we improve the Fl score by 15.6% on Beer- Aper-appeer dataset, and 6.0% on the Beer-Aroma dataset.
7 Conclusion, limitations, and future work
This paper investigates the susceptibility of the widely adopted MMI criterion in XAI to spurious comelations. We design new criterion that can treat spurious features as plain noise, making rationale extraction from datasets rich spurious features as straightforward as extracting from clean datasets, thus simplifying rationale extraction. Given the versatility of the self-explaining rationalization

---

Table 4: Results with BERT. We follow CR S 10%.
results obtained from Table 11 of CR.
Datasets
Beer-Appeene Apeearppeeeceee
Beer-Aroma
Methods
R
F1
R
RNP
10.0 (m/a)
(1.4)
(1.9)
(1.7
10.0 (W/4)
49.1 (3.2)
28.7
(2.5)
VIB
10.0 (n/a)
52.6
(2.3)
32.9 (2.1)
10.0 (m/a)
37.7 (2.8) (1 (2.9)
A2R"
10.0 (n/a)
55.0
34.3 (1.6
10.0 (n/a)
(2.8)
(3.1
(1.3
S 10%
INVRAT
10.0 (n/a)
56.4 (2.5)
21.3
36.7 (2.1)
10.0 (W/a
(3.6
(1.(
33.2
CR
10.0 (n/a)
(1.9)
(1.6
(1.0
10.0 (m/a)
(2.9)
(2.0
MRD (ours)
10.6 (1.1)
(15.2)
(6.0
(8.8)
(.9)
71.7 (4.6) (4.3
55.1 (4.5)
framework, exploring how our method can be applied to broader fields such as computer vision and graph learning is a worthwhile future direction.
One limitation is that, although some researchers have found that rationalization can benefit large language models (L..Ms) by providing high quality data (please refer Appendix [A.IT, this paper does not involve LLMs.. Given the recent remarkable success of LLMs, exploring how our MRD can aid in training trustworthy LLMs is another avenue worth pursuing.
Acknowledgements
This work is supported by the National Natural Science Foundation of China under grants 62376108 62302184, 62436003 and 62206102; the Science and Technology Support Program of Hubei Province under grant 2022BAA046: Hubei Science and Technology Talent Service Project under grant 2024DJC078; and Ant Group through CCF-Ant Research Fund.
We thank Lang Gao and Yang Qiu for their help during the rebuttal period. They helped lot with the experiments on L.LMs and graph data.
We are also grateful for the valuable suggestions provided by the anonymous reviewers, which greatly helped to improve the quality of this paper.
10

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A Appendix
A.1 The comparison between MCD and MRD
This paper is inspired by a previous paper MCD ( Liu et al. 2023b). This part aims to clarify the distinct contributions of our MRD.
We first note that the preliminaries of causality ($3.2) is provided by the paper of MCD. And the contribution of the causality analysis does not belong to us.
Apart from this, the practical network architecture (Figure may also look like that of MCD. However, the core of this paper focuses studying different optimization objectives. In fact, in the field of rationalization, the network structures of many different methods are similar; the key difference lies in the optimization objectives. This phenomenon is akin to research in the GAN (Generative Adversarial Nets) field, where diverse approaches often share similar architectures but differ primarily in their optimization strategies.
Our primary contribution is that the proposed MRD criterion allows the objective treat the spurious features equally as noise. To the best of our knowledge, this is the first research that can treat spurious features as noise. And we do need coordinate the penalty term.
In MCD, the objective for rationale selection is
min Dw(((((YX)))IPTTTT
(11)
While in our MRD, it is
min Diin-Dxz(P(y1i) (XX-DDx(((0XX22))XXXXXX2)) 6.
(12)
where X-2 is the remaining part after removing the selected rationale candidate Z from the full input X.
The research motivations behind MCD and MRD quite distinet, approaching the problem from opposite perspectives. MCD focuses on the properties that the selected rationale candidate Z should satisfy. On the other hand, MRD examines the properties that X should exhibit after discarding Z, emphasizing what remains in the input after the rationale is removed. This contrast highlights a fundamental shift in how the problem of extracting meaningful information is addressed.
Aside from the motivations, the novelty of the practical method in this paper is also considerable. Most existing research primarily focuses the selected rationale the main subject of study, whereas this paper shifts attention to the unselected remaining parts. While some methods in the field of explainable AI have also considered the unselected portions, their primary purpose has been to achieve comprchensiveness, treating the unselected parts as supplements to the main content and still requiring the balancing of multiple objectives Yu al., 2019). Moreover, these methods consider the unselected parts not for achieving causality but for other aspects of interpretability. This paper is novel in suggesting that focusing solely (i.e., completely through out the selected rationale candidate) on the unselected remaining parts can effectively achieve causality, marking a distinctive approach in the study of explainable Al.
A.2 A toy example of the backdoor path
This example is provided by (Liu et al. 2023b). To make the readers that are not familiar with causality better understand the spurious correlations, we borrow it to provide more intuitive understanding of the correlation in Figure We assume U, XA, XT, and Y are all Bernoulli variables, with their respective probability distributions as:
rUU 1) = p(U = 0) 0.5,
=)) =1)=1)====+111001)0 p(X7 = OJU 0) = 0.9, MXA 1 11) =1)==(X,, OU 0) 0.9, D( = =|)== 11>p(==000 = = =0) 0)= 0.9.
p(Xr
(13)
With some simple derivations, we can casily obtain (detailed derivation is in Appendix A.4
p(X== = 1) p(Xr p(Y = 1) 0.5.
(14)
15

