Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Authors: Bingsheng Yao
Ishan Jindal
Lucian Popa
Yannis Katsis
Sayan Ghosh
Lihong He
Yuxuan Lu
Shashank Srivastava
Yunyao Li
James Hendler
Dakuo Wang
Venue: EMNLP 2023 Findings (2023)
Keywords: active learning
low-resource learning
explanation generation
natural language explanations

Abstract: Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts' real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). 
While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Bingsheng Yao
Ishan Jindal
Lucian Popa IBM Research
Rensselaer Polytechnic Institute
IBM Research
Yannis Katsis IBM Research
Sayan Ghosh
Lihong He IBM Research
UNC Chapel Hill
Yuxuan Lu Northeastern University
Shashank Srivastava UNC Chapel Hill
Yunyao Lit Apple
James Hendler Rensselacr Polytechnic Institute
Dakuo Wang Northeastern University
Abstract
Dual-ifodel Learnigg System
Uniabeted
Real-world domain experts (c.g., doctors) rarely annotate only decision label in their day-to-day workflow without providing expla- nations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural lan- guage explanation of data point. This work proposes a novel AL architecture to support experts' real-world need for label and expla- nation annotations in low-resource scenarios. AI. architecture leverages an explanation- generation model produce explanations guided by human explanations, a prediction model that utilizes generated explanations to- ward prediction faithfully, and novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Au- tomated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human notation efficiency and trustworthiness with our AL architecture. Additional ablation studies lustrate the potential of our AL architecture for transfer learning, generalizability, and in- tegration with large language models (LLMs). While L.LMs exhibit exceptional explanation- generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.
Annotated Dests
Human Annotator
Active Learing
Datr Selector
Selected
Annobated Explanation
Annotited
Explanation- Beneratin Model
Generated
Prediction Modis
Label
Figure 1: Our dual-model AL system architecture every iteration: 1) the AL data selector chooses a few unlabeled examples; 2) human annotators provide an explanation and label for each data instance; the anno- tated explanations are used to finetune the explanation- generation model; 4) the annotated labels and generated explanations are used to finetune the prediction model. Then, humans can review the predicted labels and gen- erated explanations for unlabeled data and start the iteration. Green arrows indicate the training target.
1 Introduction
2021) demonstrate astonishing performance var- ious NLP tasks, including Question Answering (QA) and Question Generation (QG) (Rajpurkar et al., 2016; Duan et al., 2017: Kočiský al., 2018; Yao et al., 2022), Natural Language Infer- ence (NLI) (Bowman et al., 2015; Wang et al.,
State-of-the-art (SoTA) language models (Devlin et al., 2019; Radford et al., 2019; Winata et al.,
dd. wwng@northeasterm.ccmmmm edu Corresponding Author. Work done while Yunyao was at IBM Rescarch.

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2018), etc. Despite the superior generative capa- bilities, the lack of faithful explainability within these "black boxes" may lead to mistrust of their predictions (Lipton, 2018), where humans, on the other hand, can develop intermediate rationales to facilitate the mecisign-making process.
2) A prediction model that accepts the data con- tent and the generated explanations for prediction. We integrate AL to reduce human annotation efforts and establish human trustworthiness by ac- tively engaging humans in the training process. We design a novel data diversity- based AL sampling strategy to select the most representative examples by exploiting the explanation annotations, which is analogous to the prevalent core-set (Sener and Savarese, 2017) strategy. Our AL architecture aims to support low-resource model predictions and Al trustworthiness by explicitly generating natural lan- guage explanations. Specifically, we request label and free-form explanation annotations for a very limited number of examples (e.g.. 3 or 10) selected by our AL sampling strategy at every AL iteration. Subsequently, the generated explanations serve input for the final prediction, demonstrating the po- tential for these explanations to support the model's predictions faithfully.
The lack of explainability and untrustworthiness of models is magnified in the real world (Drozdal et al., 2020), where domain experts rarely only an- notate decision label in their daily workflow with- out providing explanations (i.e., clinical diagnoses by clinicians) (Zhang et al., 2023), and humans need explanations to understand and trust model predictions (Zhang et al., 2021). Therefore, a few approaches were proposed to retrospectively ana- lyze the probability distribution within the model ask models to generate explanations along with predictions (Ribeiro et al., 2016; Lundberg and Lee, 2017; Yu et al., 2019; Rajagopal et al., 2021; Chen et al., 2021), despite, the former is still very difficult for laymen to understand while the latter explanations are not faithful toward predictions.
We conduct two AL simulations with different amounts of samplings and iterations on large- scale NLI dataset with human-annotated expla- nations to justify incorporating explanations in AL data selection can consistently outperform ran- dom, traditional data eiversity-basee and model probability- bassdd sampling strategies. We make the code publically available'.
As researchers looked into the quality (Car- ton et al., 2020: Yao et al.. 2023) of human- annotated natural language explanations (Camburu et al., 2018; Rajani et al., 2019; Aggarwal et al., 2021), they discovered numerous issues in exist- ing datasets (Geva et al., 2019; Chmielewski and Kucker, 2020; Narang et al., 2020; Sun et al., 2022), such that the human annotations are of low qual- ity and significant inconsistency. Furthermore, the ever-increasing costs in terms of labor, finances, and time for large-scale, high-quality data annota- tions remain a persistent challenge for the research community. This challenge has given rise to var- ious methodologies to reduce reliance on human annotations, such as Active Learning (AL) (Settles, 2009). AL is a human-in-the-loop framework that utilizes AL sampling strategies to iteratively select small number of representative examples, request oracle annotations, and subsequently fine-tune the model using the annotated data. However, prior AL works predominantly focus on labels and overlook the fact that real-world scenarios often need both labels and natural language explanations.
A human evaluation of perceived validity, ex- plainability, and preference of the generated expla- nations among our system, a SoTA explanation- generation system, and human- canotatd explana- tions shows that, despite human explanations being ranked highest, explanations generated by our sys- tem are preferred over the SOTA system. Addition- ally, we conduct three ablation studies to explore the capability and potential of our proposed AL ar- chitecture in transfer learning, generalizability, and incorporating large language models (LLMs) for explanation generation to further reduce human ef- forts. LLMs demonstrate exceptional explanation- generation capabilities on relatively simple tasks. However, their effectiveness in handling complex real-woria tasks warrants in-depth study.
2 Related Work
In this work, we propose dual-model AL ar- chitecture for human annotation of labels and ex- planations, drawing inspiration from the human decision-making process. Our system consists of:
2.1
Datasets with Natural Language Explanations
Wiegreffe and Marasovic (2021) conducted a com-
1) An explanation -ggerrattonnnn model guided by numan-providee explanations
hittps://githut http://ggghhab..cmmeuuhaii explanation amriched-cc active- -earmingg

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prehensive review of 65 datasets with explana- tions and provided a 3-class laxonomy: highlights, free-text, and structured. Among the large-scale datasets with free-text explanations, e-SNLI (Cam- buru et al., 2018) is a prominent one, which ex- tended the Stanford Natural Language Inference (SNLI) corpus (Bowman et al., 2015), classifi- cation task to determine the inference relation be- tween textual contexts (premise and hypothe- sis): entailment, contradiction, or neutral. The e- SNLI dataset (examples are shown in Appendix A) contains
Fu al., 2013; Schröder and Niekler, 2020; Ren et al., 2021) of sampling strategies provide two high-level selection concepts: data diversity and model probability. We propose a novel data diversity-based strategy that leverages human- annotated explanations to select data. Our data se- lector shares similar concept with the established data-based clustering strategies (Xu et al., 2003; Nguyen and Smeulders, 2004) and core-set (Sener and Savarese, 2017) that aim to select the most rep- resentative data while maximizing diversity. Com- pared with model probability- -based strategies, data diversity-bassee ones are modellaggostii and need much less computing resources, whereas the for- mer requires inference on unlabeled examples to calculate probability.
free-form explanations for 549, 367 examples in train, 9, 842 in validation, and 9, 824 in test split.
