STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK
Authors: Danilo Neves Ribeiro
Shen Wang
Xiaofei Ma
Henghui Zhu
Rui Dong
Deguang Kong
Juliette Burger
Anjelica Ramos
zhiheng huang
William Yang Wang
George Karypis
Bing Xiang
Dan Roth
Venue: ICLR 2023 notable top 25% (2023)
Keywords: natural language understanding
question answering
structured explanations
soft reasoning
dataset

Abstract: We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer. We perform extensive evaluation with popular language models such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models still lag behind human performance when producing such structured reasoning steps. We believe this work will provide a way for the community to better train and test systems on multi-step reasoning and explanations in natural language.

Published as a conference paper at ICLR 2023
STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK
Danilo Ribeiro ,2.-, Shen Wang's o1 Xiaofei Ma', Henghui Zhu', Rui Dong', Deguang Kong', Juliette Burger', Anjelica Ramos', William Wang'. Zhiheng Huang', George Karypis'. Bing Xiang', Dan Roth
AWS AI Labs.
(shenwa, xiaofeim, henghui, ruidong, kongdegu, burgerju @ amazon.com, Jajeeramm, wyw, zhiheng. gkarypis, bxiang, drot) @ @amazon.com Northwweserrn University,
(dnribeiro) nuu.trrheettem.ddu
ABSTRACT
We introduce STREET, unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answerre (QA) datasets, expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer. We perform extensive evaluation with popular language models such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models still lag behind human performance when producing such structured reasoning steps. We believe this work will provide way for the community to better train and test systems multi-step reasoning and explanations in natural language.
INTRODUCTION
A long-term pursuit in Artificial Intelligence is endow machines with the ability to reason and manipulate premises to reach conclusions and perform tasks. Initially, most reasoning systems performed multi-step operations over symbolic or probabilistic knowledge (Newell & Simon [956] McCarthy et al. 1960 [Siler & Buckley (2005), and even though these systems were able to perform complex tasks (Vennon al. 2007] Metaxiotis et al. 2002, Ribeiro & Forbus 202T). there were still shortcomings when it comes to encoding such knowledge, learning reasoning rules and dealing with ambiguity (Bell 1985 Ribeiro al.) 2019). Some recent works the field of question question-answeriin answering (QA) have demonstrated that language models can bypass some of these issues and learn to reason directly over natural language (Clark et al. (2020), allowing for more flexible and adaptable reasoning capabilities. Another ad advantage of performing multi-step reasoning over natural language is that allows for more inspectable outputs, improving the explainability of models that are otherwise regarded as black box systems (Jain & Wallace 2019: Rajani et al.) 2019a Danilevsky et al.) 2020). Despite the recent progress, we notice that there is still a gap in resources for training and evaluating general reasoning capabilities over natural language.
To facilitate research in this direction we propose the STructured REasoning and Explanation Multi- Task benchmark (or STREET for short), containing collection of tasks in various domains including quantitative reasoning (math questions), analytical reasoning (logic puzzle questions), and deductive reasoning (common-sense and science questions). We build upon existing QA datasets by adding multi-premise, multi-step, structured explanations in the form of reasoning graphs, as depicted in Figure [ The STREET benchmark contains 35.8k questions, each of which is accompanied by a reasoning graph, either created by expert annotators or programmatically. When combined, all reasoning graphs contain total of 151.1k l reasoning steps (or textual entailments), of which 14.7k
'Work done during internship at AWS AI, current address: Department of Computer Science, Northwestem University, Evanston, IL 60208, USA Cooresponding Author.

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Published as a conference paper at ICLR 2023
Question Context
Reasoning Steps
Answer
GSMBK
[i] Notzilo sold dips 48 of Frindd Apri
Natalla 48
CONTEXT: (1) Natala sold clips to
April,
half
(2) and then she sold hal clps May
Answer:
72
QUESTION: (3) How many dios did Naralia sell atogether in April and Nay?