---

Then, we can further get (see Appendix A.5 for the detailed derivation of Equation I6d and 17)
pUU 1|xx =) MU = 1,xr=1) p(X, - 1) = 0.9.
(15)
p(x==1)
(xx==1)
p(XA= = =|x Σ - 0.9
0.1 0.9
0.82.
(16)
(e(0,11
p(Y [[==|X==1)=
Σ
pV = (XX)) XT 0.82 + 0.9 0.18 0.1 0.756.
(17)
Xxe(u,1)
A.3
The association between different variables and Y
Though it is not the core claim of this paper, will have a brief discussion about why LarMr(C) MM (S) < (N).
The MMI loss is used measure the indicative degree of Z towards the task label Y. First, we think the noise N is independent of Y, thus has the lowest mutual information with and the highest MMI loss.
And for CMSM (C) S (SM, the reason is that C' always co-occur with the target label in all data samples. While in some data samples, there is not S but only C. So, C usually has higher correlation with Y. This can also understood from the probabilistic graphical model in Figure C' is the direct cause of Y. The association between S and Y needs to flow through a path that passes through C.
A.4 Derivation of Equation 14
We use XA as an example, and the others are nothing different.
P(Xx=1)= Σ 1(U)=
Σ
p(XA 11U) 0.9 0.5 0.1 0.5 0.5. (18)
Ue(U.1)
Ve(0.11
A.5 Derivation of Equation 16 and 17
In Figure b), we have Xr a X and XT 11 X|X That is to say,
P(XAUU PU) P(Y Xr) P(Y(XA).
(19)
Then we can easily get Equation 16
p(Xx = 1|x, 1)= Σ 1 1,U 1)
Uc(0.1)
Σ p(xA= X x)
(20)
(E(0.1)
Σ p(XA= 11)=x((-== (e(0.1)
And Equation 17 is similar.
A.6 D-separation
separatiion is an important concept in probabilistic graphical models.
D-Separation (Bishop) 2006): A, B, and C' denote arbitrary, non-intersecting sets of nodes (and their union might cover all nodes of the graph) in given probabilistic graph. Our objective is to determine whether a specific conditional independence statement A 1l BJC is implied by this graph. To do so, we examine all possible paths from any node in to any node in B. path is said to be blocked if it includes a node such that either
(a) The arrows on the path meet at node o, forming either a chain (i.e., - o -)) or fork (i.c., + -), with the node o being part of set C, or
16

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(b) The arrows on the path meet at node to form a collider (i.e., 0 +)) and neither the node o itself nor any of its descendants are included in set C.
If all paths are blocked, then A is considered be d-separated from B by C, meaning that B|C.
Liu et al. (023b) have theoretically shown that Y and the non-causal features are d-separated by the causal features.
A.7 Minimizing the cross-entropy is equal to minimizing the KL-divergence
The cross-entropy consists of two parts:
HaYY, Xx|x)x H(YI))+ DEL (PC((PY)))))))X
(21)
X) determined by the dataset itself and is irrelevant to the predictor. So, when we train predictor to minimize Hy(Y, Yx(X), we are in fact minimizing Dx (PX((()))))P(((x())))) We know that if and only if P(Y = P(Ex we get the lowest KL-divergence (equal to 0).
So, we can finally use P((x|X) to approximate P(YJX) by training predictor and minimizing Xx
A.8
The implementation with Pytorch
For batch of (X,Y). we first send X the extractor to get and X X-z:
Z = = X-z = X - Z.
(22)
Then we get a copy of X-z with the pytorch function "torch.detach()
X=2 z torch.detach( X-z).
(23)
Then, get Yx and yz2:
fx = fx(X,
(24)
V fp(X-2):
Then update the predictor with
min[torch.nn hatn:/oocc...functiioniiii www.ffunttionllcrssseeetttooooii
+ htt.:/ffuncttoaal.cosseeeettttee Y)],
6,
(25)
which is the first part of Equation 10
At the same time, we update the extractor with Equation
Now, we deal with the second part Equation
10 We first freeze the predictor's parameters and get
X-z again:
Z = fe(),, X- = Z.
(26)
We now do not copy X-z. z. Instead, we directly get Vx and Й-z:
FxX,, Ý. fx(x2))
(27)
Then we update the extractor with
min F.kk -...-Flkdivvvif FiFF__ii((Fssfmmax((22 Fisttma (onettmaxxVxx))
(28)
8
where "F" denotes "'n.functional" In practice, we have added Equation 4 to 28
Now, update round for Equation 10
completed, and we repeat the above steps again.
17