Another popular group of datasets extended the Commonsense QA (CQA v1.0 and v1.11 versions) datasets (Talmor et al., 2019), includ- ing two variants of Cos-E dataset (CoS-E v1.0 and CoS-E v1.11 (Rajani et al., 2019)) and the ECQA (Aggarwal et al., 2021) dataset. Many re- cent works (Narang et al., 2020; Sun et al., 2022) have found explanations in CoS-E be noisy and low-quality, and thus, Aggarwal al. (2021) care- fully designed and followed the explanation annota- tion protocols to created ECQA, which is of higher quality compared with CoS-E.
In addition to AL., Marasovic et al. (2022) in- troduces a few-shot self-rationalization setting that asks model to generate free-form explanations and the labels simultaneously. Similarly, Bhat et al. (2021) proposes a multi-task self-teaching frame- work with only 100 train data per category. and Bragg et al. (2021) provides guidance on unifying evaluation for few-shot settings.
2.3 Natural Language Explanation Generation
In this paper, leverage the e-SNLI dataset as the benchmark dataset for our AL simulation ex- periment because 1) the classification task is pop- ular and representative, 2) the massive data size ensures data diversity, and 3) explanations for a classification task may provide more effective help compared to CQA task where training and testing data may be unrelated. We additionally conduct an ablation study on the ECQA dataset explore the generalizability of our proposed AL architecture.
Different approaches have been explored to en- hance the model's explainability by asking them to generate natural language explanations. Some of them (Talmor et al., 2020; Tafjord al., 2021; Latcinnik and Berant, 2020) propose systems generate text explanations for specific tasks. Dalvi et al. (2022) propose a 3-fold reasoning system that generates reasoning chain and asks users for cor- rection. Other recent works (Paranjape et al., 2021; Liu et al., 2022; Chen et al., 2022) explore different prompt-based approaches to generate additional information for the task and examine the robust- ness and validity. We believe that our dual-mode system provides and uses explanations explicitly towards prediction, while the relinaationalioation setting falls short. Hase and Bansal (2022) argues that explanations are most suitable as input for pre- dicting, and Kumar and Talukdar (2020) designed a system to generate label-wise explanations, which is aligned with our design hypothesis. Neverthe-
2.2 Active Learning for Data Annotation
Owning to the paucity of high-quality, large-scale benchmarks for long tail of NLP tasks, learn- ing better methods for low-resource learning is ac- quiring more attention, such Active Learning (AL) (Sharma et al., 2015; Shen al., 2017; Ash et al., 2019; Teso and Kersting, 2019; Kasai et al., 2019; Zhang et al., 2022). AL iteratively 1) se- lects samples from the unlabeled data pool (based on AL sampling strategies) and queries their an- notation from human annotators, 2) fine-tune: the underlying model with newly annotated data, and 3) evaluates model performance.
less, there exist other works (Wiegreffe et al., 2021; Marasovic al., 2022; Zelikman al., 2022) that explore the use of rationalaaainoo setting. We include the self rationalzaioooooo setting in our human
few AL surveys (Settles, 2009; Olsson, 2009;

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evaluation of the explanation quality in Section 4.4,
Explanation-genrratie Model:
Training Input explain: what is the relationship between
3 Dual-Model AL System
[hypothesis) and [premise] choice1: entailment choice2:
neutral choice3: contradiction
Training Target (human annotated explanations! Model Generation [generated free-form explanation]
3.1 System Architecture
Figure 1 illustrates our proposed framework. The system comprises three primary model
AL
Prediction Model:
Training Input question: what is the relationship be- tween [hypothesis] and [premise] choicel: entailment choice2: neutral choice3: contradiction <sep> because [generated free-form explonation|
modules: 1) an asplanation-generall that takes the data, fine-tunes on human-annotater explanations, and generates free-form explanations; 2) a prediction model that accepts the data content and the generated explanations as input, fine-tunes on human- provided labels, and predicts the final human-provided
Training Target [human annotated label] Model Prediction [predicted category]
Table I:
The prompt-hased input templates for both models in our system, with the e-SNLI (Camburu et al., 2018) dataset as an example.
label: 3) an AL data selector that selects set of representative examples in terms of the seman- tic similarity between each unlabeled data text and labeled data's human explanations. The AL data se- lector plays a erucial role in finding a small, highly representative set of samples at every iteration, and further details of our AL selector are in Section 3.2.
the original task content. For the e-SNLI dataset, the task content becomes "what the relationship between" the hypothesis and premise sentences; 2) "choiceN" is followed by candidate answers, where N E [1, for the e-SNLI dataset corre- sponds to entailment, neutral, and contradiction. We pass the choices to the explanation--eenraane model, expecting that it will learn to generate free- text explanations that may reflect potential relation- ships between the data content and the task;
In each AL iteration, after the data selector sam- ples unlabeled examples for human annotations, we first fine-tune the palllattioongnerattion model supervised by numan-provided free-form explana- tions. Then, we instruct this model generate ex- planations for the same set of data. Subsequently, we fine-tune a prediction model using the data con- tent and explanations generated by the previous model as input, supervised by human-annotaa labels. The fine-tuning process teaches the predic- tion model to rely on the explanations for predic- tions (Yao al., 2023). Additionally, we fine-tune the prediction model with model-generail expla- nations instead of human-annotated ones for better alignment during inference, especially when no human annotations are available. After each AL it- eration, we evaluate the framework on a standalone evaluation data split.
3) for the prediction model, an additional prompt " "because"" is followed by the explanations gener- ated by the opplanation-generattii special token to separate the original task content and the explanation."
model. We use
3.2 AL Data Selector
Algorithm Our Data Diversity- AL Selector
Variables:
Drnen unlabeled data in train split
Dam previously- annotated data
dian data content a string of d,, (for e-SNLI. is the premise and hypothesis oe mreviously-annotee
free-form explanation of d, number of data to be selected each iteration
Both the ielianatioongenerattnnn model and the prediction model can be any SoTA sequence-to- sequence models, such as BART (Lewis et al., 2020) and T5 (Raffel et al.. 2020). In this work. we utilize T5 as the backbone for both models and design a prompt-based input template for both mod- els, as shown in Table 1, inspired by a few existing works (Schick and Schütze, 2021; Gao et al., 2021; Zhou et al., 2023). To elucidate how each prompt addresses different part of data content:
Deran Ien(D Nyre = pree) for D, Dmnin do iteration then
scores, Amax EdjeDran similarityld dfais daaaer else
scorea, apeer LdfeDDer similarity(a/ "d end end for
D) train = rank D Daais by score
Daniensd select data from D' with equal intervals Human annotation D wirerd
D D Duieenai D Durct + D Diienn
1) "explain." and "questionn"" are the leading prompts in the explanation- -eeerrationnnn model and the prediction model, respectively, indicating dif- ferent tasks for both models and are followed by
According to recent surveys of AL (Settles, 2009; Olsson, 2009; Fu et al., 2013; Schröder and Niekler, 2020; Ren et al., 2021), there are two primary approaches for AL data selection:

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model probability-based and data diversity-baser approaches. Model probability-be approaches, firstly, aim to select examples about which the mod- els are least confident. These approaches involve conducting inference on unlabeled data at every it- eration, which consumes more time and computing resources. Unlike data diversity- -baedd approaches, they are not model-agnosten which may affect the effectiveness of the sampling strategies depending on the model use.
Preliminary Experiment for Dual-model System on e-SNLI (averaged on 3 trials)
81.61
$4.08
85.19
86.11
87.02
80
Accuracy
68.62
00.1
60
34
40
300
1500
4200
9000
15000
Train Data Amount
Secondly, data diversity- -rusee approaches lever- age various data features, such as data distribution and similarity, to select a representative set of ex- amples from the candidate pool while maximizing diversity. This paper introduces data diversity- based AL selection strategy that shares concept similar to traditional data-based clustering strat- egy Nguyen and Smeulders (2004) and core-set strategy. However, our strategy differs from tradi- tional strategies because ours incorporates human- annotated explanations for selection. More specif- ically, our data selector aims to choose examples that are representative of the unlabeled data pool in terms of average similarity to auman-annotated explanations of all previously-labeled data while maximizing the diversity of newly-selected data.
