Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future
Authors: Linyi Yang
Yaoxian Song
Xuan Ren
Chenyang Lyu
Yidong Wang
Jingming Zhuo
Lingqiao Liu
Jindong Wang
Jennifer Foster
Yue Zhang
Venue: EMNLP 2023 Main (2023)
Keywords: Out-of-Distribution Generalization; OOD Robustness
TL;DR: We present the first comprehensive review of recent progress, methods, and evaluations on the topic of OOD generalization in natural language understanding.

Abstract: Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution. This poses important questions about the robustness of NLP models and their high accuracy, which may be artificially inflated due to their underlying sensitivity to systematic biases. Despite these challenges, there is a lack of comprehensive surveys on the generalization challenge from an OOD perspective in natural language understanding. Therefore, this paper aims to fill this gap by presenting the first comprehensive review of recent progress, methods, and evaluations on this topic. We further discuss the challenges involved and potential future research directions. By providing convenient access to existing work, we hope this survey will encourage future research in this area.

Out-of-Distribution Generalization in Natural Language Processing:
Past, Present, and Future
Linyi Yang * Yaoxiao Song + Xuan Ren Chenyang Lyu Yidong Wang", do
Jingming Zhuo" Linggiao Liu ,Jindong Wang" .Jennifer Foster Yue Zhang
Westlake University
Westlake Institute for Advanced Study Uniierssty of Adelaide
Dublin City University
Microsoft Research Asia
A MBZUAI
lyanglinyi, zhangyue| @westlake. edu. on
Abstract
accurately classify text instances from distributions different from those of the training data (Ben-David et al., 2010; Hendrycks and Gimpel, 2017; Hup- kes et al., 2022). This paper aims to provide a comprehensive overview of the current state of re- search in OOD generalization for natural language understanding, highlighting key methodologies, ad- vancements, and unique challenges.
Machine learning (ML) systems in natural lan- guage processing (NIPP) face significant chal- lenges in generalizing to out-of-distriuution (OOD) data, where the test distribution dif- fers from the training data distribution. This poses important questions about the robustness NLP models and their high accuracy, which may be artificially inflated due their under- lying sensitivity to systematic biases. Despite these challenges, there is a lack of comprehen- sive surveys the generalization challenge from an OOD perspective in natural language understanding. Therefore, this paper aims to fill this gap by presenting the first comprehen- sive review of recent progress, methods, and evaluations on this topic. We further discuss the challenges involved and potential future research directions. By providing convenient access existing work, we hope this survey will encourage future research in this area.
The importance of OOD generalization in NLP cannot be overstated, real-world data often ex- hibit diversity and unpredictability. Numerous ap- plications, such sentiment analysis, document categorization, and spam detection (Shen al.. 2021; Yang et al., 2022), necessitate models ca- pable of adapting to novel and unforeseen data distributions. While machine learning models gen- erally demonstrate strong in-distribution perfor- mance, their performance frequently deteriorates when confronted with OOD instances, underscor- ing the need for effective strategies that facilitate generalization beyond the training distribution.
1 Introduction
Although research on OOD generalization in NLP is emerging, it is not on the scale of other tasks like computer vision (Ye et al., 2021; Koh et al., 2021) and time series (Du al., 2021b; Gagnon-Audet et al., 2022). Furthermore, most related surveys in NLP focus on measuring and improving model robustness against adversarial at- tacks (Schlegel et al.. 2020; Arora et al., 2021), ), or providing causal explanations (Keith et al., 2020). Among them, Wang et al. (2021d) is the most rele- vant review to this paper, but their work does not differentiate between data-level variance and short- features and also not discuss LLMs.
Pre-trained Language Models (PLMs) (Devlin al., 2018; Liu al., 2019b; Radford et al., 2018) have revolutionized natural language pro- cessing (NLP) and enabled remarkable advances in Large-scale Language Models (LLMs) (Touvron et al., 2023; Gozalo Brizuula and Murrido-Merchan 2023; Pichai, 2023). Despite substantial progress in developing accurate models in several natural language understanding tasks, including sentiment analysis (Kaushik et al., 2019 Ni et al.. 2019; Yang al., 2021; Lu et al., 2022; Luo et al., 2022a,b), natural language inference (Williams et al., 2018), and machine reading comprehension (Kaushik and Lipton, 2018; Sugawara et al., 2020), major chal- lenge persists -iitt--diittibuttoon (OOD) gener- alization which entails the ability of a model to
To address these limitations, this survey provides extensive examination of the existing literature on OOD generalization in NLP, covering a diverse array of techniques and approaches. We focus on two perspectives of OOD generalization: the data distribution, which is model inneediindeen and the feature distribution, which is model-oriented. Ad-
'These authors contributed equally this work.
LLarge Language Models (LLMs))" refers to recent gen- crative models while "Pre-trained Language Models refers to small-scalc pre-trained models" in this paper.

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ditionally, we discuss the evaluation metries and benchmarks employed to assess the effectiveness of these techniques, as well as the limitations and drawbacks current methodologies.
of all possible documents, Y be the set of all possible labels, and D be a training distribution defined on X' x V. Suppose the true target distri- bution is Px.., which is close to but not identical to D with Px.y: # D. When we encounter a doc- ument that is drawn from distribution Qx that is significantly different from Px. we refer to it as out-of-distribution (OOD) sample. An OOD sample may have vocabulary language not presenting Px-
Throughout this survey, trace the evolution of OOD generalization techniques in natural lan- guage processing, from the early approaches based on traditional machine learning algorithms more recent advancements driven by deep learning ar- chitectures, also including the discussion of the most recent emergent abilities of LLMs. We iden- tiy the key innovations and breakthroughs that have shaped the field, while also highlighting ar- eas where progress has been relatively slow or incremental. Our analysis emphasizes the inter- connected nature of these advancements and the importance of driving fundamental research in the generalization problem towards unforeseen data distributions. In addition, this survey aims to iden- tify open challenges and future directions for OOD generalization in NLP, especially for LLMs. We discuss the limitations of current techniques, poten- tial avenues for improving model robustness and adaptability, and emerging research trends that may contribute to the development of more effective OOD generalization strategies.
A text classification model f: f: y is consid- ered OOD if its performance on Qx is significantly worse than on Px due to the distribution shift. The OOD detection function can be derived from a probb abilistic perspective using Bayesian inference. In this case, we can estimate the posterior probability of document being OOD given its bag-of- bag-of-worde words features through Bayesian model averaging:
P(OOD| Σ P(OOD)0,,))P0la))
Σ
P(x)
where 0 denotes the model parameters, æ is the ofg-oof-words representation of document, P(OOD|#) (f) is the prior probability of the model being OOD assuming the model parameter 0, P(x|OOD) 0) is the likelihood of observing the bag-of-words features æ given that the document is OOD and the model parameter 8, P(#) is the prior probability of the model parameter 0, and P(x) is the marginal likelihood of observing the bag-of-worde features æ.
The remainder of this survey is organized as follows: we formalize the scope of OOD general- ization in Section 2. Then, we propose a novel taxonomy towards OOD robustness and review existing methodologies developed for addressing OOD issues in Section 3. particular, we identify two salient aspects of OOD generalization, namely Data Variance and Shortcut Features. We outline two representative application scenarios in Section 4, namely Deployment in High-stake Domains and Social Bias. We also introduce the methods for improving the OOD robustness in Section 5 before discussing the redefinition of OOD in the of large language models.
The conditional OOD probability of model fo on input given the parameter 0 is defined as:
P(OOD 0, x)) = P(z|OOD, P(OOD(()
P(x16)
As can be seen from the above equations, OOD can be perceived in terms of both the data- and model- levels, robust models can be more resistant to data variances. The OOD detection function can be defined as a threshold on the posterior OOD probability:
2 The Scope of OOD Generalization
Denote set of labeled data as = ((1,, y1 ) 1= where an input x E X, output y E Y, and N the number of datasets. A training dataset Dtrain {( {(Itrunny Yirrin)} } is generated by sampling from D with distribution Pirain and the test dataset Diest ((Xtest: Ytest)} is sampled from D with distribution Prest-
g(x) = [[max P(ylx) < e],
where P(y[=) is the posterior probability of the document belonging to class y given the bag-of- words features æ, and is a threshold parameter that determines the confidence of the prediction.
(OOD) refers to the circumstance when Ptrain Ptest- In the context of text classification, let X' be the