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Table 5: Statistics of datasets used in this paper.
Train
Dev
Annotation
Datasets
Pos
Neg
Pos
Neg
Pos
Neg
Sparsity
Appearance
202385
12897
28488
1318
923
13
18.5
Beer
Aroma
172299
30564
24494
3396
848
29
15.6
Palate
176038
27639
24837
3203
785
20
12.4
Location
7236
7236
906
906
104
96
8.5
Hotel
Service
50742
50742
6344
6344
101
99
11.5
Cleanliness
75049
75049
9382
9382
99
101
8.9
A.9
More details
To the best of our knowledge, all datasets are sufficiently anonymized to make identification of individuals impossible without significant effort. For beer-related datasets, users need consult the original authors (McAuley et al. 2012) for permission first
There another widely used version of Beer where the data containing spurious correlations has been manually removed by Lei et al. (2016). The cleaned version is used to study other problems rather than causality. Since Inter RAT and MCD.
studying spurious correlations,
the original version used by
All datasets are in English. We process the datasets in the same way MCD (Liu et al. 2023b).
The maximum text length is set to 256. More statistics of the datasets are Table 5 The datasets of BeerAdvocate is unbalanced. For the training data, we sample from the positive data to get same number positive and negative texts.
In practice, the approximators for the two distributions are shared to reduce model complexity. But this trick is not necessary, if two separate nets are used to approximate the two distributions, the performance can sometimes be even better.
Some previous methods needs very careful hyper-parameter tuning. To make fair comparisons, most results of the baselines are copied from previous papers.
We follow MCD to learning rate of 0.0001 and batchsize of 128 for the beer-related datasets. For the hotel-related datasets, we also follow MCD to use a learning rate of 0.0001 and a batchsize of 256.
We report the average results of five different random seeds. The experiments are run on a RTX4090 GPU, with 24GB memory.
A.10 Examples of the extracted rationales
We provide a visualized example of the rationales extracted by different methods in Figure The dataset is Beer-Appaarncce, and the rationale sparsity is set to about 10%. The causal rationale should be the comments describing the beer's appearance (the underlined texts). The vanilla RNP extracts the taste the rationale. Inter RAT selects both aroma ("aroma is fruity") ) and taste ("smooth and very effervescent"), That to say, both RNP and Inter_RAV select the spurious features as the rationale. MCD selects both causal features ("yellow color notes") and spurious features ("aroma is fruity...") "). While our MRD selects only the causal rationales.
A.11 The potential impact of rationalization in the era of LLMs
In comparison to traditional "model-centric" XAI methods which solely focus on the model's learned information, "data-centric" approaches primarily aim to extract model-agnostic patterns inherent in the data. So, apart from improving interpretability, rationalization can serve as a method of data cleaning (Seiler 2023).
Domain-specific large models often require supervised fine-tuning using domain-specific data. Un- cleaned data may contain harmful information such biases and stereotypes (Sun et al. 2024).
18

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Label (Beer-A | Positive.
Label (Beer-A ppearance);; Positive. Prediction: Positive,
Prediction: Posttive,
this one was sent gracioualy to me by [unknown)] an buddy yellow color with some orange notes head is white and disappear quickly. bazed and very opergae aroma fruity cloves banenes typical betewekzen taste shows notes of orange and the typical befeweizen
Impul:
Impul: this one was sent gracioualy to me by (unknowal an trader. cheers huddy yellow beer with some orange notes. head is white and di diapeerr quickly hazed and very opeque aroma friity cloves hefeweizen taste shows notes of orange and the typical hefeweizen tasle cloves beranas ). smooth very efferveseent, almost no bittrmsss too very drinkable and refreshing. a nice befeweizen
awesome trader,
typioal
benenes) smooth and very effervescent, almost no bitterness too... very drinkable and refreshing. befeweizes
(a) RNP
(b) Inter RAT
Label gpeerance);:
Label (Beer-) ppearance);: Positive.
Predlccioon Positive.
Prediction: Posttive.
one graciously by [unknown]] awesome trader, cheers buddy yellow color beer with some orange
Imput:
Impul: this was sent graciously me by [unknowa] an awesome trader. cheers buddy yellow color beer with some orange notes,
white disaggear quickly bazed and very ocerrae aroma fruity cloves and bananes] typical of befeweizen taste shows notes orange and typical hefeweizen tadle ( cloves and bemenies) ). smooth and very effervescent, almost no bitterness too. very drinkable refreshing. nioe befeweizen
disappear quickly. hazed and very opente aroma is friity cloves and benenas] ). typical of hefewweizen taste shows notes orange and the typical hefeweizen taste cloves and benanas ). smooth and very affervescent. almost bittemess too very drinkable and refreshing a nice heleweizen
(c) MCD
(d) MRD (ours)
Figure 5: A visualized example of the rationales extracted by different methods.
Recent research suggests that training predictors with extracted rationales can remove irrelevant harmful information, enhancing robustness (Chen et al. 2022) and generalization (Wu al. 2022 Gui et al. 2023).
Since LLMs are usually pretrained on various datasets, they tend to be less controllable than small models (Zhao et al. 2023). Considering that for simple tasks (such as text classification), small models are also capable and can achieve satisfactory results, we can train separate rationalization model for a single domain sppccifc dataset. Small models trained on a single dataset are often more controllable and save computational resources (such as searching for hyperparameters and adding regularization terms) (Guo et al., 2023). Then using the extracted rationales for supervised fine- tuning might prevent large models from learning harmful information from new data. Additionally, shortening input texts can also reduce the memory required for fine-tuning.
A recent study has also found that training small model for data selection (although not the same as rationale selection) and producing a small subset is useful for fine-tuning LLMs (Xia et al. 2024).
19

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NeurIPS Paper Checklist
Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: [NA]
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Section ?
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification: They included in the Appendix.
4. Experimental Result Reproducibility
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Answer: [Yss]
We provide the experimental details in Appendix A.9 We provide the Pytorch implementation Appendix A.88 We provide the code at https: http:///nnnnmmusu.//a/ 4open
Justification:
sciencce/r/MRD-O00 0427
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Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
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M42- We provide detailed README.md to help the users code.
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Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
Justification: We provide them at Appendix are borrowed from our baseline MCD.
A.9 Most of the Experimental Setting/Details
7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: We report the results of five random seeds.
8. Experiments Compute Resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time exccution) needed to reproduce the experiments?
Answer: [Yes]
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Justification: They are included in Appendix A.9
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Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics http:///ewwae.cccpppo/oc/ccccaaa
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21