How Natalla
(S1 Natali sold 24
AR AS-LSAT
CONTUAT:
music store carries mcanttyy ten
(5) used pop on sale and new opera in not.
jazz. opero, pop, and soul. (3) The store having a sale on some thess types CDs, 14) folioerng
116) If now jazz used jazz are not onsale, thien new pop &s on sale.
sale and opens appiy both types of pop are on sale, than all soul is. If both types
(6) both txpes of pop are sale.
sole. is. 00 it wither type of rap sale, then scuil
Answer::
nelther
(R) If neither
vale
pup then ssed
then
CANNOT be true? (TT) Netierr tpps opera and netterr type of rap on sale. (121 Neither tope Nethher
[10) wheh nne the fnlinwing CANKIT
type spera and nether soul sale. (14) Netther type of juez and neither cype of soul on sale. (15) type
of
(18) netther both and munt sale, which contredict DI
(14) Nother of jazz and type
Figure I: Two examples from our proposed STREET benchmark. The questions are derived from the Grade School Math (GSM8K) and Analytical Reasoning Law School Admission Test (AR- LSAT) tasks. The QA components (e.g. question, context, and answers options) are broken into textual logical units, TLUs. These TLUs are connected to form reasoning graph. Our proposed benchmark builds upon existing QA datasets by adding structured reasoning explanations that shows how one can derive the answer to a given question.
were created by our expert annotators. We carefully selected the tasks such that most of the relevant knowledge required to answer the questions is contained within the question or context themselves.
Therefore, we focus on the reasoning problem, with greater number of reasoning steps (an average of 7.8 reasoning steps per answer) and more complex reasoning structure than previous datasets. These properties differentiate our work from single-step reasoning such as Natural Language Inference (NLI) (Bowman et al. 2015 Williams et al. 20188 Zellers et al. 2018) or multi-hop QA (Yang et al. Chen et al. (021)) that require specific factual knowledge retrieval.
2018
In our proposed evaluation, the models are expected to not only answer the questions, but also generate the reasoning graphs (including the textual intermediate steps) that explains their output answer. With that in mind, we design few evaluation metrics to verify if the generated reasoning graphs match the expected golden data. We perform extensive evaluation using some popular language models of various sizes, namely T5 (Raffel et al.) 2020) and GPT-3 (Brown et al.) 2020), either fine-tuning on training data or using few-shot prompting. Our experiments show that even though these models can achieve high solving rates on many of the original QA datasets, they still struggle to generate coherent and relevant reasoning graphs and appear be far below human performance.
Our main contributions are as follows: (1) We define reasoming graphs, which are structured chains of reasoning in natural language that provide explainability the output models on QA tasks. (2) We propose STREET, multi-task and multi-domain benchmark containing questions requiring diverse types of reasoning skills. The answers in the dataset contain annotated or generated reasoning graphs We make the data and evaluation code available online (3) We evaluate the performance of LMs such as fine-tuned T5 and few-shot prompting with GPT-3 on our proposed task. Our results suggest there is still room for improving language models when it comes to generating complex multi-step reasoning explanations.
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2 TASK AND DATA
2.1 TASK DEFINITION
In the standard definition, a question-answering task T = (C,Q, R) has the following com- ponents: an optional context C such as a passage or problem description; a question Q that might reference the context; answer options O (01, 02,...,00 oK) in the case of K K-way multiple choice questions; an expected answer A (where A E O, if options are present). Some MRC tasks (Ling) jet al., 2017 Camburu et al. 2018 Rajani et al. 2019b; Cobbe et al. 2021) also provide rationales It as free-form textual explanations of the answer A.
To generate a more fine-grained explanation, we modify the above formulation so that the data also contains a structured, step-by-step explanation of the answer, as depicted in Figure [ To this end, we define textual logical units (TLU), which are essentially a sequence of tokens from elements in T that defines premises that will possibly be referenced in the reasoning steps. More formally, a TLU for QA component in T E T is a list of the sequence of tokens in T. For instance, given the tokens T (4,, 42, ................................ 41r1), the TLU of T is defined as the list of spans LT = ((41,2), (12 + 1,13)..... (aa))).....((1r111111.1. (1(1)1-11 1, 1))) where 4 E {I | S [T]} and 4 > l for i > j. Each pair (1i, LT represents the sequence of tokens (f(L), ta, +1)......... .,,,)) The TLUs can be extracted automatically from the text by using a simple algorithm, e.g., breaking down paragraphs by punctuation mark. The algorithm we used to create the datasets can be found in Appendix A.5 Note that the TLUs for the rationale LR can be thought of as a sequence of step-by-step explanations.