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Model
Train
Test
Data Perspoctive
Technology
Biology
(Domain Generalization)
"Metaverse" "network" "Blockchain"
OOD Test
"COVI-19" "vaccine" diagnostie
0OD generalization
challenge
Premise
Two dogs are running through field.
Length (Spurious Feature)
Model Perspective
Entailment
There are some animals outdoors.
5(=7)
(Inductive Reasoning)
Neutral
Some puppies are running catch stick besides boy on the playground. 14(=12)
Contradiction Pets are sitting.
3(+5)
Figure Taxonomy OOD generalization scope and examples.
The OOD detection performance can be evalu- ated using metrics such as the Receiver Operating Characteristic (ROC) curve or the Kolmogorov- Smirnov (KS) statistic, which capture the trade-off between true positive rate and false positive rate, or the maximum distance between the cumulative dis- tribution functions of the OOD and in-distribution predictions, respectively.
fer (Johnson al., 2017; Levy et.,. 2017; Eriguchi et al., 2018; Wang et al., 2022). These studies can be broadly categorized into input-level variation and output-level variation. Notable comprehensive surveys dedicated to this task include those by Ram- poni and Plank (2020) and Wang et al. (2021d) but fail to decouple data and features.
Compositional generalization refers to the chal- lenge of learning the distribution of atoms given the surface distributions of their compositions. It has garnered significant attention in NLP research, encompassing areas such semantic parsing (lyer et al., 2017; Gupta et al., 2022), QA (Gu et al., 2021; Lewis et al., 2021), machine translation (Li et al., 2021), and general natural language under- standing (NLU) tasks (Lake and Baroni, 2018; Key- sets et al., 2020). Researchers (Keysers et al., 2020; Kim et al., 2021) have found that state-of-the-art neural models struggle to generalize to novel com- pounds in a manner similar to human performance. Several benchmarks have been introduced to eval- uate compositional generalization. For example. the SCAN dataset by Lake and Baroni (2018) is designed for sequence-to-sequenee generalization (Russin et al., 2019; Li et al., 2019a; Gordon et al., 2019; Andreas, 2020). Additionally, Keysers et al. (2020) and Kim and Linzen (2020) propose the CFQ and COGS benchmarks, respectively, for se- mantic parsing, Li al. (2021) propose the CoGni- tion dataset to assess how neural machine transla- tion models generalize to novel compounds (Hup- kes et al., 2020; Zheng and Lapata, 2021; Dankers et al., 2021; Jung, 2022).
3 Taxonomy of Out-of-Distribution Problems
We classify OOD problems into two perspectives, depicted in Figure I, namely Data and Features. Data variance encompasses the domain general- ization problem, while "shorteut features" repre- sent a range of issues typically caused by shortcut learning, which cannot be avoided from inductive reasoning.
3.1 Data
Data variance can seen as a typical problem of domain generalization methods, assuming the un- availability labeled or unlabeled data from the target domain. Previous studies have explored this approach in sentiment analysis (SA) (Kaushik et al., 2019; Ni et al., 2019; Yang et al., 2021; Lu et al.. 2022), natural language inference (NLI) (Williams al., 2018; Hendrycks al., 2020), and named entity recognition (NER) (Jia and Zhang, 2020; Plank, 2021). Different domains have intrinsically different feature distributions, and instances from different domains have different predicted vocabu- lary distributions, which leads to the OOD general- ization challenge, as shown in Figure 1.
To address the challenges of compositional gen- cralization, achieving OOD robustness is highly de- sirable current NLP models have shown fragility to variations in expression, where even minor punc- tuation changes can lead to different outputs (Wang et al., 2021c). Furthermore, Moradi et al. (2021) ob- serve significant performance decay of NLP mod-
Numerous NLP studies aim tackle system- atic variations between training and testing distri- butions, encompassing a vast body of literature on domain generalization (Blitzer et al., 2006; Ganin et al., 2016; Ruder and Plank, 2018; Han and Eisen- stein, 2019; Guo et al., 2020) and tross-task trans-

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els in domain-specifie tasks, such as the clinical do- main, due to noisc, grammar errors, missing words, punctuation, typos, and other factors. Additionally, Wang et al. (2021c) develop a unified multilingual robustness evaluation platform for NLP tasks to provide comprehensive robustness analysis.
algebraic and logic deduction, which turn out be extremely challenging learn using existing pre-trained models, leading to weak generalization.
Current NLP methods tend to learn implic- itly superficial cues instead of the causal associ- ations between the input and labels, as evidenced by (Geirhos et al., 2020; Guo et al., 2023b), and thus usually show their brittleness when deployed in real-world scenarios. Recent work (Sugawara et al., 2018, 2020; Lai et al., 2021; Wang et al., 2021b; Du et al., 2021a; Zhu et al., 2021; Bastings et al., 2021) indicates that current PLMs uninten- tionally learn shortcuts to trick specific benchmarks and such tricks (i.e., syntactic heuristics, lexical overlap, and relevant words) that use partial evi- dence produce unreliable output, which is partic- ularly serious in the open domain.
Another source of OOD data is human-crafted adversarial data. For example, the recently pro- posed contrast sets (Kaushik et al., 2019; Gardner et al., 2020; Warstadt et al., 2020) reveal the failure of capturing true underlying distributions, which show the fragility of models against small varia- tions of input expressions. In addition, although researchers also propose benchmark to reveal the importance of OOD detection (Hendrycks and Gim- pel, 2017; Hendrycks et al., 2020; Fort et al., 2021), there is a consensus that we still lack a standard definition of OOD examples and fine-grained eval- uations. A full survey of current available OOD datasets can be found in Appendix A.
4 Application Scenarios
We highlight the importance of OOD generaliza- tion in two real-world application scenarios, in which low OOD robustness may lead to serious consequences.
3.2 Features
Models' predictions are often influenced by short- cut features learned from spurious patterns between training data and labels, as well as existing short- cuts in the dataset. For instance, as illustrated in Figure (Model Perspective), sentence length has inadvertently become learned feature during train- ing. where 60% of the hypotheses in entailment examples have 7 or fewer tokens and half of the hy- potheses with more than 12 or fewer than 5 tokens are neutral or contradiction, respectively (Gururan- gan et al., 2018).
4.1 Deployment in Practical Domains
Despite the generalization ability of LLMs, such as ChatGPT (OpenAI, 2023b), the relatively low generalization ability of medium-size models hin- ders the deployment of NLP systems, especially for high-stake domains, from health and medicine to finance and business (Imbens and Rubin, 2015; Choi et al., 2023), and should be taken more se- riously. Notably, recent comprehensive evalua- tion of OOD generalization in text classification named GLUE-X (Yang al., 2022) shows that the average accuracy of PLMs on cross-domain evaluations falls significantly short of human per- formance, even for the highest-performing model (80.1 % human versus 74.6% % - model). In con- trast to GLUE, where over 20 single-model results outperform human baselines, none of the baselines, including InstructGPT and ChatGPT, considered in GLUE-X is able to surpass human performance using OOD tests. The lack of sufficient OOD gen- eralization ability is also related to social bias.
Ideally, a model should learn rational (Jiang et al.. 2021; Lu et al., 2022) features for robust generalization. Take sentiment classification for example. In order to decide positive polarity for the sentence "I like this movie.", rationale fea- ture should be "like" rather than "movie". The latter is referred to as a spurious feature (Kaushik et al., 2020), which leads to reduced generaliza- tion. Other cases of feature issues include shortcut features (Geirhos et al., 2020). For instance, in machine reading comprehension, if the question asks for a date and the input passage contains only date, model can bypass reasoning process and directly use the date feature for output (Lai et al., 2021). For numerical (Hendrycks et al., 2020; Wang et al., 202 a; Cobbe al., 2021) and logi- cal (Yu et al., 2019b; Liu et al., 2021c) reasoning tasks, the rationale feature should be the underlying
4.2 Social Bias
Recent studies (Gardner al., 2020) have uncov- ered problematic tendency for gender bias in sentiment analysis (Zmigrod et al., 2019; Maud- slay et al., 2019; Lu et al., 2020). Bias exists