Reviews:
[{"review_id": "HAYwOuGumv", "reviewer": "Reviewer_UFAP", "summary": "The paper proposes a novel conseptualization within the Rationalizing Neural Predictions (RNP) framework of explainable AI, namely the \"Maximizing the Remaining Discrepancy\" (MRD) training criterion for a system consisting of an Extractor and a Predictor network. Contrary to existing methods, the MRD criterion treats spurious correlations within the data similarly to noise, and thus aims to find only the causally correlated features within the data. In practice this is performed in a straightforward way by maximizing the contrast between the predictive capabilities of the raw input and the (generated-explanation-complement) residual of the input. \n\nThe method is tested against multiple competing methods in two previously published general datasets that both have three tasks of binary outcomes (a total of six). The results are reported against human-annotated subsets of each dataset in the task of correctly identifying the correct explanation word-tokens. The results show clear improvement against the other methods in all tasks.", "questions": "1. Abstract: Indicate which metric the 10.4% performance gains is\n2.  line 225: \"each\" -> \"easy\"", "limitations": "I would have liked to have seen some more thought put into the limitations of the presented approach. A clear limitation that I see is that the method is not guaranteed to differentiate spurious correlations from causal correlations: If the given example of wolves being commonly depicted in snow is in actuality the only (or clearly over-represented) occurrence case for both categories (\"snow\" and \"wolf\") within a dataset, the correlation will be seen as a causal one within the model training.", "rating": 7, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "The paper is clearly written, and as a researcher outside of the \"explainable AI\" field, I was well able to comprehend the rationale and the methodology. The core idea is solid and well justified. Taken that the authors have used existing state-of-the-art methods as comparisons in within the experiments (which I am not an expert to judge), the performance gains are systematically positive. Particular detail has been placed in the mathematical formalism throughout the text.", "weaknesses": "I see some weaknesses within the experiments. First, the datasets seem to be quite similar in terms of the task (i.e., both have short descriptions with spurious correlations that map to binary outcomes within a given semantic task). I would have liked to see a more difficult/\"real world\" experiment alongside the present experiments. Even though the authors' stated justification is that the present ones are the most likely to show a contrast between the methods, I would view the performance of models in these datasets as \"necessary, but not sufficient\" milestones of performance.\n\nFor more minor issues in the experiments, I would have liked to see the actual binary task performances in the appendix (also for a baseline system that is trained without any RNP goals). Fruthermore, as the authors report the mean and standard deviations of the performance metrics in the tables, they could also perform perform statistical testing (e.g., a t-test) to check for statistical significance between the two top-performing metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for taking the time to carefully review our work and provide constructive feedback.\n\n**Weakness1**. The datasets seem to be quite similar in terms of the task.\n\n**A**. Thank you for your valuable suggestion. We now add a graph classification task. Different from the previous text classification datasets, the graph classification dataset is a more challenging 3-class classification dataset.\n\nOur research topic imposes special requirements on the dataset. Firstly, the primary challenge in the dataset needs to be spurious correlations. Classification tasks are relatively easier to construct datasets with spurious correlations. As far as we know, the vast majority of studies on spurious correlations use classification tasks for experiments. Additionally, to compare the quality of rationales extracted by different methods, we need manually annotated ground-truth rationales as a test set, which further constrains the dataset. Here, we use a graph classification task to verify the generalizability of our method in other fields, as this domain has a dataset, GOODMotif (GOOD means graph out of distribution, it is from a public graph OOD [benchmark](https://openreview.net/forum?id=8hHg-zs_p-h)), which contains both ground-truth rationales and spurious correlations. \n\nWe compare our MRD to the standard RNP and the strongest baseline MCD (other baselines such as Inter_RAT, NIR, and CR are designed for text data only and cannot be applied to graph data). The encoder is a three-layer GIN. We select a set of nodes from a graph to form the rationale. The sparsity is set to about $30$\\% (close to the sparsity of ground-truth rationales). The results are as follows:\n\n|  |Acc| P | R | F1 |\n|---|---|---|---|---|\n| RNP |64.3| 43.5 | 45.9 | 44.6 |\n| MCD | 67.2|45.3 | 46.8 | 46.0 |\n| MRD (ours) |**71.3**| **48.7** | **51.9** | **50.2** |\n\n**Weakness2**.  I would have liked to see the actual binary task performances.\n\n**A**. Some experimental results are copied from baseline papers. We followed the Inter_RAT settings and initially did not report classification accuracy. Now we report the classification accuracy of the standard RNP, the strongest baseline MCD, and our MRD. Additionally, we trained a regular classifier not used for interpretability, which we refer to as Classifier. (Due to time constraints, we are report results from a single random seed on the three beer-related datasets.) And we will consider a t-test in future work.