Figure 2:
Preliminary experiment result our dual- model system on c-SNLI (Camburu et al., 2018) dataset.
each unlabeled example and rank all the unlabeled data in terms of the average similarity score. To select the most representative data in the candidate pool while maximizing diversity, we choose exam- ples from the ranked data list with equal intervals. Note that in the first iteration, since previously annotated explanations are available, we compare the similarity between the data content.
Evaluation
We conduct the AL simulation experiment with the e-SNLI (Camburu et al., 2018) dataset. The pri- mary objective is to justify that our proposed dual- model framework, when combined with human- annotated explanations in AL data selection, can effectively identify more representative and helpful data from a reasonably large-scale dataset.
We assume that human-annotated explana- tions contribute significantly to the model's pre- diction and convey more information than the original data content alone. These explanations reveal underlying relationships between con- cepts in the data content and the relations between the data content and choices. For instance, in the e-SNLI dataset, the data content consists of the concatenation of hypothesis and premise sentences. Later, we construct a baseline selector in the AL simulation experiment (Sec. 4.2) with the same setup, except that it only compares the similarity between data content. Additionally, we include ran- dom baseline and probability- baseline strate- gies. Our results demonstrate that using human- annotated explanations for data selection consis- tently leads to improved prediction performance compared to using data content alone.
Given that e-SNLI dataset comprises a substan- tial 549, 347 examples in the train split, we per- formed a preliminary experiment to determine a reasonable number of candidate data for the AL simulation. This approach aims to save time and computing resources. Our goal is to identify an ideal candidate data size that would not introduce potentially biased feature distributions signifi- cantly degrade model performance when compared to fine-tuning on the full dataset. We employ the pre-trained bS-base (Raffel al., 2020) as the back- bone for all the experiments and provide the hyper- parameters in Appendix C.
4.1 Preliminary Experiment
Here we delve into the details of our data-based AL data selector (shown in Algorithm 1). For each unlabeled data instance, we use sentence- transformers (Reimers and Gurevych, 2019) to cal- culate the semantic similarity between its data con- tent and every previously annotated explanation. Then, we take the averaged similarity scores for
The expected outcome of the aforementioned pre- liminary experiment is 1) determine the upper bound of performance and observe how the per- formance of our dual-model system gradually de-
creases as we reduce the amount of training data, and 2) to identify a suitable candidate data size for

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the AL simulation.
random (Baseline)
Diversity based [Bascline]
We also randomly sample the same amount of data for each category in the preliminary experi- ment to minimize potential bias introduced by un- even distribution, especially when the sampling size per iteration is very small. Specifically, select eight different sampling amounts per cate-
Model Probrbility-based
Data Diversity [Ours)
0.6
Evaluation Accuracy
0.45
0.35
gory from the e-SNLI training split, ranging from [10, 50, 100, 500, 1500, 3000, 5000] and the com- plete data per category. Since the e-SNLI dataset consists of three categories: entailment, neutral, and contradiction, the total sampling size in each setting becomes [30, 150, 300, 1500, 4500, 9000, 15000, and 549,367 367 (full train split)], respectively, shown in Figure 2.
Iterations Active Learning Simulation
(a) Setting 1: examples per iteration 20 iterations
random (Baseline)
Diversity [Baseline]
-Model Probability-baee
Data Iversttybbared (Ouns)
Evaluation Accuracy
0.75
0.7
0.65
For each sampling setting, we conduct three tri-
als to obtain an averaged result. In each trial, we fine-tune the explanation generation model and the
prediction model once and conduct hyperparame- ter search. The framework is then evaluated on the test split of e-SNLI (9, 824 examples).
Iterations Active Learning Simulation
(b) Setting 2: 30 examples per itcration + 1,5 iterations
Figure Results of AL Simulation experiment on our Dual-model system with different data selectors.
The preliminary experiment results are shown in Figure 2, where the blue dot denotes the averaged prediction accuracy (in percentages) at each setting, and the red bar indicates the standard deviation of accuracy among three trials. Notably, with more than 1, 500 data per category, the performance drop compared to the full train split is inconspicuous (84.08% to 87.02%), while the standard deviation is below 0.5%. This observation indicates that us- ing I 1% of the original training data size only leads to performance drop of merely 3%. Additionally, we found that even with only 10 data points per category (30 data total), our system still achieves an average accuracy of 45%, although the deviation is relatively significant. Furthermore, when we ex- tend the training data size from 100 to 500 data per category (300 to 1500 in total), reasonably appli- cable setting in real-world scenarios, the accuracy reach over 80% accuracy, showing promising results considering that the amount of training data is much smaller than the size of evaluation samples.
formance for ours and the baseline data selectors at every iteration. During each trial, we start by randomly selecting 3, 000 examples per category from the complete train dataset, then use the same data conduct AL simulations with different data selectors in our dual-model framework. This way, we can ensure the performance differences during each trial are not due to different unlabeled data pools but to actual differences in the performance of the AL data selectors. For the evaluation, we randomly sample 300 examples per category (900 in total) from the test split of e-SNLI every trial and evaluate with the same test data after each iteration.
The AL simulation comprises two settings, where we simulate annotating 180 and 450 data instances, respectively. These two levels of data annotations reasonably mimic real-world scenarios where users have limited budgets, annotators, and data for annotation. Specifically, experiment with the following two settings:
4.2 Simulation Experiment: Evaluation Setup
1) For every iteration, select examples per cat- egory (9 in total) with 20 iterations, which results in 180 examples altogether;
Based on the findings from the preliminary exper- iment, we decide to use 3, 000 examples per cat- egory (9, 000 in total) as the candidate unlabeled data pool for the Active Learning simulation.
2) For every iteration, select 10 examples per cat-
egory (30 in total) with 15 iterations, which results in 450 examples altogether.
Inspired by the few-shot evaluation guid- ance (Bragg et al., 2021), we conduct 80 trials for each AL setting and calculate the averaged per-
Our AL simulation experiment involves our data selector, two baselines, and an additional model

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probability-based selector. Our data selector, de- scribed in Section 3.2, is a novel data diversity- based sampling strategy that leverages human- annotated explanations. For comparison, we use a random data selector as the basic benchmark and algorithm
Yes/ No Count
Label
Exp.
Exp. - Label
Trustworthy
Ground-ruth
83;7
86,4
87/3
78: 12
Durl-makl (ours)
64 64/36
68/22
48/42
35/55 55
SalFrtiioaliationn
42/48
67/23
51/39
21/69
Table 2: Human evaluation results.
another traditional data diversity-bast that shares the same procedures with ours, except that it only compares the similarity between each unlabeled data's ss content and the previously- data's 's content, not using the human- annottaed ex- planations. The probability-base ducts inference on unlabeled data and selects exam- ples with the least probability at every iteration. We fix the same set of hyperparameters (Appendix C).
SoTA few-shot explanation -eriiontinnnntioonn system, the celf-rationalization baseline (Marasovic et al., 2022), and the human ground-truth, we recruited three human participants to conduct a human eval- uation following the prior literature (Xu et al., 2022). The self-rationnlizzaiin baseline is T5- base model, which uses the same input template of our explanation euplanation-generationn model shown in Table but asks the model to generate both the label and explanation simultaneously.
selector con-
Worth noting that our data selector does not use task content in previously labeled examples; in- stead, exclusively rely
We leverage AL setting 1 described in Sec- tion 4.2 to fine-tune system with total of 180 examples over 20 epochs and use the same 180 examples to fine-tune the ratooaattoiiiioaioonn baseline. Both systems are used to infer the com- plete test split of e-SNLI after fine-tuning: then, we randomly sample 80 examples for the human study,
ex- planations demonstrate their greater utility com- pared task content. In the first iteration, both ours and the data diversity baseline perform identically because no previously annotated data available.