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Data Augmestattinn
Label pessining
Bayer et al., 2021; Chen et al., 2021a; Li et al., 2022). In this study, our focus on data augmenta- tion regarding OOD generalization.
Label lipping
OOD Generalization Method
Madel-level Centrol
Causal based feature
Semi-fact Data Augmentation. One common type of data augmentation method in NLP involves sub- stituting part of the content or introducing perturba- tions to the original data, primarily focusing on en- hancing the diversity without altering the semantic meaning or label. Synonym substitution has been explored by Zhang el al. (2015), Miao et al. (2020) and Yue and Zhou (2020) to replace words or en- tities. Perturbation techniques typically involve manipulating tokens within sentences (Zhang al., 2018; Wei and Zou, 2019; Miao al., 2020; Xie et al., 2020; Zhao et al., 2019, 201 8a), as well as ad- versarial perturbations (Miyato et al., 2017; Cheng et al., 2019; Zhu et al., 2019; Jiang et al., 2020; Zheng et al., 2020), which employ large pre-trained models (e.g.. GPT-2, BART, BERT) for generating conditional data augmentations. Lu et al. (2022) ap- ply the human-in- in-the-looo llop technique incorporated with semi-fact data augmentation for improving the OOD robustness of PLMs in sentiment analysis. Counterfactual data augmentation (CDA) is widely adopted to mitigate bias in neural NLP tasks by operating biased text (Maudslay et al., 2019; Zmigrod al., 2019). A counterfactual example constructed by flipping the label helps learn real associations between input and label. For instance, Lu et al. (2020) proposes a CDA method to mitigate gender bias in neural coreference resolution, which is generic methodology for corpus augmentation via causal interventions (i.c., breaking associations between gendered and gender-neutral words). In text classification, Kaushik et al. (2019), Kaushik et al. (2020), and Wang and Culotta (2020) employ humans for generating counterfactual data, which has been shown to be effective to mitigate the in- fluence of spurious patterns. Automatic counterfac- tual generation aims to change the data distribution of the training data so that models can alleviate re- liance dataset-specific bias and exclude spurious correlations (Yang et al., 2021; Wang and Culotta, 2021; Wu al., 2021) and has been improved in a recent work (Fan al., 2023) by using deta-level and sentence-level constraints.
DRO
Training Techniques +f IRM
Tuning
Figure 2: Classifying methods regarding the OOD gen- eralization problem.
in different forms of language representations. in- cluding word embeddings (Bolukbasi et al., 2016; Caliskan et al., 2017; Zhao al., 2018b; Gonen and Goldberg, 2019), contextualized word embed- dings (Zhao et al., 2019) and sentence embeddings (May et al., 2019). Some found that the embed- dings of feminine words and masculine words are often clustered into different groups (e.g., occu- pation) (Bolukbasi al., 2016; Caliskan et al., 2017; Zhao al., 2018b, 2019; Gonen and Gold- berg, 2019). Gender bias also affects coreference resolution systems, which tend to link a pronoun to occupations dominated by the pronoun gen- der (Rudinger et al., 2018; Zhao et al., 201 8a). In machine translation, Vanmassenhove et al. (2018) and Stanovsky et al. (2019) find that models tend to make stercotypical assignments of gender roles when translating occupation words. Apart from gender bias, there are other forms of social bias in NLP data, such as disability (Hutchinson al., 2020), race (Kiritchenko and Mohammad, 2018), age (Diaz et al., 201 8), etc.
5 Methods
Existing work to address OOD issues in NLP can be categorized into three groups: data augmenta- tion 51). model-level control (Sec.5.2), and training approaches (Sec.5.3) shown in Fig. De- scriptions of current OOD generalization meth- ods categorized by tasks are introduced in the Ap- pendix.
5.1 Data Augmentation
Data augmentation (DA) techniques are employed to enhance the diversity of training data without the need for explicitly collecting new data (Feng et al., 2021). This approach proves beneficial for improv- ing the generalization of NLP models by reducing overfitting and enhancing robustness. Several ex- isting surveys have discussed data augmentation in low-resource NLP scenarios from different per- spectives (Hedderich et al., 2021; Feng et al., 2021;
5.2 Model-level Operations
Feature representation learning holds pivotal role in OOD generalization. In this section, we evaluate model-level approaches, focusing on two critical

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aspects: invariance and the causal factor.
In addition, learning a causal model requires fewer examples to adapt to new environments (Schölkopf et al., 2021).
Invariant Common Features Research on invari- ant features as a means facilitate transfer learning has been an enduring pursuit in the field. In the context of discrete linear models, various methods have been developed to harness data from the target domain to aid representation learning. For instance, Structured Correspondence Learning utilizes un- labeled target-domain data to establish mappings between features across different domains (Blitzer al., 2006). On a similar note, Daumé III (2009) employs labeled data for this purpose.
There has been much research using causal inference to improve OOD generalization. For in- stance, in social media, Pryzant et al. (2018) induce a lexicon that helpful for target label prediction yet uncorrelated to a set of confounding variables, and Saha et al. (2019) perform propensity score- based causal analysis on social media posts for evaluating the effect of psychiatric medications.
5.3 Training Approaches
Additionally, Johnson and Zhang (2005) also uses unlabeled data, but in a different setting. Tran- sitioning to neural models, adversarial learning emerges as prevalent technique (Goodfellow et al., 2015; Ganin et al., 2016; Zhang et al., 2019a). In this approach, an adversarial loss function is em- ployed train domain classifier. This classifier attempts eliminate domain-specific information in the hidden layers, thereby producing representa- tions that are more amenable for cross-domain (Liu et al., 2018; Li et al., 2019b; Du et al., 2020) or cross-task decision making (Johnson et al.. 2017; Levy et al., 2017; Eriguchi et al., 2018; Lee et al.. 2019; Wang et al.; Keung et al., 2019; Vernikos et al., 2020; Wang et al., 2022). In sentiment analy- sis, Liu et al. (2018) and Du et al. (2020) conduct adversarial training to derive enhanced domain- invariant features for cross-domain classification.
In the presence of distribution shifts, optimization tends to be influenced by irrelevant signals, result- ing in severe failures when applied to OOD test data (Liu et al., 2021b). Consequently, there has been significant interest in recent work regarding training techniques.
Distributionally Robust Optimization (DRO) aims to learn a model on the worst-case distribu- tion scenario (domain) while expected to generalize well on test data. To improve the worst-case do- main, Sagawa et al. (2020) propose group DRO method that requires explicit group annotation of samples. Methods based on group DRO and its variants have recently been applied in NLP tasks, such as NLI (Sagawa et al., 2020; Liu et al., 2021b), machine translation (Zhou et al., 2021a), spoken language understanding (Broscheit et al.), and toxi- city detection (Michel et al., 2020). For example, Oren et al. (2019) design a DRO procedure for generative modeling that minimizes the simulated worst-case distribution scenario over the mixture of topics. Zhou et al. (2021c) consider the worst-case with language pairs to optimize multilingual neural machine translation.
Feature clustering and other techniques are also adopted to learn invariant features, which requires OOD generalization on unseen tasks. For instance, Johnson et al. (2017), Arivazhagan et al. (2019). Ji et al. (2020), Liu et al. (2021a) la) train translation models for better learning of Linguage-indeenndeee representations, which help the model generalize unseen language pairs. More recently, Yin et al. (2022) categorize source contextualized representa- tions to boost compositional generalization.
Invariance Risk Minimization (IRM) Different from DRO, which focuses on domain shift robust- ness, IRM methods focus on learning invariant rep- resentations. IRM (Arjovsky et al., 2019) is a re- cently proposed learning paradigm that estimates non-linear, invariant, causal predictors from mul- tiple training environments for improving OOD generalization. It has several advantages. For ex- ample, it does not need extra knowledge to manip- ulate the original data (e.g.. human intervention or rulc-based methods) and extra large computa- tion. Existing work has studied the IRM and its variants in NLP. Choe et al. (2020) investigate IRM on synthetic settings and simple MLP and machine
Causal-based Features Causal inference aims to determine the effectiveness of one variable on another variable (Holland, 1986; Morgan and Winship, 2015; Imbens and Rubin, 2015; Pearl et al., 2000). Because the relationships between the causal features and the labels are invariant under distribution shift (Pearl al., 2000; Quionero- Candela et al., 2009), learning causal relationships allows model to acquire robust knowledge that holds beyond the distribution of a set of training tasks or the observed data (Schölkopf et al., 2021).