\n\nThe results with $S\\approx$10\\% are as follows: \n\n| Datasets | Appearance | Aroma | Palate |\n|---|---|---|---|\n| RNP | 80.0 | 83.4 | 84.0 |\n| MCD | 81.1 | 85.5 | 87.2 |\n| MRD (ours) | 81.8 | 87.0 | 87.8 |\n| Classifier | 90.7 | 90.4 | 89.9 |\n\nThe results with $S\\approx$20\\% are as follows: \n\n| Datasets | Appearance | Aroma | Palate |\n|---|---|---|---|\n| RNP | 84.5 | 82.7 | 83.4 |\n| MCD | 87.5 | 88.8 | 89.7 |\n| MRD (ours) | 88.3 | 89.7 | 90.9 |\n| Classifier | 90.7 | 90.4 | 89.9 |\n\nThe results with $S\\approx$30\\% are as follows: \n\n| Datasets | Appearance | Aroma | Palate |\n|---|---|---|---|\n| RNP | 85.7 | 84.8 | 85.8 |\n| MCD | 88.2 | 89.1 | 87.2 |\n| MRD (ours) | 89.7 | 89.3 | 88.5 |\n| Classifier | 90.7 | 90.4 | 89.9 |\n\n\n**Question1**. Indicate which metric the 10.4% performance gains is.\n\n**A**. It represent the rationale quality, which is measured by F1 score (the overlap between human-annotated rationales and model-selected tokens). On the Beer-Palated dataset with $S\\approx 10$\\%, the F1 score of the second best baseline MCR is $53.1$\\%, while our MRD gets $63.5$\\%. The improvement is $63.5$\\%-$53.1$\\%=$10.4$\\% (L316). We will include this discussion in our revision.\n\n**Limitations**. If the given example of wolves being commonly depicted in snow is in actuality the only (or clearly over-represented) occurrence case for both categories (\"snow\" and \"wolf\") within a dataset, the correlation will be seen as a causal one within the model training.\n\n**A**. \nWe greatly appreciate your reminder and will include the following discussion in our revision.\n\nWe agree that our method cannot handle this situation, but it is a too extreme case. If a wolf always appears with snow in an image, and snow never appears without a wolf, then snow and wolf will have identical statistical properties in this dataset. In fact, we believe that even humans would not be able to handle this situation. Suppose a human is asked to classify these images without being told the basis for classification. In that case, they wouldn't know whether to classify based on the wolf or the snow, as either could be used to complete the classification task.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FtYQDJ1xa5", "reviewer": "Reviewer_voxd", "summary": "This paper focuses on rationalization, especially on finding the most reasonable subset of a text sequence that can predict the assigned labels. \n\nIt proposes a novel criterion, MRD (maximizing the remaining discrepancy), which minimizes the negative KL divergence of the distribution of labels given the input with and without the rational part. \n\nMRD is tested on six different datasets and achieved promising results compared with other baselines leveraging MMI (maximum mutual information) and its variants.", "questions": "1. Tables 1 and 4 show that using the BERT encoder is no better than using the GRU encoder, which is somewhat counterintuitive since BERT is generally considered stronger. It would be beneficial to include more analysis for this part.\n2. There seems to be no explanation provided for the bolded and underlined numbers in all tables (I assume they represent the best and second-best results).", "limitations": "1. Please see Weakness (1).", "rating": 7, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "1. The paper is well-written and self-contained, with comprehensive explanations of the main ideas and details provided in both the main text and the appendix. It makes this paper easy to read and understand.\n2. The proposed criteria are well-founded from both high-level conceptual and mathematical perspectives, adding to the paper's overall credibility. Furthermore, the idea itself is both interesting and intriguing.\n3. The experimental results showed significant improvements over baselines, showcasing the effectiveness of the proposed approach.\n4. Code is open-sourced, promoting transparency and enabling further research and replication of results.", "weaknesses": "1. The scope of the scenario in this paper (as shown in the experiments) is a bit narrow. It would be beneficial to demonstrate the generalization of the criterion through more experiments on different types of data, such as images/video or speech. Since the criterion is not specific to text, the absence of these experiments weakens the potential applicability of this idea to general machine learning problems.\n2. Although direct comparison may not be entirely appropriate, it would be beneficial to include a LLM as one of the baselines to understand the performance gap between the proposed method and LLMs. If the proposed criterion can outperform current LLMs, it would be a good achievement. Otherwise, additional experiments involving other modalities would better demonstrate its value. (I tried the example in Figure 5 with GPT-4o, using the proper prompt, and the output was the same as the one generated by MRD.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for dedicating your time and expertise to review our paper. Your insightful comments and suggestions are highly valued and appreciated.\n\n1. **Weakness1 (It would be beneficial to demonstrate the generalization of the criterion through more experiments on different types of data)**. \n\n**A**. Thank you for your insightful suggestion. We now add a graph classification task.\n\nAlthough the framework of RNP can be applied to other fields, most of the improvement methods are designed for text data. When applying it to other domains, it is not easy to find enough proper baselines. Additionally, to evaluate the quality of rationales extracted by different methods, we need manually annotated rationales as a test set, which are rarely available in the image and speech domains. Here, we use the graph classification task to verify the generalizability of our method in other fields, as this domain has a dataset, GOODMotif (GOOD means graph out of distribution, it is from a public graph OOD [benchmark](https://openreview.net/forum?id=8hHg-zs_p-h)), which contains both ground-truth rationales and spurious correlations.