4.3 Simulation Experiment: Result
The AL simulation results are presented in Fig- ure 3. To explain the diagrams in detail, each dot is the average accuracy on 80 trials at every iteration for each data selector. The wreenyyeloo//re/bblle dots denote data sclector/data diversity- based baseline/random selector/model probability-ban selector, respectively. We observe that our data selector consistently maintains an advantage over the traditional kltt-bbsed sampling baseline, while the traditional one consistently beats the random baseline by a significant margin. Additionally, we observe that the model probability-base selector outperforms the random baseline in both settings. data selector outperforms
For each data instance, the rater is presented with the textual content of the premise and hypothesis of the original data paired with three sets of labels and explanations from our system, baseline system, and the SNLI dataset. Participants who are not aware of the source of each label-eyy -xpllanatinn pair are asked to answer four questions with [Yes/No]:
ground-truth from the e-
1) Is the Prediction correct?
2) Is the Explanation itself a correct statement?
3) Regardless of whether the Al Prediction and Explanation
the Explanation help you to understand why Al has such Prediction?
correct
To summarize, both baselines in every iteration for both AL set- tings, indicating that using human-annotated ex- planations in the data selector with our dual- model AL framework is more beneficial than using the data content alone. Even with only 180 and 450 data to be annotated in each setting, system can achieve 55% and 72% accuracy on average, respectively. We anticipate that our exper- iment will reach similar performance around 85% as shown in Figure 2 but converge much faster than the random selector if we continue the AL process.
4) Will you trust this Al real-world decision- making?
To ensure inter-coder consistency, we first con- duct a 30-min tutorial session to educate all three participants with ten examples to build a consen- sus among them. In the actual experiment, each of the three participants is then asked to rate 30 data instances (20 unique ones and 10 shared ones), which make up total of 70 data instances, and 360 ratings rater*30 instances* questions). We first calculated the Inter-Rater Reliability score (IRR) among them for each of the four questions. With the IRR score of (Q1: 1, Q2: 0.89, Q3: 0.98, Q4: 0.87), we are confident that the three coders have the same criteria for further result analysis. Our questions all have binary responses, and we
4.4 Human Evaluation Setup and Results
To qualitatively evaluate the explainability of the generated explanations from our system against

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rely on Chi-square analysis (Elliott and Woodward, 2007) to examine the statistical significance of the rating groups' differences. As shown Table 2, the participants rated human ground-trun explanations highest across all four dimensions. Between our system and the few-shot self-rationalization system (baseline), participants believe our systems' pre- dicted labels are more likely to be correct, with 64 'valid' ratings out of 90 for our system versus 42 out of 90 ratings for the baseline. Chi-square test indicates such a difference statistically significant (12 = 21.61, p < 0.01).
random (Baseline)
Data Diversity based (Baseline)
Data Diversity-bseed (Ouns
0.45
Evaluation Accuracy
0.4
0.35
0.25
16
Iterations Active Learning Simulation
(a) Setting 1: examples per iteration 20 iterations
random (Baseline)
Data Diversity based (Baseine)
Datta Diversity-baee based (Ours]
When asked whether they would trust the Al if there were such Al systems supporting their real- world drecision-makinge 35 out of 90 answered 'Yes'
0.45
Evaluation Accuracy
0.4
0.35
for our system, and it is significantly better than the baseline system (21 'Yes' out of 90) (x2 p p < 0.01). In comparison, 78 out of 90 times people voted that they would trust the human- annotated explanation's 's quality.
0.25
11
Iterations Active Learning Simulation
(b) Setting 2: 30 examples per itcration + 1,5 iterations
As for Question 2 ("the validity of the generated explanation") and Question 3 ("whether the gener- ated explanation is supporting its prediction"", the human evaluation fails to suggest statistically mean- ingful results between our system and the baseline system (1) 0.06, p 0.89 for explanation validity, and (1) 0.41, p 0.52 for explana- tion supporting prediction). In summary, human participants believe our system can outperform the baseline system on the label prediction's 's quality and the trustworthiness of AI dimensions. Still, there is large space to improve as human evalua- tors believe the ground-truth label and explanation quality is much better than either Al system.
Figure 4: Ablation study results of AL simulation periment on our Dual-model system with different data selectors on ECQA dataset.
ing AL simulation. We fine-tune the explanation- generation models on e-SNLI with the same two settings in the previous experiment, average the re- sult on 15 trials of experiments, and keep consistent with every other hyper-paranen parameters.
The ablation results are shown in Figure of Ap- pendix B. The blue/red lines denote the explanation- generation model is fine-tuned on e-SNLI with each setting in Section 4.2 correspondingly. We observe that the explanation-generatl provided helpful explanations, leading to an im- provement in the system's 's prediction performance, with accuracy reaching more than 65%. In addi- tion, the explanation-generatio model fine-tuned on more data can perform better, suggesting that it had learned to generate more helpful explanations.
model consistently
4.5 Ablation Study 1: Transfer to Multi-NLI
We conduct an ablation study with transfer learn- ing through AL simulation from e-SNLI to Multi- NLI (Williams et al., 2018). This study explores whether the poolanatioo-generatinn model trained e-SNLI is helpful for AL a similar task.
4.6 Ablation Study 2: Our AL Framework on ECQA
The transigr ablation study consists of the following steps: 1) fine-tune an explanation- generation model using our AL framework on the e- SNLI dataset; 2) freeze the explanation explanation-gemeration model and use it to generate explanations in the AL simulation for Multi-NLI: 3) fine-tune the predic- tion model for Multi-NLI at every iteration. Unlike the e-SNLI experiment, our AL data selection algo- rithm will use model moder-generated explanations to se- lect examples at every iteration in the transfer learn-
We additionally conduct an AL Simulation experi- ment on ECQA (Aggarwal et al., 2021) (a recent dataset extends the CommonsenseQA dataset with high-quality human-annotated explanations) with our data selector, random baseline, and similarity- based baseline that does not use explanations. We comply with the same experiment settings for the e- SNLI AL simulations described in Section 4.2. The

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fap, teachrork
on e-SNLI and ECQA datasets explore whether can further reduce human annotation efforts.
Accuraty
The results are presented in Figure 5, where horizontal dotted line represents the benchmark of the explanation generation model fine-tuned on human-annotated explanations in Section 4.3 and 4.6. The LLM-AL framework significantly out- performs the explanation generation model guided by human annotation in both Active Learning set- tings. However, hypothesize the LLM's planation generation capability can vary from task task. It may be highly efficient in relatively easy tasks, such as e-SNLI and ECQA datasets, both of which are training datasets for FLAN-T5. Yet, LLMs may struggle to provide helpful explana- tions in complex real-world domain Nomain-specific tasks, where human experts' feedback may still be neces- sary and preferred. This leads to another potential avenue for future work: exploring the capability and limitations of leveraging LLMs for explanation generation real-world scenarios.
03
# of Itarations In Active Leaminn Simulation
(a) e-SNLI Dataset
Setting 11 Hamon txp. tenchwork
Setting F5MM-1 for Geseration
06
Evaluation Accuracc
of Iterations Active Learnnng Simulation
(h) FCQA Dataset
Figure 5: Ablation study results of AL simulation ex- periment with FLAN-T5-XL for explanation (exp.) gen- eration in our Dual-model framework compared with best annottted explanations on e-SNLI (top) and ECQA (bottom) datasets.
Conclusion and Future Work
In summary, this paper introduces a novel dual- model AL system designed to address the com- real-world need for domain experts to provide both classification labels and natural language planations. Our system comprises purpose-buill data diversity- AL example selector and lequence-to-sequene language models, one for explanation generation and the other for label pre- diction. Through an AL simulation evaluation and human assessment of the e-SNLI dataset, our re- sults demonstrate the effectiveness of explanations in AL sampling with system. They consistently outperform both baselines, and the explanations generated by our system are preferred over a state- of-the-art poolanation-generatte
results are shown in Figure 4, where our proposed data selection strategy can consistently outperform both baselines in both simulation settings. Interest- ingly, the similarity- based baseline performs sim- ilarly to the random baseline, which could be be- cause using data content alone is not sufficient to select more helpful and representative examples while using annan-annotated explanations can fa- cilitate better data selection consistently.