Given the TLUs, we also define a structured explanation, which we call reasoning graph. Each element in Ln (referred here reasoning steps or intermediate conclusions) will be connected to set of other TLUs (or premises) that contain sufficient information supporting the reasoning step. The reasoning graph can be defined as a of vertices and edges = (V,E), where the nodes are TLUs Lo ULA LR) and the edges & C V (Lo ULOULA UR) are pairs of nodes. reasoning step, while the ending node is the output of a
such that 1 C (LoU LQ Lo The starting node of an edge is a premise reasoning step, i.e., an intermediate conclusion or an answer. Note that in this case the explanation graph G is directed (edges go from premises to conclusions) and acyelic (each conclusion should only be generated once). Our reasoning graph formulation shares some similarities to Entailment Trees from Dalvi et al. (20TT). However, our benchmark does not require a pre-assigned corpus of textual facts or a hypothesis (which must be included with the data). Furthermore, reasoning graphs allow for other QA elements (e.g., context, answer options, and expected answer) and represents the reasoning using the less restrictive directed acyclic graphs (a tree data structure can't 't easily be used to represent the examples from Figure] T).
2.2 DATA SOURCE AND ANNOTATION
With the goal of testing complex reasoning capabilities, we build upon existing QA datasets in which solutions require multiple reasoning steps. Since our focus is testing reasoning capabilities, we disregard datasets that require domain knowledge to solve the problem. Instead, we focus on the ones containing most of the information within the context, question, and answer options. We categorize the reasoning tasks according to their level of existing structured reasoning steps, which we describe below.
The first category, comprised of the science question dataset A12 Reasoning Challenge (ARC) Clark et al. 2018). already contains annotated structured reasoning steps provided by ENTAILMENT B ANN (Dalvi et al. 2021). ENTAILMENT BANK comes with an external source of knowledge (Jansen et al. (2018) from which premises could be retrieved to generate explanations. Since the retrieval of premises is out of the scope of this work, we directly add the gold premises to the context C' of the
QA t
The second category uses the Sequential Context-Dependent Execution dataset (SCONE) (Long et al., 2016). The questions in SCONE describe sequence of actions that modify a given toy world (c.g.. list of objects and their relative position), and the expected answer is the final world state. We extract
Entailment Bank has similar formulation which they call Task-1. However, they do not consider the problem of choosing the correct answer given the multiple choice options from ARC.
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Task
Task Domain
# Original Questions
Used Questions
# Reasoning
Answer
Name
Steps
Type
ARC
Science
7,787
1,840
5,881
4-Way MC
SCONE
Processes
14,574
14,574
130,482
State Pred.
GSM8K
Math
8,792
1,030
4,666
Number
AQUA-RAT
Math
101,449
1,152
7,179
5-Way MC
AR-LSAT
Logic 2,046
500
2,885
5- Bay MC
TOTAL
134,648
19,096
151.093
Table 1: The different tasks used to create the proposed benchmark. In the answer types, "K-Way MC stands for multiple choice answer with K options.
the reasoning steps programmatically this dataset also provide the intermediate world states for each question.
The third category of tasks, namely GSM8K (Cobbe et al. 2021 and AQUA-RAT (ing et al. 2017) contain unstructured rationales (in natural language) showing the chain of reasoning used to solve the given questions. In this category, we further annotate the datasets. First, we split the context, question, answer options, and rationales into TLUs, assigning a number ID to each of them. Afterwards, ask human annotators to create the structure of the reasoning steps, assigning which premises were used in each entailment step. Note that some entailment steps might not have any premises (e.g., when stating a known fact as in "one hour has 60 minutes").
Finally, the last category is comprised of the AR-LSAT dataset (Zhong et al. 2021 ). which is a relatively complex reasoning task (transformer-basee models are shown to struggle in this task) and does not come with any rationale or explanation. Therefore, we annotate the rationales and reasoning structure from scratch. We ask expert annotators to solve the questions given the context and the answer. While writing down the step-by step-by-step explanations, they are also asked to assign which premises are used reach each intermediate conclusion.
2.3 DATASET DETAILS
A summary of all datasets in our proposed benchmark be found in Table T Note that not all questions in the original datasets were used due to the significant amount of time required for annotation. Examples of the final data for each dataset be found in Appendix A.I
Annotation Details: Each dataset is pre-processed and QA components are broken down into TLUs. For all datasets except ARC and SCONE, we ask human annotators further annotate data points by first creating multi-step rationales (this is skipped if the dataset already contains rationales), and then connect each rationale step to the premises that support that conclusion (in the user-interface the annotators select a set of numbers for each rationale step). Note that human annotators are given an unlimited amount of time to complete each task, and they are mostly comprised of experts with undergraduate or graduate level education, opposed to randomly selected online workers.