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learning models in sentiment analysis. Dranker al. (2021) study OOD generalization for NLI by IRM, in which environments are constructed by ensuring whether the dataset and bias are synthetic or naturalistic. Peyrard et al. (2021) ) propose a language framework based on IRM-games (Ahuja et al., 2020) for learning invariant representations that generalize better across multiple environments. The OOD objective in learning the causal invari- ance can also be viewed as a multi-objective op- timization problem, which has been explored by Chen et al. (2023b) using a pareto learning strategy. Tuning Three popular tuming approaches for pre- serving the pre-trained features are reviewed: prompt tuning, adapter tuning, and linear probing.
model with linear probing and then performs fine- tuning (LP-FT). This approach has been theoreti- cally proven to improve both in-domain and OOD performance for deep neural models.
Large Language Models
Large language models (LLMs) have attracted in- creasing attention in the field of artificial intelli- gence recently. However, towards artificial general intelligence (AGI), the OOD robustness is still under-explored (Wang et al., 2023b). Given its importance, we review the recent work on the OOD generalization of L.MMs. OOD Definition It is of imminent importance to reframe the OOD definition in the era of LLM dom- inance since the pre-trained corpora of LLMs are not publicly available. The absence of pre-trained corpus information makes it hard to define OOD examples for LLMs in NLP. Although providing an accurate and strict definition remains challenging for large foundation models, researchers make at- tempts to build label-sharing OOD data for LLMs from two perspectives, namely, synthetic data, and distribution shift over time. Synthetic data is gen- erally defined as artificially annotated information generated by algorithms or simulations, which can be hand-crafted as challenging OOD examples for LLMs. Distribution shift over time refers to the idea of using real-world datasets collected after 2021 as OOD test data, which is the latest data collection time of ChatGPT (Wang et al., 2023b). Another type of OOD data refers the task of generalizing to unseen classes. For instance, in open-set label shift (Garg et al., 2022), the test data includes examples from novel classes not present in the training data, making it impossible for clas- sical small models to predict correctly. LLMs such as ChatGPT can alleviate this issue by using in- context learning, evidenced by recent research (Xu et al., 2022). This means that LLMs can be used improve robustness with minimal human in- tervention but they cannot fully solve this problem and open-set label shift remains challenging.
a crucial property
Adapter tuning (Rebuffi et al., 2017; Houlsby et al., 2019) contains few task-specific train- able parameters and are injected between layers of frozen pre-trained models. Training only the adapter modules can help models achieve com- petitive performance on various tasks, such as multi-task text classification (Houlsby et al., 2019), NER (Pfeiffer et al., 2020), multi-task QA (Fried- man et al., 2021), and multilingual speech transla- tion (Le et al., 2021).
Prompt tuming (Liu et al., 2021f) methods con- vert the downstream problems into language mod- eling problems. It adds prompt tokens as the prefix to the questions and converts them input texts, then use a pre-trained language model to process the input texts in order to generate the answer se- quences. There are two variations of prompt tokens, hard prompt tokens, and soft prompt tokens. Tun- ing hard prompt tokens requires fine-tuning the pre-trained models (Petroni et al., 2019; Cui et al., 2021). Tuning soft prompt tokens only need to fine- tune the prompt tokens, thus preserving the pre- trained features (Li and Liang, 2021; Lester et al., 2021; Qin and Eisner, 2021; Liu al., 2021g). Soft prompt tuning is helpful for wide range of cross- domain tasks, such as NER (Chen et al., 2021c, 2022b), text classification (Gao et al., 2021; Zhong et al., 2021a; Utama et al., 2021), table-to-text (Li and Liang. 2021), QA and paraphrase detection (Lester et al., 2021) and NLU (Liu et al., 2021g).
OOD Detection Previous research on OOD de- tection has employed models to identify test ex- amples that come from different distribution (Hendryeks and Gimpel, 2017; Hendrycks et al., 2018). Some of these approaches introduce new training objectives, such as using a contrastive ob- jective (Winkens et al., 2020; Zhou et al., 2021c). When the type of distribution shift is known, the
Linear probing (Liu et al., 2019a) fine-tunes the top layers while keeping the lower layers frozen. Compared full fine-tuning, linear probing per- forms better for OOD generalization but reaches lower accuracy on IID data. Kumar et al. (2022) propose a two-step strategy, which first trains the

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model can be trained exhibit uncertainty when presented with known OOD examples (Hendrycks et al., 2020). However, the distribution of SOTA LLMs, such as ChatGPT and GPT-4 is hidden and cannot be inferred. Very recently, CoNAL (Xu et al., 2022) provides an alternative for generating novel examples which simulate open-set shifts and has proven to be effective for OOD detection.
ent perspectives, including factuality (Wang et al., 2023a), hallucination (Rawte et al., 2023), and eval- uation methods (Chang et al., 2023).
7 Future Directions and Conclusion
We consider multiple promising directions for im- proving the OOD robustness from four perspec- tives: (1) enhancing the learning of such salient causal features, either by the help of human guid- ance (Kaushik et al., 2019; Lu et al., 2022) via human-in-the-loop or through psychologically in- spired neural structures (Chowdhery et al., 2022), can be worthy of consideration; (2) data-centric AI: both the selection of training data and the care- ful design of prompt learning have proven effective in domain generalization (Chen al., 2022c). In addition, the emerging ability of large-scale lan- guage models holds a huge potential for OOD gen- eralization, benefiting from the instruction tuning, which requires a high-quality data construction pro- cess; (3) alignment methods: this can be effec- tuated through the deployment of reinforcement learning algorithms, be it in an online or offline setting (Christiano al., 2017; Chen al., 2023a); (4) neuro-symbolic modeling for NLP: purely neural models like ChatGPT can possess incredi- bly powerful generalization abilities. While is more-or-less accepted that purely neural models face challenges of reasoning beyond surface-level patterns. In order to avoid picking up spurious cor- relations, neuro-symbolic approaches are proposed to improve the models' OOD robustness by com- bining the learning capabilities of neural networks with the expressive power of symbolic reasoning (Alon et al., 2022; Jung et al., 2022; Manhaeve et al., 2018; Hamilton et al., 2022).
Regarding language models (LLMs), the deep- fake detectors aimed at distinguishing content gen- erated by humans or LLMs is closely related to pre- vious algorithms designed for OOD detection (Guo et al., 2023a). When comes to deepfake detec- tion, one intuitive approach is to employ statistical boundaries that differentiate linguistic patterns be- tween human-writte and machine- machine-generated text (Mitchell et al., 2023). However, these statistical methods have limitation: they assume access to the model's prediction distributions is possible, which hinders their application models behind APIs, such as ChatGPT. An alternative paradigm involves training neural-based detectors (Bakhtin et al., 2019; Fagmi et al., 2021), including the offi- cial implementation of OpenAl (OpenAI, 2023a).
OOD Robustness Previous studies have exten- sively examined various aspects of ChatGPT in the domains of law (Choi et al. 2023), ethics (Shen et al., 2023), reasoning (Bang et al., 2023) and planning (Yao et al., 2023). However, limited atten- tion has been given its robustness (Kawaguchi et al., 2017) against out-of-distributio (OOD) in- puts. Evaluating OOD robustness a reliable man- ner poses a significant challenge due to the massive and unknown training data of LLMs. Wang et al. (2023b) offers an initial investigation into the ro- bustness of ChatGPT by presenting OOD results on Flipkart and DDXPlus. Building on this work, Ye al. (2023) delimit the robustness of LLMs in comparison to conventional models, with focus on aligning the threat model to the realistic deploy- ment of LLMs. Additionally, Zhu et al. (2023) measures LLMs' resilience to adversarial prompts using adversarial textual attacks on character, word, sentence, and semantic levels. Collectively, these evaluations raise similar concerns regarding the limited robustness of ChatGPT. Among different attack levels, character-level attacks demonstrate higher robustness, while word-level attacks pose the greatest vulnerability. Recently, we have no- ticed that several papers survey the robustness issue in Large Language Models (LLMs) from differ-
This paper presents ambitious attempt to cate- gorize the challenges of OOD generalization, focus- ing on both data and model levels. By undertaking this categorization, our aim is shed light on the limitations of current methods, emphasize the cru- cial nature of OOD robustness, and provide quick access to existing references for further exploration. In addition, we emphasize the ongoing significance of OOD robustness in the era of large language models, emphasizing the need to address this as- pect. We call upon researchers in the NLP commu- nity to delve deeper into the proper definition of OOD the context of large models and develop ap- propriate benchmark tests that accurately measure the OOD generalization ability of LLMs.