4.7 Ablation Study 3: LLM for Explanation Generation
The recent prevalence of Instructional-ineture large language models (LLMs) (Wei et al., 2021; Chowdhery al., 2022; Ouyang et al., 2022) with exceptional generation capabilities off-the-sheef en- abled a straightforward idea upon our dual-model framework: can LLMs generate natural lan- guage explanations that are on par or even of higher quality than human-annotated ones, to facilitate the prediction model fine-tuning pro- cess? We conduct ablation experiments leverage FLAN-T5-XL (Chung al., 2022) for explanation generation our framework substitute the T5 model fine-tuned on human explanations (LLM- AL, hereinafter). We conduct the AL simulations
system.
Our work lays a step-stone towards a human- centered interactive Al solution (it can easily implemented as an interactive system as illustrated in Fig 7 in Appendix D) that supports domain ex- perts for their data annotation tasks. Many real- world tasks still require domain experts to review and annotate each data instance with decision and an explanation for accountability purposes (e.g., a lawyer reviewing and signing off a legal doc- ument). We invite fellow researchers to join us in advancing this research direction, essential for supporting this prevalent real-world requirement.

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6 Limitations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018, e-SNLI Nat- ural language inference with natural language expla- nations. Advances in Neural Information Processing Systems, 31.
In this paper, we demonstrate the effectiveness of our framework on a representative large-scale classification dataset (e-SNLI,, but there are many other NLP tasks, such as question answering and commonsense reasoning. The generalizability of our system other NLP tasks remains unexplored. Another limitation is that this work proposed data diversity-basee AL selector design. We benchmark it with a traditional data diversity-baad -ased selector as well as a model probability- design to demon- strate the usefulness of explanations. Prior litera- ture has proposed other designs, such as ensemble approaches, which are not evaluated in this paper.
Samuel Carton, Anirudh Rathore, and Chenhao Tan. 2020. Evaluating and characterizing human ratio- nales. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9294 9307, Online. Association for Computational Linguistics.
Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang. 2021. Generate natural language planations for recommendation. arXiv preprint arXiv:2101.03392.
Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022. Can rationalization improve ro- bustness? In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, pages 3792-3805, Seattle, United States. Association for Computational Linguisties.
Acknowledgements
This work was supported by the Rensselaer-IBM AI Research Collaboration (http:/airc.rri.eddu) which is part of the IBM AI Horizons Network http://bbmbbiz/AHHorrzzoo
Michael Chmielewski and Sarah C Kucker. 2020. An mturk crisis? shifts in data quality and the impact on study results. Social Psychological and Personality Science, 144::4644473.
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Appendix
Voce RO vainerr IWnel
Mivie www..nmmiiiiin.oo 451
025055
A e-SNLI Examples
051
E20
0.5
Table 3 illustrates an example data of each category in the e-SNLI dataset. Every data instance con- tains premise and hypothesis along with human annotated label and free-form explanation.
Avis
0.
020
0.45 2200.5
0.5
DUT
20
0.36
This church choir sings to the masses as they sing joyous songs from the book at church.
Premise:
Itrabora
(a) Active Learning on Mulit-NLI using explanation-
Hypothesis: The church is filled with song. Label: entailment
generation model from e-SNLI with Setting
Human-annotater explanation: "Filled with song" is rephrasing of the "choir sings the masses.
Vicul PG heired 100
Modi HN tuaned USALL
054 40.0000
Up
Premise: A man playing an electric guitar on stage.
031
Hypothesis: man is performing for cash. Label: neurral
0.12
0.51
3.1
0:00
Humannannotated explanation: It is unknown if the man is performing for cash.
0.5
0.0
0.42 DAD0
237
0.50
0/0
0.4
0.35
0:11
220
Premise: A couple walk hand in hand down street.
0.39
Hypothesis: A couple is sitting bench.
03
Label: contradiction
Human-annotated explanation: The couple cannot be walking and sitting the time.
Becodoss
(h) Active Learning on Mulit-NI. using explanation- generation model from e-SNLI with Setting
Table Sample data each category e-SNLI (Cam- buru al., 2018) dataset.
Figure 6: Results of Transfer Learning Ablation Study of AL Simulation experiment on our Qual-modell system from e-SNLI Multi-NLI. Setting 1/2 refers the settings for Active Learning Simulation in Section 4.2.
B Transfer Learning Ablation Study Diagrams
Figure 6 shows the results of our Ablation Study results described in Section 4.5. The explanation- generation model is fine-tuned from AL on e-SNLI dataset with two different AL settings, then we freeze the oellanation-generatiinn model to train the prediction model AL simulation for Multi- NLI dataset under two settings. Setting 1/2 refers to the settings for Active Learning Simulation in Section 4.2.
of parammtarsesters is: batch_size _are_per_____ 2: learning_rat
size__errGGUU GPU le input_max__eeeee 64
512; sevee_max_leete
We conduct a hyper-parameter search for the number of fine-tuning epochs for each amount sampled examples, details are shown in Table 4.
of train data category total
epoch Mgu
epoch for Mp
10/30
25
100
C System Environment and Hyper-Parameters
50 150
25
250
100/ 300
10
250
500 / 1500
The computing resource all the experiments we conducted this paper has 128 Gigabytes of RAM.
1500 / 4500
5
50
3000 / 9000
5000 / 15000
5
25
In addition, we use 2 NVIDIA Tesla V100 GPU for the preliminary experiment and 8 NVIDIA Tesla v100 GPU for the AL simulation experiment.
Full
Table 4: Fine-tuning epochs of each model in dual- model system with different data amount settings.
C.1 Preliminary Experiment
For the Preliminary experiment described in Sec- tion 4.1, we leverage the same set of fine- tuning hyper-parameters other than the num- ber of fine-tuning epochs for the explanation- generation model (denotes as MEG) and the pre- diction model (denotes as Mp). The same set
C.2 AL Simulation Experiment
For both of the AL Simulation settings we ex- perimented in Section 4.2, we leverage the same set of hyper-parameters for fine-tuning our dual- model AL system: batch_size sirti_siieeee______ PPU =

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2; learning_rad raate le MMG bire_train_epece
20, Mp_trar fr_train__ee "rain_epoch 250; input aati_max__ee lengghhhthhhh
512; target_max_lenggle = 64
Proposal for an Interactive System
Our proposed dual-model system can be easily im- plemented interactive human-centered Al sys- for supporting domain experts and human notators in labeling both labels and explanations.
Mldell
Systen
Assoraded Data
Learingg
Sckriee
Ansoutee
Areokied
Human Annotatior
Explansties #odd!
festenation
#ode
Libel
Figure 7: Our proposed dual-model system can be im- plemented as an interactive AL-based data annotation system to speed up users' annotation productivity, Such system can simply have an interface with four output functions (i.e.. display unlabeled data, display AL se- lected data, display -xplaattdd-xpllaatioon. and display predicted labeled) and one input function (i.e., annotate label and explanation the unlabeled data.