\n\nWe compare our MRD to the standard RNP and the strongest baseline MCD (other baselines such as Inter_RAT, NIR, and CR are designed for text data only and cannot be applied to graph data). The encoder is a three-layer GIN. We select a set of nodes from a graph to form the rationale. The sparsity is set to about $30\\%$ (close to the sparsity of ground-truth rationales). The results are as follows:\n\n|  |Acc| P | R | F1 |\n|---|---|---|---|---|\n| RNP |64.3| 43.5 | 45.9 | 44.6 |\n| MCD | 67.2|45.3 | 46.8 | 46.0 |\n| MRD (ours) |**71.3**| **48.7** | **51.9** | **50.2** |\n\n2. **Weakness2 (it would be beneficial to include a LLM as one of the baselines)**\n\n**A**. Thank you for your valuable suggestion. \nWe now add the experiments conducted with the llama-3.1-8b-instruct model. We perform both 2-shot prompting and supervised finetuning. The results are in the General Response (at the top of this page). We find that our method can sometimes outperform the finetuned llama-3.1-8b-instruct. \n\nWe do not compare with GPT-4 because GPT-4's training involved extensive human alignment, which is very expensive. GPT-4 is overly powerful and not representative, as many studies on LLMs even use GPT-4 as a judge to evaluate different methods. Additionally, GPT-4 cannot be privately deployed. \n\n3. **Question1 (using the BERT encoder is no better than using the GRU encoder)**\n\n**A**. This phenomenon is indeed counterintuitive but has been validated by a set of previous papers. One of our baselines, MCD, also summarizes this phenomenon. There are roughly two possible reasons for this: first, when implementing RNP using BERT, it is highly sensitive to hyperparameters; second, it is prone to overfitting.\n\n4. **Question2 (bolded and underlined numbers)**\n\n**A**. Yes, they represent the best and second-best results. We will add this explanation to our revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9kcpRSEsVO", "reviewer": "Reviewer_98v5", "summary": "Aurthors propose a way to obtain NLP explanations via novel criteria. Idea is instead of adding a regularization term to MMI loss they use tokens not selected.", "questions": "- I wonder why some of the results in Tables 1 & 2 are not consistent. Such as Table 2, NIR F1 goes up when spurious rate is increased and then goes back down. Can this be explained somehow? Some random chance?", "limitations": "- Does not directly work with LLMs.", "rating": 7, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Very neat idea that has been well explained. Experimental results also support the hypothesis.", "weaknesses": "- Now results Tables only F1 numbers are bolded. Please bold best and underline 2nd best for the other columns also.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your detailed review and the thoughtful suggestions you provided.\n\n**Weakness 1**. Now results Tables only F1 numbers are bolded. Please bold best and underline 2nd best for the other columns also. \n\n**A**. Thank you for your suggestion, we will do it in our revision.\n\n **Question 1**. I wonder why some of the results in Tables 1 & 2 are not consistent. Such as Table 2, NIR F1 goes up when spurious rate is increased and then goes back down. Can this be explained somehow? Some random chance?\n\n**A**. We think this is a misunderstanding. The term $S$ represents the average sparsity of the selected rationales, that is, the average percentage of selected tokens in relation to the full text. \n\nThe reason why \"NIR F1 goes up when the spurious rate is increased and then goes back down\" could be due to two factors. First, the sparsity of the ground-truth rationale is approximately between 10% and 20%. When the specified sparsity exceeds 20%, the model is forced to select additional tokens, leading to a decrease in F1. When the sparsity is very low, the model might randomly select either spurious correlations or the real rationales, resulting in a relatively low F1 as well.\n\n**Limitations**. Does not directly work with LLMs.\n\n**A**. We now add the experiments conducted with the llama-3.1-8b-instruct model. We perform both 2-shot prompting and supervised finetuning. The results are in the General Response (at the top of this page). We find that our method can sometimes outperform the finetuned llama-3.1-8b-instruct. \n\nWe do not compare with GPT-4 because GPT-4's training involved extensive human alignment, which is very expensive. GPT-4 is overly powerful and not representative, as many studies on LLMs even use GPT-4 as a judge to evaluate different methods. Additionally, GPT-4 cannot be privately deployed.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "noV3WXDa96", "reviewer": "Reviewer_wByu", "summary": "This paper presents a novel approach to “rationalization,” that is finding explainable support for predictions made by black-box models such as deep neural networks.  It proposes to change the focus from extracted rationalization features to focusing on residuals, and argues that doing so makes it easier to suppress spurious features by making them indistinguishable from noise.  A novel KL divergence based criterion is proposed.  Empirical results on two datasets — BeerAdvocate and HotelReview — demonstrate the superior performance of the proposed approach over previously published rationalization approaches.", "questions": "Please see above", "limitations": "Limitations are sufficiently addressed", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* Novel formulation for extracting rationalization features\n* Clear presentation\n* Good empirical gains on two datasets", "weaknesses": "Two main limitations in my view are:\n\na) The reasoning presented in Sections 4.1 and 4.2 is insufficient in my opinion.  