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8 Limitations
Yejin Bang. Samuel Cahyawijaya, Nayeon Lee, Wen- liang Dai, Dan Su, Bryan Wilie. Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multi- task, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv: 04023.
When we categorise OOD-related NLP work, mostly focus the recently appearing papers, which can be retrospected classical generaliza- tion studies. Moreover, the literature on domain generalization and domain adaptation has not been distinguished in this work. Lastly, the introduction of classical transfer learning algorithms has not been included for the time being.
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebas- tian Riedel, and Pontus Stenetorp. 2020. Beat the ai: Investigating adversarial human annotation for read- ing comprehension. Transactions of the Association for Computational Linguistics, 1662-678.
Acknowledgement
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021a. Improving question answering model robustness with synthetic adversarial data generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1830-8845
This publication has emanated from research con- ducted with the financial support of the Pioneer and "Leading Goose" R&D Program of Zhejiang under Grant Number 2022SDXHDX0003 and the 72nd round of the Chinese Post-doctoral Science
Max Bartolo, Tristan Thrush, Sebastian Riedel, Pon- tus Stenetorp. Robin Jia, and Douwe Kiela. 2021b. Models in the loop: Aiding crowdworkers with generative annotation assistants. arXiv preprint 1rX0v:212..0006..
Foundation project 2022M722836. Yue Zhang is the corresponding author.
Jasmijn Bastings, Sebastian Ebert. Polina Zablotskaia. Anders Sandholm, and Katja Filippova. 2021. " will you find these shortcuts?" protocol for evaluating the faithfulness of input salience methods for text classification. arXiv preprini arXiv:2111.07331
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A Appendix
an effort to clearly outline the various challenges tied to OOD generalization, we divide our discus- sion into two distinct aspects, represented by Table (Data Distribution) and Table 2 (Feature Distribu- tion), respectively. In Table 1, we focus on issues that arise due differences and changes in data. It systematically lists the ways in which variability in data attributes can make difficult for mod- els to effectively generalize to out-of-distribution samples different tasks. In general, we cate- gorize the reference materials from two perspec- tives: annotation artifacts (lahel-sharing generaliza- tion) and output variance (label-different general- ization). Different from label-sharing generaliza- tion approaches, which rely on few-shot unla- beled data from the target domain, label-different generalization based on zero-shot learning using clustering and other techniques. We also outline the typical datasets for each task and corresponding representative methods.
On the other hand, we concentrate different set of issues from the feature perspective. These problems originate from the models' tendencies to learn from spurious patterns or "shortcut features" in the data, which might not reflect the true underly- ing relationships between inputs and labels, leading to the generalization challenge. Ideally, a model should learn rational features. However, inductive reasoning naturally relies on patterns and trends from the training data. This reliance can result in models performing well familiar data but poorly when faced with new, OOD examples. The OOD generalization challenge can not be avoided when using deep learning based approaches, yet it can be alleviated by several techniques as illustrated in Table 2. Collectively, Table and Table provide a thorough understanding of the challenges in OOD generalization, and set the stage for developing strategies to address these issues.
To provide a fine-grained description of OOD generalization methods in NLP, introduce key points of representative methods in different tasks from Tables 3-5, ranging from the scope and method to dataset and metric. We hope these mate- rials can serve as a quick access to existing refer- ences for further exploration.

---

Data Variance
Task
Papers
Key Methods
Typical Datasets
Sentiment Analysis
Ganin al. (2016); Chen and Cardie (2018); Luparra al. (2020).
Adversarial learning
Amazon Reviews;
Multinomial advensurial net- works.
WiCoPaCo;
RWSE
Invariant representation leamning; Training on adversarial exam- ples.
(2017); Khayrallah and Koehn (2018).
Belinkev
Wikipedia:
MT
Revision Dataser;
MERLIN corpus.
Label-sharing
Noisy supervised pre-training:
CoNLL2003: Tweet, Web-
Liu et al. (202 ldy Huang et al. (2021a).
Calibrated confidence methods.
page; Wikigold.
ROC Story; HOTPOTQA;
Cai (2017): Min (2019); Bartolo et (2021baj;; Lyr al. (2022).
QA
Generator-in-the-lowe models.
NewsQA: SQuADL.I; AdversarialQA.
Poliak al. (2018); Naik al. (2018); Zellens (2019); McCoy (2018); Feng (2020); Sakaguchi (2019);
Data sugmentation:
Humna and mode-lllll-iiee aeael-in-ii in-the- -n-te----o--- loop:
Stress
ANLE SWAG: HANS; SNLI-
Test;
Annotation artifacts
NLI
Adversorial filtering;
al. (2020); Nie
(2020); Liu al.
Training on adversarial exam- ples.
hard: MultiNLL-hadd,
(2020); Gardner al. (2021): Pozeshkpour et (2022): Wu et al. (2022).
Kaushik and Lipton (2018); Sugawara et al. (2018, 2020); Bartolo et al. (2020); Lai et al. (2021).
SQuAD; CNN:
MRC
Shortcut investigation,
Whadid-What DuoRe.
Vunmaseenowe et al. (2018); Stumossky al. (2019); Tomalin et (2021): Choubey al. (2021).
Adversurial leamiing; Gender- self- siff-trainig.
MT
WineMT; MoST-SHE.
Coreference
WineBias:
Rudinger (2018); 8); Zhao al. (2018a).
Data augmentation.
Resolution
Winogender Schemas.
Data augmentution;
Sexist Tweets Abusive Tweets (abx).
Toxicity Detection
Park et al. (2018): Dixon al. (2018).
Debias word embeddings.
Snell al. (2017); Ghaddar and Langlais (2017); Wu al. (2020); Nguyen al. (2021):
Self-training methods; Prompt- based metheds: Information the- ories; Protntype-han based meth-
CoNLL20033 MIT Movie;
Label-Different
Restaurant;
Cui et al. y; Ma et al. (2021); Zhou al. (2021b); Lee et al. (2021); Das et al. (2021):
Ourput
WNUT2017:
ods; Distance-bused methods:
5.0 Dataset; BioNER.
Wang et al. (2022).
Knowledge enhanced methods.
soowledge-ehanneed
Multilingual corpus pre- traitrainnng:
Back translation:
WM1114 14: WMT New-
Johnsen et al. (2017); Zhang al. (2020), Ati- vazhagan et al. (2019); Ji et al. (2020); Liu et (202
Machine Translation
Invariance representation ing:
stest 2012; Newstest 2013; Newstest Newstost 2015; IWSLT 2017.
Language independent represen-
tations leamiin.
Table 1: OOD generalization challenges related the data variance.

---

Flexibility Expression
Task
Papers
Key Methods
Typical Datasets
Oren et al. (2019); Hendrycks et al. (2020); Wang et (2021b): al. (2021la); Liu (2021h); Moradi Samwald (2021): Náplava al. (2021);
augmentation;
Regularization shortcuts;
WildNLP; L; TextFlint;
Spurious features identification & moval; Distribationally robust optimization (DRO).
Text Classification
IMDB Reviews:
Kindle Reviews.
Wang (2021c).
Cheng et al. (2019); Zhang et al. (2019b); Zhou et al. (2021a); Hewitt (2021).
NIST:
WMT'14 XSUM;
Natural Language Generation
Adversarial attack learning:
WevNLG;
Group DRO: Robust fine tuning.
Open-domain
Czamowsku (2019), (2019); Gardner al. (2020); Warstadt al. (2020); Hu al. (2020); Lewis al. (2020); Lazaridou al. (2021); Liu et al. (2021e); Koh et al. (2021);
et
BLiMP; XTREME;
Evaluations
Contrast sets; Fine-gruined evaluations.
MLQA; ARKIV; Wilds:
SQuAD.
Compositional generalization
Chen et (2022a).
Lake and Baroni (2018); Russin al.
(2019y, Li (2019a); Gordon
Dedicated train objects;
(2019): Andreas (2020); Keysers
SCAN: CFQ: COOS.
(2020); Kim Linzen (2020y; Kim
Structure annotation.
al. (2021).
(2017); Lake and Baroni (2018); Dong and Lapata (2018); Lake
Span-level supervised attention:
Human-ino seaun-i--the--oree
Semantic Parsing
GEO, CFQ.
(2019); Yu et al. (2019a); Furrer et al.
Meta seguence-to-seqeeeeeeeeee learning: Structurally diverse sampling.
(2020); Kim (2021); Gupta et al. (2022).
Machine Translation
Chen al. (2020); Zheng and Lapata (2021).
al. (2021);
Neural symbolic stack machines: Representation disentanglement.
CoGnition; SCAN.
GRAILQA; TriviaQA;
Gu et al. (2021): Lewis et al. (2021);
Data augmentation; Promgt tuning: Continual pre-training.
Open Natural Questions: WehQuestions.
Bogin et al. (2021).
Dong Lapata (2016):
SQUAD: DROP; LogiQA;
(2019h); Rogers al. (2021); Liu al.
GAN: Graph neural aetworks: Knowledge enhanced methods.
Logic reasoning
MRC
HorpotQA; ReClor; AR- LAST.
(2021e); Zhong al. (2021b); Huang al. (2021b). Brown et al. (2020); Cobbe et (2021); Drori et al. (2021); Hendrycks et al.
Self-supervised training (GPT3); Training verifiers; Program synthesis (Codex).
Mathematical Problem
MATH Datasets;
DeepMind Datasets.
(2021)
Table 2: OOD generalization challenges related to shortcut features learned by models.