For quality control, we performed two passes for each reasoning step, using third pass to break ties when needed. As indicator of annotation quality, using Fleiss Kappa x (Fleiss) 197I). Each directed edge in the reasoning graph is regarded as binary question (edge should be present or not). Finally, the first annotation passes are used compute = 0.79, indicating "substantial agreement" among annotators. With a total of 14,730 reasoning steps annotated (for GSMSK, AQUA-RAT, and AR-LSAT), estimate a total of 1,467 (paid) work hours. Further annotation details can be found in Appendix A.2
compute the annotation agreement
We analyze the data all combined datasets to obtain some insights into the scale and reasoning complexity. Figure 2 shows the distribution of the "number of reasoning steps" among the data points in each annotated dataset. Note that most of the tasks contain a larger number of reasoning steps compared to previous multi-hop hop QA datasets (Jaamtani & Clark 2020 Yang et al.) 2018 Geva et al. 2021; Chen et al. (02T). For the most part, multi-hop QA questions only contain up two "hops" (or reasoning steps), while STREET has average of 7.8 steps per question, with
Data Statisties:
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0.35-
0.30
0.25
AQUA-
0.20
0.15-
LSAT
0.03
0.00
Number Reasoning Steps
Figure 2: A histogram containing the distribution data points and the number of reasoning steps for each annotated dataset (training, development, and testing split combined), truncated to maximum of 15 steps. The distribution varies among datasets, with an average of 7.8 steps across all data points.
26.4% of the questions containing more than 10 reasoning steps. The number of incoming edges for each node (a.k.a "in-degree" or "valency". which is the number of directed edges with such node as destination) in the reasoning graph is usually between one and three, with questions containing nodes with more than five incoming edges. Further data statistics and dataset analysis are available in Appendices A.3 and A.4
3 BASELINE MODELS
We measure the performance of various models on our multi-task reasoning and explanation bench- mark. We show results separately for each of the tasks, where we mainly evaluate (1) the standard QA accuracy (i.e., can the model predict the correct answer?) and (2) the models' ability generate the reasoning graph. The evaluation metrics will be described in section 4.1
To this end, we use two approaches solve the structured reasoming task. The first approach fully supervised, where we fine-tune T5 model (Raffel ct al., 2020), which is similar to other work on reasoning datasets (Tafjord et al., 2021 Dalvi et al.] 2021 Ribeiro Forbus 2021). The second approach uses the much larger GPT-3 language model (Brown et al. (020). Instead of fine-tuning GPT-3, we use few-shot prompting. These large language models have been shown to have strong step-by-step reasoning generation capabilities (Wei et al. 2022 provided with a handful of examples.
Wang et al. 2022) even if just
3.1 REASONING GRAPH ENCODING
Following prior work with structured input and output (Chen al. 2020 Tafjord al, 2021 Dalvi al.) 2021 Neves Ribeiro et al. 2022), we linearize the reasoning graph such that it can be generated by the language model as sequence tokens. First, each one of the candidate premises (i.e., context, question, and answer options) are assigned according to a valid topological order (i.e., all premises must be part of the linearized string before adding reasoning step node). For tasks where answer types are multiple-choice node will contain the text with the value the predicted answer, such as "The answer is A)" or "The answer 15". The text encoding for the GSMSK example in Figure [ can be seen below:
id token. Then we sort the reasoning graph's steps
number, the last
$questions = (1) Natalia sold clips to 48 of her friends in April, and then (2) she sold half as many clips in May. (3) How many clips did Natalia sell a altogether in April and May?
$proof$ (1) (2) >> (4): Natalia sold 48/2 24 clips in May;
(1) & (3) (4) -> (5): Natalia sold 48+24 72 clips altogether in April and May; (3) & (5) -> (6) The answer is 72;
The SCONE task is special case where do not expect the generative models output the state of every tracked object the answer node. Instead, the answer is extracted from the intermediate nodes of the reasoning graph (examples shown in Appendix A.I
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3.2 SUPERVISED TRAINING
For full supervision, we fine-tune the T5-large model (770 million parameters) on the training data for each task separately. The model is fine-tuned for up to 30 epochs, and we select the check-point with the highest answer accuracy on the development data at the end of each training epoch. The training done using a machine with four NVIDIA Tesla V100-SXM2. and the Hugging Facd pre-trained T5-model distribution. Further implementation details are available in Appendix C During inference, we use beam search with beam size of generate the reasoning graph and the answer for a given question.