Reviews:
[{"review_id": "7CLgKpmcTa", "reviewer": "Reviewer_LfJH", "summary": "", "questions": "1. The paper doesn't have a result table showing the comparison of different pre-trained models on the unlabeled data sampled.\n2. Also, the paper doesn't have multiple unlabeled data selection techniques. How they performed.", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank your review and feedback on our paper. All concerns raised will be fully addressed in the camera-ready version, such as adding the suggested references, improving the presentation for results, improving the writing style, eliminating misunderstandings, and fixing typos. \n\nWe would like to clarify a few misunderstandings regarding our contributions and novelty. The misunderstanding could possibly be due to our narrative, and we will revise the paper narrative to make our claims and statements precise and clear in the final camera-ready version:\n\n> “Paper uses a pre-trained T5 base model fine-tuned on small human-curated data +unlabeled data(labeled via similarity as mentioned in 1) to generate explanations and the corresponding labels for the data. The defined technique doesn't seem to be novel. “\n>\n> “Paper uses a simple way of selecting unlabeled data which is very similar to human-annotated data via calculating similarity with the help of sentence transformer embeddings.”\n\nOne of our primary contributions is to **propose a novel Active Learning similarity-based data selector that leverages human-annotated explanations, and we justify the use of explanations to help better select representative and helpful examples**. The hypothesis behind our proposed Active Learning data selector is that we assume the human-annotated explanations can convey more helpful and richer information than the original data content alone, which can facilitate better selecting representative and helpful examples compared with using data content alone (highlighted in Section 3.2). \n\nWe showed the importance of human-annotated explanations in selecting representative samples which was previously overlooked, and there were no techniques in the past that showed how to use these explanations in selecting the Active Learning examples. Here we showed one technique (a similarity-based strategy) that is simple but shows promising results on different NLP tasks.\n\nWe justified our hypothesis through the Active Learning Simulation, where the similarity-based baseline follows the same similarity calculation algorithm but only uses data content. We additionally implement a model probability-based baseline selector because these two baselines represent the primary types of traditional data selectors. We justify the effectiveness and helpfulness of using natural language explanations in data selection because our proposed data selector can consistently outperform both baselines at every iteration in two active learning simulation settings (Figure 3).\n\nIn addition, we are the first to propose this novel active learning framework, including the novel AL data selection strategy and the dual-model architecture, to the best of our knowledge.\nOur proposed framework provides a substantial contribution to the community that better incorporates human natural language feedback for enhancing data selection, model finetuning, and model explainability while reducing annotation effort.\n\nBased on our results and contributions, we justified our hypothesis that natural language explanations are indeed helpful in active learning data selection, and we broad up the research space of further exploring better generating and use of natural language explanation in active learning data selectors, which will be another full of work we are planning to explore in the future. \n\n\n> “The paper doesn't have a result table showing the comparison of different pre-trained models on the unlabeled data sampled. “\n>\n> “Also, the paper doesn't have multiple unlabeled data selection techniques. How they performed.”\n\nWe appreciate you bringing up some related data selection strategies, and we will add these papers as references. To clarify, we are aware that there are a lot of different data selection strategies, and there does not exist one that is better than any others, because the effectiveness of different strategies can vary from task to task. In this paper, we include two different types of data selector baselines in our Active Learning simulation (data diversity-based and model probability-based), which are two primary types of traditional data selectors. \n\nIn this work, one of our primary contributions is to justify the effectiveness of incorporating natural language explanations in active learning data selection, compared with traditional AL data selection strategies that do not take this type of human feedback into consideration. Given our hypothesis regarding natural language explanations above, our similarity-based selection strategy can distinguish those instances that may look similar (e.g., have a close similarity score by comparing data content alone) but need different rationales to solve, where the traditional strategy falls short. We justified our hypothesis through the AL simulation by comparing it with the traditional similarity-based strategy and the model probability-based strategy without explanations (plotted in Figure 3). \n\nSpecifically, we focus on demonstrating the effectiveness of incorporating human-annotated natural language explanations into data selection with a data similarity-based strategy and compare with traditional data similarity-based and model probability-based strategy that does not use such human feedback. We are proposing a new data selector for a more real-world and new scenario, where human domain experts need to label both labels and explanations. We provide the rationales for focusing on the data diversity-based selection strategy in Section 3.2 that the model probability-based approaches highly depend on the models and can not faithfully justify the effectiveness of using natural language explanations, but the data similarity-based strategy is purely based on the data features and does not rely on model architecture inherently. Thus, the experiment across different models is less relevant to our proposed methodology, and we do not include such an experiment in this paper. It is worth mentioning that we did implement a probability-based strategy as a baseline to compare the effectiveness of our strategy.\n\nWe agree it will be an interesting and important idea of future work to comprehensively compare a large number of existing active learning data selection strategies on a variety of different tasks, which will consist of a large amount of work alone and will not fit in our current work.\n\nAdditionally, we conduct the Active Learning SImulation experiment on ECQA (a recent dataset extends CommonsenseQA dataset with high-quality human-annotated explanations) with our data selector, random baseline and similarity-based baseline that does not use explanations. The results are shown below (we will add the tables in the Appendix section to visualize the clear gain), where our proposed data selection strategy can consistently outperform both baselines in both simulation settings. Interestingly the similarity-based baseline perform similarly as the random baseline, which could because using data content alone is not sufficient to select more helpful and representative examples, while using human-annotated explanations can facilitate better data selection consistently.\n\n30 examples per iteration and 15 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.26|0.30|0.31|0.34|0.36|0.37|0.37|0.38|0.38|0.39|0.40|0.40|0.41|0.42|0.41|\n|Similarity Baseline|0.27|0.29|0.32|0.33|0.35|0.36|0.37|0.38|0.39|0.39|0.40|0.40|0.41|0.41|0.41|\n|Exp. Similarity (ours)|**0.28**|**0.31**|**0.33**|**0.35**|**0.37**|**0.38**|**0.39**|**0.39**|**0.40**|**0.41**|**0.42**|**0.42**|**0.43**|**0.43**|**0.44**|\n\n\n9 examples per iteration and 20 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.23|0.25|0.26|0.27|0.28|0.29|0.30|0.30|0.31|0.32|0.33|0.34|0.34|0.35|0.35|0.35|0.36|0.36|0.37|0.37|\n|Similarity Baseline|0.24|0.25|0.26|0.27|0.28|0.30|0.31|0.31|0.32|0.33|0.33|0.33|0.34|0.33|0.35|0.35|0.34|0.35|0.36|0.36|\n|Exp. Similarity (ours)|**0.25**|**0.26**|**0.28**|**0.30**|**0.30**|**0.31**|**0.31**|**0.31**|**0.32**|**0.33**|**0.34**|**0.34**|**0.35**|**0.35**|**0.35**|**0.36**|**0.37**|**0.38**|**0.38**|**0.39**|\n\nWe believe this approach can generalize to other datasets because the abovementioned hypothesis and additional results. In addition, our proposed data selection strategy should be inherently both model-agnostic and task-agnostic.\n\n> Missing References\n\nWe appreciate you bringing up these related data selection strategies, and we will add these papers [1][2][3] as references in the camera-ready version if this paper gets accepted.\n\n[1] Incremental Learning with Unlabeled Data in the Wild. Lee et. al(CVPR 2019) \n\n[2] Negative sampling in semi-supervised learning. Chen et al.(ICML 2020) \n\n[3] Label Efficient Semi-Supervised Learning via Graph Filtering. Li et al.(CVPR 2019)", "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 are:\n1. It proposes a dual model that is an explanation-generation model guided by human-provided explanations\n2. a prediction model that takes the data text and explanations generated by the explanation generation model for prediction.\n3. An AL(Active Learning) data selector that selects a set of representative examples in terms of semantic similarity between each unlabeled data text and labeled data’s human explanations.