---

Work
Task
Scope
Mathod
Dunasct
Matre
Dang and Eapat (3016)
MRC
Logicl
model imvrramt opueseattion
enhaneed cucoder-decdder JOBS, GIDD ATIS, IPTTT
Accurscy
(Domiin Variance)
Ininchase
Yt (2019h)
MRC
Lupical Reasoning (Bias)
reqpiiing
(RaCler)
Accurscy
Lirerill al. (20211c)
MRC
Logical Reswning (Biss)
Introduce a comprehensive dataset which Legip
soueed from peettwwiiten queciioss.
Zhoug et (2021b)
MRC
Lugica Russoning (Bias)
Intoduce a new damneet damser consisting questions
from the Law School Admisaion Test from 1901
AR-ESAT
Accazzy
2016
Esaallih SQUAD,
Who-did-Whut
Kaushik Lipion (2018)
MRC
artifactss
datsetss finling that question and pascage-nnly oflmn per surpriongly well.
bAbL, SQUAD, CBT. CNN, Whodid- What
Aocurac
SQUAD,
QAIMRE LAMBADA, WikIRauding,
Saguwarn
datasets, dudiug that question passage only models olten perfemm sargrisingy well.
PruPtm, SQUAD, DuaRC
Accuuccy
Amottzion artifacts (shoricut)
Prepose
ablation-iesd
DuoRC, Hotport2,, SquAD. SQUAD, ARC, MCTes, MulsR., SWAG
MRC
methudology
capacity
Accuncy Fl
Banolo et al. (2020)
MRC
Ansotatinn artifacts (shorizut)
Propose an adversarial anaouicon data colle-- fim methed. Toeming on mnsabenarially vollactel sampleskeads strong genecalization.
SQJAD1.1
F1
Aroutation artifacts (sbortcut)
Propuse two synthetic dosaset and two new methed to investigate shorteut MRC espe- cially on paraphrasing.
Loi es al. (2021)
MRC
QWM Para detaset derived from SQLAD
Chong (2019)
improve robustness.
Prepose abversarial input MT
camuus 20020013,014
nEW-
BLEU score
word-levell campling
only
sapames
Zhung (20196)
(caposure
predicted sequeace selected semence optimun.
sequence
durng
(2021a)
(domain)
Prepuse new leomnnng objeettive for MNMT based on DRO.
58-lasgnages TED cocpus,
Aroutation artifacts
Present methods to combine the bemelts of full
BLEU score
Howin et (2001)
NLG
(domain)
and lightweigtt honetniigg achieving smong formance both ID and OOD.
WeaNLG, XSUM. Open-domain QA
lxcast match socurccy
ROUGE scoce
Table 3: Methods towards OOD generalization challenge in the task of MRC and NLG.

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Woek
Tack
Scape
Mothod
Ditisct
Metrie
Fareral (3019)
NFR
Eeput variomoe
Design cross domnis cross- tikk netwock
for domais generuisation.
CBS Newws
CONLL BINNLPEP,,, BRNKKIBUG,
Zhang (2020)
NER
Exput variance
NER domain genevuiiatioon
Malti usk leamnnng multi cell LSTM
BANLLIIPC, BONLPI3CG,
CONL1.003,
Broad
F1
Liu al. (20230)
NER
Expuc variance
domain related coppus and propose baseline.
Introduce a cross domain NUR cataset with
CONLLZUR3, ClosSNER
F1
Dasa Angmerattio for coodonmii NER Pro-
ct (202
NER
Expuc variance
pose a novel neural archinecture transfomm the
Omnenntes S.O. Tamporal Twittrr
datn reprosenaaioo from high resource to low rescurce dociais.
Ghadlar and Langloris (2017)
NER
Ourpui variance
Proguse large, high quility, annotated eorus quality WINER for enssedemmin NER.
CONLL MUC; ONTO, WGOLD, WED
F1
(2020)
NER
Ousput vuriance
musked with genmiiization,
Adversrially
CONLL2203, WNUT2DIG, FIN, INLPBA,
BUZGM, BiNLLIN BioNLPIIBPT
Propuse texcher-dtsdent looming mathul fro linguaal NBR.
Wu al. (20/2))
NER
Uuspui variance
CoNLL 2002, CoNLL 2003
Mguyen all (3021)
NER
Ouigut variance
Cross domain zero shot NER with knowlolge base.
music, science datset
F1
Propose templaae based method for NUR sequeace
language model making punblea frunework, where templates filled
Cui al. (2001)
NFR
Quigut sariance
original by casdidate camed anllty span
CONLE, MIT Movse Review, MIT Ressurant Restew
the sequesee the target sequence.
Ma al. (2021)
NER
Ourgui variance
Refonmiate NER taiks LM problems without templates.
C.NT1.000,, Ostonatos S.O, MIT-Mosic
F1
Propose Misked Eaanyy Language Modeling
Zhou al. (20216)
NER
Ousput vasianee
work for jow-resouree NER to alleviste the token label misaligamen.
(MELM) as a novel data nigiemattion frame- C-NTL
F1
Propose demoostratin based leamning method for NER, which lats the input be prefseed by demoostratios for contett leazinng.
Loe (2021)
NER
Ousgut vuriance
CONLL-2003, Omnonotes BCSCDR
Propose costrstive learning technicue token distbbution dis-
Dasetal (202))
NER
Ourgui vtriance
that optnizes initaal clas- specitic aimbbtte Few- NER.
OmteNotes, CANLEO03, WNUT '17, GUM
F1
Wing al. (2012)
NFW
Ourgut varimnce
Propose infonuation thenretic perspeetive mathrd to imporee onl-ff-wsabblary miliins proliction.
TOUTTOIE.TWINNNER..RRR Types, Conli1--0Y
Coalloa.
Propuse calibrnted confidenze eximation and integrae
(2021d)
NER
Data
seiferaining fismework for
C.NLL Tucet, Welspage, Wikigold
boorling perforaace in peneril miny wittings
Ga all (2021)
QA
Compostioual genotiization (Biss)
Constract new large scale, high-qualtt dataset GrsilOA, and pripose novel BERT-bool
GRAILOQ
Fl
KBQA model.
Dvalusie three popoltr open domain benchmork datasets and find that all medels perforn dranit-
Lauis et al. (2001)
QA
Compositioall genertizaiion (Blis)
ically wone on quessons that conncc be memo-
Wabiuusstions TávaQA Open Natural Ques lisss
Fract monch stome
raed from training sess.
Propose
that computes represeniation decotation question spess in bottons-
Bogis ol. (2021)
QA
genertizaiion (Blis)
Compositonal
up. compositonal manner using akyl parser. inductive blas towards tree surictures drumatically impeoves systeautic goses alization out cur-of-diitilaate distibattion exumples..
CLOS
arithmetic expeessions benchmark, CLEVK
Cai al. (2017)
QA
Compostional genstalization
Propose hisrarctical KNN amension encode senience endaugs.
candi-
Aocuracy
Compuctional genetalization
Min (3019)
QA
Propose single hop BERT based RC model.
HORPOTVA
Medias timtle exam- Tie
Validated Mudil Frmor Rale (vMER) Modion time vali- deeed medel-ffoling ample Downsrram effective-
Compositioul genzralization (dlomain)
Latoddee gesemter-ia--te loop model real-tine maimtiin advantages DADC reduce
Bartolo et al. (2021h)
QA
which.
SQUADL.I AdverarisiQAA GAA-asciond dats
(FI acces)
Estmnd the scope of "DOD" by spliting DA ex- smples into diffcrent sublomains accoeding their several internal chaneterictes inchuding qpociion type. tusi lingh, anowrr prodiion Ex- smine the performance of QA systems tninel on the dana from different vuldkamiis.
Compostional
Lyuetal.
QA
generalization
SQUAD I.I. NewsQQ
F1
(domain)
Table 4: Methods towards OOD generalization challenge in the task of NER and QA.