Section 4.1 states that $Y$ and $S$ have high mutual information, potentially as high as mutual information between $Y$ & $C$, and this is what makes it hard to distinguish between $S$ and $C$.  But then in Section 4.2 it is argued that $D( P(Y|X_{-S}) || P(Y|X) )$ would behave quite differently from $D( P(Y|X_{-C}) || P(Y|X) )$.  However, if $Y$ & $S$ have high mutual information, why would $D( P(Y|X_{-C}) || P(Y|X) )$ not be zero or close to it, making it hard to learn $C$?\n\nb) The empirical results are nice but it appears that the BeerAdvocate dataset has been retracted by the authors as mentioned on their website (https://snap.stanford.edu/data/web-BeerAdvocate.html). Also the HoteReview dataset does not seem to have many published results, I could only find a couple of papers on that.  It will be helpful if the authors can point out references that show results on the HotelReview dataset.\n\nMinor comments:\n* Define terms used in Eq 4.\n* Line 225 “each” -> “easy”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you deeply for taking the time to thoroughly review our paper. We are truly grateful for the insights and recommendations you've provided.\n\n**Weakness1 (The reasoning presented in Sections 4.1 and 4.2 is insufficient)**\nFor Section 4.1, we guess that you think $I(Y;S)$ can be as high as $I(Y;C)$ because we use the expression\n\n$\\mathcal{L_{MMI}}(C)\\leq\\mathcal{L_{MMI}}(S)<\\mathcal{L_{MMI}}(N)$ (L209)\n\nHere, we use \"≤\" only for mathematical rigor, but in practice, equality is hard to achieve. Using \"$<$\" directly might be more concise and clear. Below, we will analyze why equality is hard to achieve.\n\nThe overall idea is the [Data Processing Inequality](https://en.wikipedia.org/wiki/Data_processing_inequality). For the three variables $Y,C,S$, we have that  \n$P(Y,C,S)=P(Y)P(C|Y)P(S|C,Y)$.    \nAnd from Figure 2(a), we know that $S \\perp Y|C$. So, we further have \n $P(Y,C,S)=P(Y)P(C|Y)P(S|C)$, which means that they form a Markov chain $Y\\rightarrow C \\rightarrow S$ (note that the arrows here do not mean causality). Thus, with the data processing inequality, we have that $I(Y;C)\\geq I(Y;S)$, where the equality holds if and only if $Y \\perp S|C$.\n\n$C$ is the direct cause of $Y$, while $S$ is correlated to $C$ through some intermediate variables, and then to $Y$. Therefore, this conditional independence hardly holds in practical applications. As a result, we can hardly have $I(Y;S)=I(Y;C)$. Therefore, we can say $I(Y;C)>I(Y;S)$. And in our analysis of the damage caused by spurious correlations in Figure 3, we use this setting, i.e., $\\mathcal{L_{MMI}}(C)< \\mathcal{L_{MMI}}(S)<\\mathcal{L_{MMI}}(N)$. Even in this setting, spurious correlations can still pose obstacles to model training.\n\nRegarding the question in Section 4.2, if $D_{KL}(P(Y|X_{-C})||P(Y|X))=0$, we will have $P(Y|X_{-C})=P(Y|X)$, which means that $Y \\perp S|C$. Going back to the analysis above, this case can hardly happen. That is why we have Equation 7 (L251).\n\n**Weakness2 (datasets)**. \nThank you for your careful observation. The Beer dataset has indeed been withdrawn from the official website, so it is necessary to contact the dataset's authors to obtain authorization for academic use. We had already received permission by email before our research.\n\nOur datasets are the same as our main baseline MCD (NeurIPS 2023). \n\nHere are some other papers that use the Hotel dataset: DMAHR [1], DMR [2], FR [3], CR [4], GR [5]. Among them, CR and MCD are already included in our baselines. The most recently published GR is publicly available at the end of March 2024, so we do not take it as a baseline.\n\nBesides Beer and Hotel, there is another benchmark for rationalization, namely ERASER [6].\nCompared to Beer and Hotel, the main advantage of ERASER is that it contains manually annotated rationales in the training set, which can be used for supervised rationale extraction. However, the datasets in it are not designed to verify spurious correlations.\nConsidering this drawback, we did not use ERASER. We will add this discussion in our revision.\n\nHere, we use the graph classification dataset to verify the generalizability of our method in other fields, as this domain has a dataset, GOODMotif (GOOD means graph out of distribution, it is from a public graph OOD [benchmark](https://openreview.net/forum?id=8hHg-zs_p-h)), which contains both ground-truth rationales and spurious correlations.\n\nWe compare our MRD to the standard RNP and the strongest baseline MCD (other baselines such as Inter_RAT, NIR, and CR are designed for text data only and cannot be applied to graph data). The encoder is a three-layer GIN. We select a set of nodes from a graph to form the rationale. The sparsity is set to about $30\\%$ (close to the sparsity of ground-truth rationales). The results are as follows:\n\n|  |Acc| P | R | F1 |\n|---|---|---|---|---|\n| RNP |64.3| 43.5 | 45.9 | 44.6 |\n| MCD | 67.2|45.3 | 46.8 | 46.0 |\n| MRD (ours) |**71.3**| **48.7** | **51.9** | **50.2** |\n\n\n[1] Deriving machine attention from human rationales. EMNLP 2018.  \n[2] Distribution matching for rationalization. AAAI 2021.  \n[3] FR: Folded Rationalization with a Unified Encoder. NeurIPS 2022.  \n[4] Towards trustworthy explanation: On causal rationalization. ICML 2023.  \n[5] Learning Robust Rationales for Model Explainability: A Guidance-based Approach. AAAI 2024.  \n[6] ERASER: A Benchmark to Evaluate Rationalized NLP Models. ACL 2020.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Since most reviewers are interested in the results with LLMs, here we present the results of the experiments conducted with the **llama-3.1-8b-instruct** model. We perform both 2-shot prompting and supervised fine-tuning.