3.3 FEW-SHOT PROMPTING
For few-shot prompting we use GPT-3 Brown et al. 2020 by accessing the OpenAl's API The API provides access to few model variants. For experiments use the largest advertised model, namely text- detttddavnniiii 10000002022----- (175B parameters). During inference, we select up to examples (depending the tasks and models, fewer prompt examples might be provided due to the encoder token size limit) as prompts for the model, following the encoding method from Section 3.1 except we remove the expected reasoning graph from the target question. During generation, we use greedy decoding and do not set any maximum or minimum output size, expecting the model to predict the end of the structured output.
EXPERIMENTS
4.1 EVALUATION METRICS
We specify the main categories evaluation metrics described below to evaluate the answer to the questions and the generated reasoning graph.
Answer Accuracy: This metric measures the ability of generative models to predict the correct answer to a question. Exact match is used to evaluate the models on tasks with multi-choice or numerical answers. For the tasks with state prediction (i.e., SCONE), we use the combined state for each object as the expected answer. The answer accuracy will be an upper bound for the other metrics since any generated reasoning graph with an incorrect answer is also labeled as incorrect.
Reasoning Graph Accuracy: This metric compares the predicted and golden reasoning graphs in terms of both the graph structure and the content of the intermediate conclusion nodes. The comparison between the predicted graph 9, (Vp, En) and golden graph 9, (V,, y, Eg) starts with aligning the nodes in Vp and Vg. In this matching. we use the premises anchors, and the reasoning step nodes are matched according to their ancestors in a topological ordering. Given two matched textual similarity function (rtezt((p)) texr((e))
reasoning step nodes Up E Vp and 0g Vg. to test if two reasoning step nodes are equivalent. The textual similarity function varies depending on the QA Task. More details the matching algorithm and the different text similarity functions used are available in Appendix(I D Note that this is a strict metric, and small deviations from the golden reasoning graph will render the predicted graph incorrect.
Reasoning Graph Similarity: The reasoning graph similarity sim(Gp, G.y) is a "softer" metric that compares the predicted and golden reasoning graphs using the graph edit distance function 8(G,, G.)) The function ö uses insertion, deletion and substitution as elementary graph edit operators over nodes and edges. The text similarity function is used to if two nodes match. The cost of any edit operation is 1. However, if the generated answer is incorrect, the similarity is set to 0 (i.e., the edit cost for the "answer node" LA is oo). The Graph Similarity function is normalized (the output is in the range [0, IJ) and can be computed as:
8(G,, Gg) sim(9p.9. = mar([N, Epl. N2 + |EgD)
"oodll available at h/httts::/huggiinggt htt.fffccccottt1111 co/t5-1arge /htps://openai.com//p//

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Model
ARC SCONE GSM8K
AQUA-RAT
AR-LSAT
Answer Accuracy
Random Guess
25.0
20.0
20.0
T5 [large] (fine-tuned)
93.5
69.6
10.4
28.7
28.0
GPT-3 [davinci] (few-shot)
72.9
02.3
34.8
40.2
19.0
Reasoning Graph Accuracy
T5 [large] (fine-tuned)
17.1
60.0
00.7
00.0
00.0
GPT-3 [davinci] (few-shot)
01.7
01.2
00.7
00.0
00.0
Graph Similarity
T5 [large] (fine-tuned)
44.1
67.0
05.4
00.9
00.3
GPT-3 [davinci] (few-shot)
15.1
01.9
16.0
05.2
01.1
Table 2:
The main results the test across the different tasks and different evaluation metrics. Numbers are in percentage. The "Random Guess" results are included to facilitate visualization since different tasks have different answer types.
In general, computing the graph edit distance can be computationally expensive as the problem is NP-complete (Abu-Aisheh al. 2015). For this reason, compute approximation of this value by using the implementation from the networky library.
4.2
RESULTS
The main experiment results can be found in Table [ The results for the SCONE task are averaged across the different sub-tasks (namely Alchemy, Scene, and Tangrams). Further ex- periment results with different model sizes and generalization settings can be found in Appendix
Reasoning Graph Similarity for Correct Answers
Human Performance
AR-LSAT
TS (large] GPT-3 [davinci]
There are a few takeaways. First, we notice that T5 [large] (fine-tuned) either outperforms or is on par with GPT-3 [davinci] (few-shot) on ARC and SCONE across all metrics, while the opposite is true for the math domain tasks GSM8K and AQUA-RAT. Both methods per- form poorly on AR-LSAT. This result is consis- tent with the results from Zhong et al. (202I), which shows that language models still struggle with more complex analytical reasoning tasks.