---

Whek
Task
Scape
Method
Dalsett
Meric
Wong stal. (20111)
SA
Arnotstion snibett
(dhoriou))
Automntioally alentil sach spuziona concla- tots in NLP model it soie.
SST. Yel,, Orcepatio dittact, Amazos Kitcken. Aiazzon Exccooiis
Iupostase sour
Precision
Kanshik es dl (2019)
SA, NLI
Eapui vullance
CDM.
SNLI, IMDB
Accunay
Kanbiis al. (2025)
Tapui varlance
evalutte the efficayy of CDA.
Yelp. Amazon, Senteval, CRD, SNLL MuhiNII
Accttoyy
Hontryckee (2000)
Ixput
evilutt OOD geseruiiationn of pio wained
SST Revies,Auson Rovioo.MllNL
Accrncy
Way and Calota (2020)
Expui vacianee
Trai spaiics leature detecist inpooe OCD
grterahnntton
IMDII revenss Kindle novess Toric con-
Bat. Tock leesl
Accutay
Wang and Culotaa (2021)
SA
Expui vallanee
nree in quemoess comeistions via CDA.
ius finlar detectis & topposctobal
IMDB seriono, Annaon, Kinile reviewa
Accunay
Yang
Iopurt
improe precalsonios.
Yelp
Sentral
Accrnvy
La el. (1012))
SA
Expar wersece
Sngroiis ndxastaoos via sato Scati fasted dute asgancatation
IMDS, Amsznn reviews, Yelp reviews, SST. Semfool-0017 Paitter
Accenvc
< hon and Cantir (101))
SA
Para morue
impovina OOD dleation via leazaing
veltort featones
Amazco roviooo FDU PTU deiect
Accenay
Jeduscor et al. (2017)
Ouipul vaiiauee
shor moning on multilngssl corpes
WMF 14, WM
BLEU scoec
Zhang et al. (2005)
Ouput varlazce
imgnoee
beeett kaggus suckrarsbeiiss.
enfonz
OPUS-100
Win
Ariradhagaa et al. (2019)
MT
Outpa varianae
imppose wao-sbe MT: loes invariane reprcsmn
tatins via arciltary losses
tewsest 2012, WMFI1 newstes 2013, IWSLT 2017
BLEU scoee
el. (2025))
Ouput
Impoove zero langurzes.
obuis universal en
Europort MIlIUN
scoee
L/u et al. (20014)
MT
Ouput variazce
impoove zero shot MT: removing residial con acclicus.
[WSLT 2017. Baropart V7, PMindia
BLEU spoce
Composiional
Ieqroee compoioraa peneraiiation:
Fsl. El.FU scree
Zheng and Lapata (2021)
MT
generditnation
which encourges divertanleeeen
extemin sqpeeas--ooeeeeeee madkss
COGS, CFQ
Compoard ionstation ernr rare
Excad uaach scoec
Belinkov lick (1017)
lascaacc modkl insoites::: stuciacinosian
word repecsentatinss vobust trsiting.
IWSLF 2016, WSColiCa Wileperin Revicon
Daaect MERLIN cuppe, Coocs. nanuiily roays
sooee
Stanevkky al (2019)
MT'
Arnotttionn antifutss
prosan challange ccolusing gender machite tratizzion.
WInNM
F1
Aoceracy
Clasbey es (2021)
MT
Aumouio attifuts
proptee saska--litreed add-iiiiii (GFST) to impoose groder tranclation aeceracy
WindMT, MUST-SHE
Recill
Fl
BLEU scoce
Williams (2018)
tapur variance
intralase MaliNLI bendurrak.
McDINLL SNLI
Aocerocy
(2018)
propree Suco daizes
MuhiNLI
Accrnsy
Zelles d. (2018)
NLJ
Aunottion attfiats
propuie detect SWAG for mumuiig common szaeoring of NII model.
SWAG, SNLI
Accunayy
Fau d. (2019)
NLI
Autontiios atifiats
ovenlalos
portsl-ingus
SNLI
Aucuracy
McCly (2019)
Auuoiaion attffuts
Istrodused HANS desect which contaiiss
symtenicc
MchINLI HANS
Accusacy
La /imseee er al. (X00))
NLA
Arnonstion anthett
piure OOD gonciliction.
Use ATL/TI to redice damnet Notes, this im-
SNLI ANLA HANNS NLI- NLL-Doognostes, Stress 1245, QNLL MIRNNI
Accenvy
Sakagrchi (3000)
Annotstion anifrets
lazer than Wargual Schena Chalkege. Iuteddass ANLI collested via Acta-
Inteddare WINOGRANDE, whish lander
WENOGRANDE, KuowRel Wangessks
DPR
COPA
Accenvy
Nie stal (2020)
NLI
Aunorion attifats
moskatooorrs burzn-andd mnde procllune.
ANLA SNE. MeloNiLJ, SNILH-Hon NI. Stree Tests
Accrncy
Fmor mis
derive sivrrannl ecsmples seme the hyputheis only bis and exploct elgibte ways to minggic suci hine
Liu et al. (2025)
NLJ
Aunoution antfiats
SNLI, MoLINEI
Accuncy
Wa (2022)
NLJ
Actestation antffuts
trnersting dehivaal chrracts strough fiteer comtibue
conclations.
SNLLL MuNINII, HANS, SMLLHeed, MukiNLL hard
Accoracy
Propoce a shorictt miligstion framowork 13CI using knowledge ditillation frumework to sup prese the motil Scom
Do al. (2001a)
Annoution antficts (duotize))
oosscoeiikai pio lers shoncor degiee.
MaNINLL FIVER and MuNINLI bacidoor
Accrncy
samples
Phopose a simple too stage approch, that mm snires the lost over a movighted dasset csec- end sagel wlave we upa siaeia trising exupll for ane miscbecifer fed at the and of few strpe of staddad trining (lese stage). overoome the reguimment expersive group prop
Liu es al. (20216)
Arnotttive antffecis (domaia)
MatINLL ChllConmens WILDS
Accuncy
Osca et (2019)
Test doserivation
Aunotation antfects
(hoo)
Propene DRO hood appoorh called
condiound value cid.
Yelp, ONEBWORD. TPIRADY
pepplsslt
Table 5: Methods towards OOD generalization challenge in the task of SA NLI, and MT.