For 2-shot prompting, we provide the model with a negative text with its corresponding rationale, and a positive text with its corresponding rationale. For supervised fine-tuning, the supervison label is the classification label, since we perform unsupervised rationale extraction. We use 4*RTX 4090 24GB GPUs and LoRA to fine tune the models. We provide a detailed document in our anonymous code repository (https://anonymous.4open.science/r/MRD-0427/details_of_llms.pdf) to include all the details (including the prompt templates, LoRA fine-tuning parameter settings, and more).

In most cases, the model can output the rationale in the correct format. Here is an example:

**Input:** Pours a rather crisp yellow almost orange with a thin head. The aroma is dominated by sweet malts with just a slight hoppiness dancing in the background. The taste does have a surprising amount of hoppiness for a Pilsner. There is a good maltiness to it as well, but citrus hops just slightly overpower. The beer is very light and refreshing. This makes for an excellent summer session beer.  

**Expected output:** 1|pours a rather crisp yellow almost orange with a thin head .

**llama-3.1 output:** 1|pours a rather crisp yellow almost orange

Here "1" means that the class label $Y$ is positive. And the words after "|" represent the rationale. 
We convert the sentence into a list of words and then calculate the overlap between the model output and the ground-truth rationale. This might lead to a little higher results than actual because we do not take the word order into account.


But in 2-shot prompting, the model sometimes outputs additional parts along with the rationale (through manual observation, this situation does not occur frequently.). Here is another example: 

**llama-3.1 output:** positive|The overall tone of the review is positive, with phrases such as "a very nice balance of the two styles", "nice bitter dry aftertaste", "well carbonated", and "overall, a good beer" indicating a favorable opinion of the beer.
 
 In such cases, we use gpt-3.5-turbo to extract the content within the quotation marks. The GPT refined answer is "1|a very nice balance of the two styles nice bitter dry aftertaste well carbonated overall, a good beer".




The results (rationale quality, as measured by the word-level overlap) of supervised fine-tuning are as follows:

| Datasets | P | R | F1 |
|---|---|---|---|
| Beer-Appearance | 84.2 | 25.4 | 39.0|
| Beer-Aroma | 75.2 | 41.7| 53.6|
| Beer-Palate | 64.5| 34.8| 45.2|
| Hotel-Location | 58.6 | 39.0 | 46.8 |
| Hotel-Service | 77.3 | 40.6 | 53.3 |
| Hotel-Cleanliness | 54.9 | 31.3 | 39.9 |

The results of 2-shot prompting are as follows:

| Datasets | P | R | F1 |
|---|---|---|---|
| Beer-Appearance | 15.4 | 16.0 | 15.7 |
| Beer-Aroma | 17.9 | 24.2 | 20.6 |
| Beer-Palate | 13.0 | 22.2 | 16.4 |
| Hotel-Location | 45.8 | 59.1 | 51.6 |
| Hotel-Service | 45.4 | 51.7 | 48.3 |
| Hotel-Cleanliness | 39.3 | 43.0 | 41.1 |

LLMs are not good at counting, so we do not constrain the percentage length (i.e., sparsity) of the rationale extracted by the model.

Comparing the results of the supervised fine-tuned llama-3.1 with our results in Table 1, Table 2 and Table 3, llama-3.1 does not have a crushing advantage. For example, our MRD beats llama-3.1 on all three datasets of the correlated BeerAdvocate benchmark. On the less correlated HotelReview benchmark, our MRD achieves comparable  results to llama-3.1 when we set the rationale sparsity of MRD to be about $10\\%$.

Meta-review: This paper is within the Rationalizing Neural Predictions (RNP) framework, trying to find explainable support for predictions made by black-box models such as deep neural networks. It focuses specifically on finding the minimal sub-sequence of a text sequence that can reliably predict the assigned label(s). It proposes a novel criterion, MRD (maximizing the remaining discrepancy), which minimizes the negative KL divergence of the distribution of labels given the input with and without the rationalizing part. MRD is computed on the residual text, and authors argue that doing so makes it easier to suppress spurious features by making the residual signal indistinguishable from noise. MRD is tested on two different datasets with three tasks each and achieves promising results compared with other baselines leveraging MMI (maximum mutual information), regularization terms and its variants.

Reasons to accept
- The paper is well-written and self-contained, with comprehensive explanations of the main ideas and details provided in both the main text and the appendix. The paper is easy to read and understand even by researchers outside of the field.
- The proposed criteria are well-founded from both high-level conceptual and mathematical perspectives, adding to the paper's overall credibility. Furthermore, the idea itself is "neat", both interesting and intriguing with a potential to be applied to other areas.
- The experimental results show clear improvements over baselines. Additional experiments (with Llama-3.1) conducted during the rebuttal also support the hypothesis presented in this work.
- Code will be open-sourced.

Reasons to reject
- The scenario is a bit narrow and the two datasets are very similar. The beer advocate dataset is no longer available due to a request from the data owner. So it will be hard to compare the results to other work (although the hotel dataset has seen some use). Authors are urged to provide more analysis on additional datasets to further validate their approach, and increase reproducibility. 
- Broader validation of the idea (maybe even with images, video or speech) is desirable, since the criterion is not specific to text, although authors say their approach is "designed" for text. Authors present graph classification results, which somewhat alleviate this concern. 
- The proposed model does not outright outperform recent LLMs, e.g. LLaMa-3.1, which means the relevance in practical terms may be further limited
Decision: Accept (poster)