AQUA-RAT
GSMBK
SCONE
Second, the results of fine-tuned T5 [large] show that the Reasoning Graph Accuracy and Reasoning Graph Similarity substantially better on both ARC and SCONE (with perfect generation for 17.1% and 60.0% of the correct answers, respectively), while close to zero for the remaining tasks. Both ARC and SCONE are the tasks with best answer accuracy compared to "random guess". This suggests trend that the higher the answer accuracy for task, the more likely the model is able to produce a correct reasoning graph explaining their answer.
ARC
0.0
0.2
0.4
0.6
0.8
1.0
Similarity Value
Figure 3: A histogram containing the reasoning graph similarity of baseline models well hu- man performance on randomly selected subset of the test data.
hhups:/networkx..rgg

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Third, the GPT-3 [davinci] model struggles
with tasks outside of the math domain. Noticeably, the SCONE results are far worse when compared to T5 [large]. This has been observed previously by |Wei et al. (2022), where few-shot prompting models can struggle with toy "symbolic reasoning" tasks. This often happens when models are not of sufficient size or if the prompt examples are out of domain (i.e., evaluation examples have more steps than prompt examples).
To better visualize and understand the quality of the generated output, we plot the reasoning graph similarity for each task in Figure This plot only considers outputs in which answers match the golden answers for the QA tasks. For reference, we also estimate the human performance by asking expert annotators to write the reasoning graph from scratch, given only the context, question, and the expected answer (broken down into TI.Us) for 100 randomly selected questions from the test set across the different tasks.
The baseline models perform well compared human performance on both SCONE and GSM8K. Most noticeably, T5 [large] seems to generate the best structured explanations outputs for SCONE, which is expected since this task is more formulaic and contains the most training examples. On other hand, the baseline models seem to perform much worse on ARC, AQUA-RAT and AR-LSAT, with uoman--enerated reasoning graphs having over 200% higher scores compared to baselines. The human results AQUA-RAT, and AR-LSAT are somewhat lower than on the other tasks, primarily due to the diversity of the possible valid outputs (there are multiple ways one can explain an answer). In general, automatic evaluation of generated text is still challenge (Celikyilmaz al. 2020 in the field of natural language understanding.
4.2.1 ERROR ANALYSIS
To better understand the model's mistakes, we manually analyze 100 randomly selected outputs where the answers are incorrect. Since each STREET task has their own peculiarities, we analyze the error patterns for each of them separately.
ARC In this task, both baseline models seem to struggle with large outputs (i.e., more than 5 reasoning steps), which leads to a common error pattern where generated reasoning graphs do not contain the answer to the question (~ 62%).
To our surprise, GPT-3 [davinci] often fails to execute basic operations in this task. generates incorrect conclusion in first reasoning step for 83% of the analyzed outputs. This could be due to the limited number of few-shot examples (as consequence of the input size limit) or because this task is outside of its pre-training data distribution. On the other hand, the T5 [1 [large] seems to make fewer mistakes, with x 74% of all reasoning steps matching the golden data.
SCONE
GSM8K Even among incorrect answers, both models often able output partially correct proofs. small percentage of the incorrect steps are due to calculation error (s 12% in GPT-3 [davinci] outputs and 30% in T5 [large] outputs). In 37% of the generated outputs, the models seem to have misunderstood the question or applied the wrong formula in one of the steps.
In this task, both models hallucinate the predicted answer into the last reasoning step (~ 28%). even when it does not follow the step's 's equation. Similarly to GSM8K, small percentage of the incorrect steps are due calculation errors (~ 12%).
AQUA-RAT
AR-LSAT Both models struggle with this task. Most of the correct answers are due to random chance, and (z 33%) of the generated outputs don't contain an answer to the question. particular, GPT-3 [davinci] often just copies the TLUs from the question without making any meaningful conclusions.