\n4. The paper proposes a novel data diversity-based AL(Active Learning) data selection strategy to exploit the explanation annotation.", "reasons_to_reject": "Reasons to reject:\n1. Paper uses a simple way of selecting unlabeled data which is very similar to human-annotated data via calculating similarity with the help of sentence transformer embeddings.\n2. Paper uses a pre-trained T5 base model fine-tuned on small human-curated data +unlabeled data(labeled via similarity as mentioned in 1) to generate explanations and the corresponding labels for the data. The defined technique doesn't seem to be novel.\n3.  There are multiple data selection strategies from unlabeled data which the author hasn't compared\n eg: Incremental Learning with Unlabeled Data in the Wild.   Lee et. al(CVPR 2019)\nNegative sampling in semi-supervised learning.       Chen et al.(ICML 2020)\nLabel Efficient Semi-Supervised Learning via Graph Filtering.       Li et al.(CVPR 2019)\n4. Missing results table so as to see how different pre-trained models and data labelling techniques performed.", "questions_to_authors": "1. The paper doesn't have a result table showing the comparison of different pre-trained models on the unlabeled data sampled.\n2. Also, the paper doesn't have multiple unlabeled data selection techniques. How they performed.", "paper_topic_and_main_contributions": "The paper proposes a novel AL(Active Learning) architecture to support experts who need to annotate both labels and explanations in low-resource scenarios. The architecture incorporates an explanation-generation model that can utilize human-annotated explanations and generate such explanations. It uses publicly available T5 model to generate explanations and the labels correspondingly for the data.", "excitement": 3, "reproducibility": 3, "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": {"Missing_References": "References related to multiple works done in Active Learning unlabeled data selection and labeling it to tune models are missing.\nFor example:\nIncremental Learning with Unlabeled Data in the Wild.   Lee et. al(CVPR 2019)\nNegative sampling in semi-supervised learning.       Chen et al.(ICML 2020)\nLabel Efficient Semi-Supervised Learning via Graph Filtering.       Li et al.(CVPR 2019)"}}, {"review_id": "3L2mhRVesX", "reviewer": "Reviewer_BmAS", "summary": "", "questions": "- Despite the experimental effectiveness, why does it make sense to compare similarities between data instances and natural language explanations directly? Do you expect this approach to generalize to other datasets?", "limitations": "", "rating": 2, "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 feel encouraged that you recognize our paper’s contribution to the NLP community, specifically for the active learning techniques, and sincerely thank the constructive feedback. All concerns raised will be fully addressed in the camera-ready version, such as adding the suggested references, improving the presentation for plots and results, improving the writing style, and fixing typos. \n\nWe would like to address every comment and question below:\n\n> “The effectiveness of the approach is only shown on e-SNLI. The authors could use non-NLI datasets and human annotators to further demonstrate the generality of the method.”\n\nWe totally agree with you. However, there exist very few datasets with high-quality human-annotated natural language explanations, and some of the existing datasets have been criticized for low-quality human explanation annotations, such as CoS-E and ComVE. \nIn this paper, we primarily conduct the experiment on NLI tasks, with e-SNLI and transfer-learning on Multi-NLI, so that the results are more faithful and reliable. \n\n\nAdditionally, we conduct the Active Learning SImulation experiment on ECQA (a recent dataset extends CommonsenseQA dataset with high-quality human-annotated explanations) with our data selector, random baseline and similarity-based baseline that does not use explanations. The results are shown below (we will convert it to the diagram and add to the paper), where our proposed data selection strategy consistently outperform both baselines in both simulation settings. Interestingly the similarity-based baseline perform similarly as the random baseline, which could because using data content alone is not sufficient to select more helpful and representative examples, while using human-annotated explanations can facilitate better data selection consistently.\n\n30 examples per iteration and 15 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.26|0.30|0.31|0.34|0.36|0.37|0.37|0.38|0.38|0.39|0.40|0.40|0.41|0.42|0.41|\n|Similarity Baseline|0.27|0.29|0.32|0.33|0.35|0.36|0.37|0.38|0.39|0.39|0.40|0.40|0.41|0.41|0.41|\n|Exp. Similarity (ours)|**0.28**|**0.31**|**0.33**|**0.35**|**0.37**|**0.38**|**0.39**|**0.39**|**0.40**|**0.41**|**0.42**|v0.42**|**0.43**|**0.43**|**0.44**|\n\n\n9 examples per iteration and 20 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.23|0.25|0.26|0.27|0.28|0.29|0.30|0.30|0.31|0.32|0.33|0.34|0.34|0.35|0.35|0.35|0.36|0.36|0.37|0.37|\n|Similarity Baseline|0.24|0.25|0.26|0.27|0.28|0.30|0.31|0.31|0.32|0.33|0.33|0.33|0.34|0.33|0.35|0.35|0.34|0.35|0.36|0.36|\n|Exp. Similarity (ours)|**0.25**|**0.26**|**0.28**|**0.30**|**0.30**|**0.31**|**0.31**|**0.31**|**0.32**|**0.33**|**0.34**|**0.34**|**0.35**|**0.35**|**0.35**|**0.36**|**0.37**|**0.38**|**0.38**|**0.39**|\n\nWe believe this approach can generalize to other datasets because the abovementioned hypothesis and additional results. In addition, our proposed data selection strategy should be inherently both model-agnostic and task-agnostic.\n\nWe also plan to extend our framework to other NLP tasks and specific real-world tasks in the future, such as recruiting human experts for explanation annotation in specialized domains and experimenting with our proposed methodology.\n\n\n> ”Introducing natural language explanations into an active learning setup substantially increases the cost of data annotation, because annotators would use more time than when asked to provide labels alone. How would the proposed approach perform against a random selection baseline (without explanations) with equal budget for annotation?”\n\nWe agree this is an important and realistic topic about the tradeoff between gathering more varieties of human feedback and reducing the annotation cost while talking about deploying an active learning framework in real-world scenarios. From our perspective, we believe there is no gold standard answer, and it highly depends on the quality of annotated feedback (e.g., natural language explanations) and tasks. We believe the cost (time, labor, money, etc.) of additional annotation needed with our framework is reasonable and manageable with the help of active learning, where we are aiming to use as a minimum amount of annotated data as possible.\n\nWe would also like to emphasize three important advantages of our proposed framework and collecting human-annotated explanations in real-world scenarios:\n1. There exists plenty of real-world scenarios that need not only label annotations but also explanation annotations (so the cost of annotation is already paid and the explanations are necessary), such as doctors need to write down explanations for diagnosis and clinical decisions but today's label-only active learning frameworks don't support such scenarios and not make use of the explanation annotations.\n2. By asking annotators to explicitly write down their reasoning process (e.g., explanations), they will form a clear chain of reasoning in their mind to verify the label annotation, so this could improve the quality of label annotations\n3. Compared with traditional end-to-end training frameworks, our framework can provide faithful natural language explanations that explicitly contribute to model prediction and are easy for humans to understand and trust model predictions. \n\nIn addition, we conduct experiments to leverage a freezed FLAN-T5-XL, state-of-the-art large language model (LLM) for explanation generation in the e-SNLI dataset, where we could further reduce the need for human explanations. The LLM-AL framework can further outperform the explanation generation model guided by human annotation in both Active Learning settings, we can include the result in this paper. However, the LLM’s explanation generation capability can vary from task to task, where it could be very efficient in relatively easy tasks, but may fail to provide helpful explanations in difficult domain-specific tasks, and human experts’ feedback may still be needed and preferred. It can lead to another line of future work to explore the capability and limitations of leveraging LLMs for explanation generation in real active learning scenarios, which does not fit into our paper. \n\n\n> “Despite the experimental effectiveness, why does it make sense to compare similarities between data instances and natural language explanations directly? Do you expect this approach to generalize to other datasets?”\n\nWe hypothesize that natural language explanations contain more helpful and informative content compared with the data content alone, as mentioned in Section 3.2. As a result, our similarity-based selection strategy can distinguish those instances that may look similar (e.g., have a close similarity score by comparing data content alone) but need different rationales to solve, where the traditional strategy falls short. We justified this hypothesis on e-SNLI and the effectiveness of our framework for transfer learning toward Multi-NLI.\n\nWe expect this approach can generalize to other datasets because the abovementioned hypothesis and the proposed data selection strategy should be both model-agnostic and task-agnostic.\n\n\n> “Overall I find the writing style too verbose, which makes the paper difficult to read.”