Reviews:
[{"review_id": "LK7O3P7ZYX", "reviewer": "Reviewer_DYuH", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the constructive comment.\n\nQuestion A: The concepts of \"Pre-trained Language Models\" and \"Large-scale Language models\" are not clearly defined.\n\nAnswer: We agree with your point that the concepts of \"Pre-trained Language Models\" and \"Large-scale Language models\" need to be further distinguished. To address this issue, we will use a unified name, ‘Large Language Models (LLMs)’, referring to recent generative models. \n\nQuestion B: The place of Large Language Models in the structure of the paper is confusing: Section 6 is dedicated to Large Language Models but their use is already described in Section 4.1.\n\nAnswer: We will merge the description of Section 4.1 into Section 6. We will also provide a more detailed overview of the current progress of LLMs in Section 6.\n\nQuestion C: It would be useful to further connect previous research in domain adaptation and the current challenges faced by large language models (it is done a little bit in lines 653-665).\n\nAnswer: Based on the taxonomy presented in [1], previous domain adaptation algorithms are classified into three categories: data manipulation, representation learning, and learning strategies. For closed models like ChatGPT or GPT-4 in the era of LLMs, representation learning and learning strategies cannot be easily manipulated. However, it is still worth exploring whether previous methods can be effectively applied to open LLMs like LLaMA.\n\n[1] Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W. and Yu, P., 2022. Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering.\n\n\nQuestion D: lines 292-297: it would be interesting to discuss the possible consequence of the shortcut described in reading comprehension in the case there are multiple answer candidates and/or no answer [2, 3].\n\nAnswer: Thanks for the reminder! We will add the citation of SQuAD 2.0 and zero-shot event extraction accordingly. \n\n[2] Know What You Don’t Know: Unanswerable Questions for SQuAD. Pranav Rajpurkar, Robin Jia and Percy Liang, Proc. of ACL 2018\n\n[3] Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. Qing Lyu, Hongming Zhang, Elior Sulem and Dan Roth, Proc. of ACL 2021\n\n\nQuestion E: line 624: I think that the findings of Wang et al., 2023 should be presented. This is particularly relevant to Section 6.\n\nAnswer: Thanks for your suggestions. In the work of Wang et al., 2023 [4], the authors found that the ID-OOD performance does not always correlate positively with ChatGPT and text-davinci-003. They also demonstrated that there's significant room for improvement in ChatGPT, especially from an adversarial and out-of-distribution perspective. However, it's important to note that this preliminary evaluation relies on a small sample size, so the conclusions should be approached with caution.\n\n[4] Wang, J., Xixu, Hu.,  Hou, W., Chen, H., Zheng, R., Wang, Y., Yang, L., Ye, W., Huang, H., Geng, X. and Jiao, B., 2023, April. On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective. In ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models.\n\nTypos:\nThank you for pointing out these issues. We will revise our manuscript accordingly.", "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": "- The survey paper is well-written, structured and comprehensive.\n- It addresses a very important topic and challenge in natural language processing.", "reasons_to_reject": "- The concepts of \"Pretrained Language Models\"and \"Large-scale Language models\" are not clearly defined. It is not clear whether the latter only refers to recent generative models. A third expression, \"large language models\", is also used. Also, the place of Large Language models in the structure of the paper is confusing: Section 6 is dedicated to Large Language Models but their use is already described in Section 4.1. I think Section 6 should also include more details about the current knowledge, as reported by the cited papers.\n\n- It would be useful to further connect between previous research in domain adaptation and the current challenges faced by large language models (it is done a little bit in lines 653-665).", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper is a survey on out-of-distribution (OOD) generalization. It provides a definition and describes many works in the fields of domain adaptation with a focus on more recent papers, while addressing multiple approaches in a structured way. Two main contributions of the present survey is the distinction between the data and model perspectives and the discussion of recent large language models in the context of OOD generalization.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Presentation:\n- The different concepts used in the context of pretrained language models (e.g. pretraining, finetuning, prompting) should be defined and explained in more details.\n- I think Figure 1 should be explained in more details in the text, in particular for the Data Perspective.\n- line 217: \"fail to decouple\" - Maybe \"do not decouple\" is more suitable here.\n- lines 292-297: it would be interesting to discuss the possible consequence of the shortcut described in reading comprehension in the case there are multiple answer candidates and/or no answer (See for example the notions of \"plausible answers\"/ \"competitive entities\" , respectively in \"Know What You Don’t Know: Unanswerable Questions for SQuAD. Pranav Rajpurkar, Robin Jia and Percy Liang, Proc. of ACL 2018\" and in \"Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. Qing Lyu, Hongming Zhang, Elior Sulem and Dan Roth, Proc. of ACL 2021\").\n- line 624: I think that the findings of Wang et al., 2023 should be presented. This is particularly relevant to Section 6.\n\nTypo:\n- line 71: not discuss -> do not discuss\n- lines 323-324: the sentence is incomplete."}}, {"review_id": "A9spOfj1OB", "reviewer": "Reviewer_t4je", "summary": "", "questions": "- A. When presenting the various methods, it would be useful to have some empirical comparisons of their effectiveness on benchmark OOD tasks.\n- B. The paper mentions limitations of current OOD datasets and evaluations. What are some ways the community could improve OOD benchmarks and metrics to better assess model generalization?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the thoughtful review and suggestions.\n\nQuestion A: When presenting the various methods, it would be useful to have some empirical comparisons of their effectiveness on benchmark OOD tasks.\n\nAnswer: Thank you for the suggestion. In the next version of the paper, we will provide empirical comparisons of the mentioned methods using the WILDS and GLUE-X benchmarks to demonstrate their effectiveness. \n\nQuestion B. The paper mentions the limitations of current OOD datasets and evaluations. What are some ways the community could improve OOD benchmarks and metrics to better assess model generalization?\n\nAnswer: We thank the reviewer for pointing out this pivotal question for future research. Indeed, developing advanced OOD benchmarks and metrics is crucial for the future of both OOD and language model research.\n\nBased on the experience from this survey paper, we believe there should be at least two aspects for future research:\n\n(1) Diversifying Metrics: Current OOD benchmarks only focus on one metric (often accuracy). As language models grow in size and face increasingly complex tasks, it becomes infeasible to rely solely on one metric to gauge OOD performance. For instance, the holistic evaluation work [1] offers a good example of how to design diverse and challenging benchmarks for LM evaluation, which could extend to the OOD area.\n\n(2) Designing Anti-causal Benchmarks: Anti-causal benchmarks would involve reversing the cause-effect relationship, where the effect is used to predict the cause. Introducing anti-causal benchmarks could challenge existing systems with the most difficult examples. A model that can perform well on both causal and anti-causal benchmarks is likely to be more robust and less sensitive to changes in data distribution. Such a feature is particularly crucial for OOD generalization.\n\nWe will elaborate further on this topic in the updated version of the paper.\n\n[1] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A. and Newman, B., 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110.", "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": "- The taxonomy of OOD issues from data and model view is insightful and provides a structured way to understand this problem.\n- The paper comprehensively covers a wide range of tasks, datasets, and evaluation metrics related to OOD generalization.", "reasons_to_reject": "- More analysis comparing the effectiveness of different methods on benchmark datasets would be useful.\n- The limitations of current OOD datasets and evaluation practices could be discussed further.", "questions_to_authors": "- A. When presenting the various methods, it would be useful to have some empirical comparisons of their effectiveness on benchmark OOD tasks.\n- B. The paper mentions limitations of current OOD datasets and evaluations. What are some ways the community could improve OOD benchmarks and metrics to better assess model generalization?", "paper_topic_and_main_contributions": "This paper provides a comprehensive review of research on out-of-distribution (OOD) generalization in natural language processing. The key contributions are:\n1. It proposes a novel taxonomy to categorize OOD issues into data perspective (domain generalization) and feature perspective (shortcut learning).\n2. It comprehensively summarizes and compares different methods for improving OOD generalization, including data augmentation, model-level operations, and training approaches.\n3. It highlights the importance of OOD robustness in real-world applications such as high-stake domains and for mitigating social biases.\n4. It discusses redefining OOD in the era of large language models and reviews recent work evaluating their OOD capabilities.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "0hcHCmvZgs", "reviewer": "Reviewer_Z5rz", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive suggestions.\n\nQuestion A: It's unclear whether the authors have proposed any concrete solutions or strategies to address the problem of out-of-distribution generalization in NLP.\n\nAnswer: Our team has a rich history of working on OOD generalization in NLP, with over six methodology papers to our credit. However, due to EMNLP's anonymity policy, we are unable to list specific papers in this rebuttal. Our in-depth analysis of these issues inspired us to compile this survey.\n\nQuestion B: The paper might be too focused on the problem itself, without providing sufficient practical insights or guidelines for dealing with it.\n\nAnswer: As highlighted in Section 5 (Methods), we have outlined three primary strategies to enhance the OOD generalization capabilities of models. Furthermore, we plan to include additional quantitative results related to these methods in the updated version of our paper.", "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": "The strengths of the paper include:\n- The paper addresses a crucial problem in NLP, making it highly relevant to current research in the field.\n- The authors provide a comprehensive overview of the issue, considering its past, present, and future, which can be very informative for other researchers in the field.\n- The paper stimulates further discussion and research into the robustness of NLP models and the challenges of dealing with OOD data.", "reasons_to_reject": "- It's unclear whether the authors have proposed any concrete solutions or strategies to address the problem of out-of-distribution generalization in NLP.\n- The paper might be too focused on the problem itself, without providing sufficient practical insights or guidelines for dealing with it", "questions_to_authors": "", "paper_topic_and_main_contributions": "The main contributions of the paper are:\n- The authors provide a comprehensive overview of the problem of out-of-distribution generalization in NLP, discussing its history, current state, and future prospects.\n- They shed light on the significant challenges faced by ML systems in NLP when dealing with OOD data.\n- The paper stimulates further discussion about the robustness of NLP models and the need for more accurate measures of their performance.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents a survey on the problem of out-of-distribution generalization in natural language processing. The authors propose a novel taxonomy to categorize OOD challenges, describe multiple approaches in a structured way and also discuss recent large language models in the context of OOD generalization. In particular, the paper stimulates further discussion about the robustness of NLP models and the need for more accurate measures of their performance. Thus, it could be very helpful and impactful for researchers working on that topic.

Pros / Strengths:
- Survey addresses a crucial problem in NLP
- Well-written und well-structured
- Novel taxonomy is helpful in understanding the problem
- A wide range of tasks, datasets, and evaluation metrics related to OOD generalization are covered in the paper
- Stimulates further discussion about the robustness of NLP models and how to measure their performance

Cons / Weaknesses:
- Some concepts are not defined clearly enough
- There is no quantitiative analysis of methods on benchmark datasets

Action items for improving the paper:
- Concepts around recent LLMs need to be defined more clearly
- Limitations of current OOD datasets and evaluation practices should be discussed in more detail
- Practical insights and recommended ways of dealing with different OOD issues should be better highlighted throughout the paper
Decision: Accept-Main