RELATED WORK
Complex Reasoning in Question-Answerin Modeling complex reasoning is important chal- lenge and crucial component of natural language understanding. In the context of question- answering (QA), initial efforts to emulate complex reasoning used symbolic representations and
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problem solvers (Forbus & De Kleer 1993 Platonov et al. 2020). With recent advances in pre- vre-trained language models, reasoning over natural language has became more tractable as these models can more easily handle ambiguity and learn the reasoning rules implicitly (Clark al. 2018 Tafjord
et al. 2021 J. As one of the simpler forms of reasoning, textual entailment was extensively studied, and many datasets and tasks have been proposed (Bowman et al.] 2013; Williams et al.) [2018] Zellers
it al. (2018). To address multi-step reasoning over language, many multi-hop QA (MHQA) datasets
and methods were proposed (Yang et al. 2018 Dua al. 2019 Xiong al. 2021 Chen al. 2021).
Common limitations of MHQA that we try to address in this paper include (1) small number of
reasoning steps (usually up to three) and (2) simplistic evaluation, allowing models to correctly predict answers by exploiting spurious correlations (Tang et al. (021). Datasets such as CLUTRR (Sinha (et al. 2019) and RuleTaker D* (Clark et al. 2020) better addressed the multi-step and structured aspect reasoning with explanations. However, they contain mostly synthetic data and tasks that are relatively easy to solve with current language models.
Explainable Question-Answering Due to the black-box nature of neural networks (Danilevsky ct al., 2020), many approaches were proposed to improve the explainability of QA systems. They include explanation graphs (Jansen et al. 2018). generating free-form natural language explanations
(Camburu et al., a 2018 Rajani et al. 2019b Ayyubi et al.] 2020), and chain of reasoning explanations Jhamtani & Clark (2020). Most noticeably, Dalvi al. (222)) introduced the concept of Entailment Trees, containing multi-premise textual entailment steps. Entailment Trees differ from our proposed Reasoning Graphs in three main points (1) they were designed to be used mostly for explanation, not representing the answer to the questions directly (2) they require an external corpus of premises (3) they use the concept of hypothesis (as combination of question + answer) that needs be annotated as input. We believe reasoning graphs are more flexible representation for explaining answers in the context of QA.
Multi- Language Understanding Benchmarks are an important way to measure progress in natural language understanding. Datasets that contain multiple tasks have the advantage of testing the generality of models. The GLUE (Wang et al., (0188) and SUPER-GLUE (Wang et al.) 2019)
contain tasks such as reading comprehension and natural language inference. The Massive Multitask
Language Understanding benchmark (Hendrycks et al. 202 contains various QA problems extracted from the internet. BIG-Bench (Srivastava al. 2022) contains over 200 tasks drawing on problems involving linguistics, math, common-sente sense reasoning, and others. These datasets arguably test the model's ability to perform reasoning to some degree. However, most of their evaluation revolves around answering questions instead of systematically testing the reasoning required to answer such questions.
CONCLUSION
We aim to enable machines to perform multi-step reasoning while explaining their answers. We believe that teaching machines how to manipulate premises and reach conclusions can be an important step towards true language understanding. With that in mind, we introduce STREET, a new multi-task reasoning and explanation resource covering various forms of reasoning in the context of question- answering. We hope this benchmark will allow for a more systematic evaluation of the reasoning capabilities of natural language systems. Future avenues of research include exploring reasoning capabilities with knowledge retrieval and using supervised models trained on multi-step reasoning data to bootstrap unsupervised learning for multi-step reasoning.
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A STREET DATA
A. DATA EXAMPLES
We show examples (as linearized textual encoding) for each of the five tasks in STREET below. The questions and reasoning graphs were taken from the development set. The SCONE task contains three different sub-tasks (namely Alchemy, Scene, and Tangrams), and we show examples for each of them separately as their format differs significantly.
ARC:
$questions = (1) the sun rising / setting occurs once per day
(2) the sun setting is kind of event (3) the sun rising is a kind of event (4) Which event occurs on daily cycle? (5) A) The
Sun rises and sets. (6) B) Earth tilts on its axis. (7) C) Earth
revolves around the Sun. (8) D) The Moon revolves around Earth.