\n\nWe will revise the paper narrative to make our claims and statements precise and clear in the final camera-ready version, to avoid any potential misunderstanding and verbal feelings. \n\n\n> Missing References\n\nWe are truly thankful that you brought up these related works, and we will add these papers [1][2][3] as references in the camera-ready version if this paper gets accepted.\n\n[1] Schick, Timo, and Hinrich Schütze. \"Exploiting cloze questions for few shot text classification and natural language inference.\" arXiv preprint arXiv:2001.07676 (2020).\n\n[2] Gao, Tianyu, Adam Fisch, and Danqi Chen. \"Making pre-trained language models better few-shot learners.\" arXiv preprint arXiv:2012.15723 (2020).\n\n[3] Zhou, Yangqiaoyu, Yiming Zhang, and Chenhao Tan. \"FLamE: Few-shot Learning from Natural Language Explanations.\" arXiv preprint arXiv:2306.08042 (2023).", "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": "- While the idea of generating natural language explanations to assist model prediction isn't new, using it in guiding AL data selection is creative.\n- The experiment design is solid, and shows that the proposed diversity-based data selection strategy outperforms other baselines.", "reasons_to_reject": "- The effectiveness of the approach is only shown on e-SNLI. The authors could use non-NLI datasets and human annotators to further demonstrate the generality of the method.\n- Introducing natural language explanations into an active learning setup substantially increases the cost of data annotation, because annotators would use more time than when asked to provide labels alone. How would the proposed approach perform against a random selection baseline (without explanations) with equal budget for annotation?", "questions_to_authors": "- Despite the experimental effectiveness, why does it make sense to compare similarities between data instances and natural language explanations directly? Do you expect this approach to generalize to other datasets?", "paper_topic_and_main_contributions": "This paper is about incorporating natural language explanations into an active learning framework.\n\nThe main contributions of the paper is combining a dual-model architecture (an explanation generator and a classifier) with a data-diversity-based active learning heuristic. The authors demonstrate the effectiveness of this approach on e-SNLI over baselines.", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Overall I find the writing style too verbose, which makes the paper difficult to read."}, "extra_text": {"Missing_References": "*Using templates for classification*\n\n[Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://aclanthology.org/2021.eacl-main.20/)\n\n[Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295)\n\n*Templated explanation generation for classification with explanations*\n\n[FLamE: Few-shot Learning from Natural Language Explanations](https://aclanthology.org/2023.acl-long.372)"}}, {"review_id": "0Ihljpaut2", "reviewer": "Reviewer_DYu3", "summary": "", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "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 feel encouraged that you recognize our paper’s contribution to the NLP community, specifically for the active learning techniques, and sincerely thank the positive feedback. All concerns raised will be fully addressed in the camera-ready version, such as adding results, improving the presentation for plots and narratives, and fixing typos. \n\n\n> ”The AL setting is only verified on the NLI problems. It's unknown whether it can generalize further to other types of problems. Note that this paper has already included an experiment that transfers from e-SNLI to MNLI.”\n\nWe hypothesize that natural language explanations contain more helpful and informative content compared with the data content alone, as mentioned in Section 3.2. As a result, our similarity-based selection strategy can distinguish those instances that may look similar (e.g., have a close similarity score by comparing data content alone) but need different rationales to solve, where the traditional strategy falls short. We justified this hypothesis on e-SNLI and the effectiveness of our framework for transfer learning toward Multi-NLI.\n\nWe would like to point out that there exist very few datasets with high-quality human-annotated natural language explanations, and some of the existing datasets have been criticized for low-quality human explanation annotations, such as CoS-E and ComVE, so the Active Learning experiment on these datasets will not be faithful or reliable. \n\n\nAdditionally, we conduct the Active Learning SImulation experiment on ECQA (a recent dataset extends CommonsenseQA dataset with high-quality human-annotated explanations) with our data selector, random baseline and similarity-based baseline that does not use explanations. The results are shown below (we will convert it to the diagram and add to the paper), where our proposed data selection strategy can consistently outperform both baselines in both simulation settings. Interestingly the similarity-based baseline perform similarly as the random baseline, which could because using data content alone is not sufficient to select more helpful and representative examples, while using human-annotated explanations can facilitate better data selection consistently.\n\n30 examples per iteration and 15 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.26|0.30|0.31|0.34|0.36|0.37|0.37|0.38|0.38|0.39|0.40|0.40|0.41|0.42|0.41|\n|Similarity Baseline|0.27|0.29|0.32|0.33|0.35|0.36|0.37|0.38|0.39|0.39|0.40|0.40|0.41|0.41|0.41|\n|Exp. Similarity (ours)|**0.28**|**0.31**|**0.33**|**0.35**|**0.37**|**0.38**|**0.39**|**0.39**|**0.40**|**0.41**|**0.42**|v0.42**|**0.43**|**0.43**|**0.44**|\n\n\n9 examples per iteration and 20 iterations altogether\n|Data Selection Strategy|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|\n|--------|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n|Random|0.23|0.25|0.26|0.27|0.28|0.29|0.30|0.30|0.31|0.32|0.33|0.34|0.34|0.35|0.35|0.35|0.36|0.36|0.37|0.37|\n|Similarity Baseline|0.24|0.25|0.26|0.27|0.28|0.30|0.31|0.31|0.32|0.33|0.33|0.33|0.34|0.33|0.35|0.35|0.34|0.35|0.36|0.36|\n|Exp. Similarity (ours)|**0.25**|**0.26**|**0.28**|**0.30**|**0.30**|**0.31**|**0.31**|**0.31**|**0.32**|**0.33**|**0.34**|**0.34**|**0.35**|**0.35**|**0.35**|**0.36**|**0.37**|**0.38**|**0.38**|**0.39**|\n\n\nWe believe this approach can generalize to other datasets because the abovementioned hypothesis and additional results. In addition, our proposed data selection strategy should be inherently both model-agnostic and task-agnostic.\n\n\n> “Figure 2: the standard deviation is usually plotted as error bars or regions, rather than a standalone line on the figure.”\n\nThank you very much for the suggestion. We will adjust the standard deviation of Figure 2 into error bars in the camera-ready version if this paper gets accepted.", "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 use of AL in the annotation of explanation and label is an important application.  \n- The proposed AL framework outperforms the baselines.  \n- Additionally, this paper further stuides the efficacy of the explanations generated by the AL system.  \n- This paper mentions the related literature comprehensively.", "reasons_to_reject": "The AL setting is only verified on the NLI problems. It's unknown whether it can generalize further to other types of problems. Note that this paper has already included an experiment that transfers from e-SNLI to MNLI.", "questions_to_authors": "N/A", "paper_topic_and_main_contributions": "This paper considers the low-resource annotation setting, and proposes a novel active learning (AL) architecture to support experts who need to annotate both labels and explanations in low-resource scenarios.\n\nThe dual-model AL system consists of an explanation generation model and a prediction model. This system first collects a few human-provided labels and explanations. The explanations are used to fine-tune the generation model, which generates the explanations to fine-tune the prediction model, together with the human-provided labels. The prediction model finally predicts the label.\n\nThis paper conducts two AL simulations on a large-scale NLI dataset (eSNLI), and finds that the AL system consistently outperforms the baselines in two settings. Additionally, this paper recruits humans to eacluate the validity, explainability, and preference of the generated explanations. The explanations generated by the AL system is not as good as the human explanations, but are preferred compared to those generated by other baseline systems.", "excitement": 4, "reproducibility": 3, "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": "- Figure 2: the standard deviation is usually plotted as error bars or regions, rather than a standalone line on the figure.  ", "Justification_for_Ethical_Concerns": "I do not see ethical concerns in this paper."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a new method to incorporate explanations from human annotators into active learning. The idea is a very reasonable extension to existing active learning approaches, and dual-model solution also seems clean and effective. There was some initial concern about the generalizability of the approach since the experiment only focused on NLI, which the authors justified by the lack of high-quality explanation annotation. During the discussion period, the authors conducted additional experiments on another dataset, which although showed smaller improvements compared to NLI, validates the soundness of the method. It would be exciting to see how the proposed approach complement alternative active learning strategies.
Decision: Accept-Findings