$prooff (1) (2) & (3) -> (9) the sun rising and setting is the event that occurs once per day; (9) -> (10): The answer is A)
SCONE (Alchemy):
$questions (1) first beaker has 0 chemicals (2) second beaker has 1 green chemical (3) third beaker has purple chemical (4) fourth beaker has 1 orange chemical (5) fifth beaker has green chemical (6) sixth beaker has 1 red chemical
(7) seventh beaker has 1 yellow chemical (8) throw out the orange chemical (9) then, add the leftmost beaker of green chemical the yellow chemical
(10) mix it (11) then, add the remaining green chemical to it (12)
mix that too
Sproof$ = (4) & (8) -> (13): fourth beaker has 0 chemicals; (2)
(7) & (9) -> (14): seventh beaker has yellow and 1 green
chemical; (2) & (9) -> (15): second beaker has 0 chemicals; (14)
(10) -> (16): seventh beaker has 2 brown chemicals; (16) (11)
& (5) -> (17): seventh beaker has 2 brown and 1 green chemicals;
(11) & (5) -> (18): fifth beaker has chemicals; (17) (12) ->
(19): seventh beaker has brown chemicals;
SCONE (Scene):
$questions = (1) position has no person (2). position 2 has no person (3) position 3 has persen (4) position 4 has person (5) position 5 has person in red shirt and yellow hat (6) position has no person (7) position
(8) position has no person (9) position 9 has no person (10) position 10 has no the man in a red shirt and yellow hat (12) a second man in a yellow shirt appears on the left end (13) he leaves (14) the man the red shirt and yellow hat moves one space to the left (15) man in red shirt appears on his right
has no person
person (11) man in a yellow shirt appears on the right
Sprooff = (11) & (6) -> (16): position 6 has person in yellow
shirt and no hat; (1) & (12) -> (17): position 1 has person in yellow shirt and no hat; (17) a (13) -> (18): position has
person; (14) (4) & (5) (19): position 4 has person in
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red shirt and yellow hat; (14) (5) -> (20) position has no
person; (20) (15) -> (21): position 5 has person in red shirt and no hat;
SCONE (Tangrams):
Squestions (1) position has figure A (2) position 2 has figure
(3) position 3 has figure (4) position has figure (5) position 5 has figure B (6) swap the 1st and 5th figure (7) swap the 1st and 3rd figure (8) swap them back (9) delete the 5th
figure (10) add it back
Sproofs (1) (6) -> (11): position has figure B; (5) (6) ->
(12) : position 5 has figure A; (11) & (7) >> (13): position 1 has
figure E; (3) (7) -> (14): position 3 has figure B; (13) (8)
>> (15) position 1 has figure B; (14) & (8) -> (16): position 3
has figure E; (12) & (9) -> (17): position has no figure; (17) &
(10) >> (18): position 5 has figure A;
GSM8K
$questionS = (1) Adam and Tom are brothers. (2) Adam is 8 years old and (3) Tom is 12 years old. (4) In how many years will their combined age be 44 years old?
$proofs - (2) (3) -> (5): At present, the two brothers have
combined age of 8 + 12 20 years old.; (5) -> (6) Therefore,
1 year means
increase in the sum of their ages by 2
years.; (4) & (5) -> (7): : Adam and Tom need a total of 44 20 24 more years to be 44 years old together.; (6) (7) (8): So both brothers will be 44 years old together after 24 / 2 = 12
years.; (4) (8) -> (9) The answer is 12;
AQUA-RAT
$questions (1) Three birds are flying at fast rate of 900
kilometers per hour. (2) What is their speed in miles per minute?
(3) [1km 0.6 miles] (4) A) 32400 (5) B) 6000 (6) C) 600 (7) D) 60000
(8) E)
$proof$ = (0) -> (9): To calculate the equivalent of mi miles in a
kilometer; (3) -> (10): 0.6 kilometers
mile; (10) (1) ->
(11): 900 kilometers = (0.6)+900 = 540 miles; (0) -> (12): In
1 hour there are 60 minutes; (11) & (12) (2) >> (13): Speed
in miles/min miles/minutes 60 x 540 32400; (13) (2) & (4) >> (14):
Correct answer A; (14) -> (15) The answer is A);
AR-LSAT
SquestionS (1) Four boys (2) Fred, Juan, Marc, and Paul (3) and three girls - (4) Nita, Rachel, and Trisha (5) will be assigned to a row of five adjacent lockers, (6) numbered consecutively through 5, (7) arranged along straight wall.
(8) The following conditions govern the assignment of lockers to the seven children: (9) Each locker must be assigned to either one
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Promet:
Cument Raticnale:
working gam.
(8) Tuesday, Oliver worked 2-2 hours.
houro
Wedhnent Wedneeday deekkedd work
Mordey
any support Rationale?
On Thursday the gyn doood,
O No
(S) no Olker needed moncison hore which took taion as much time ne Tueaday
Click Supporting they populate
many
during doys?
C2 Monday ho Ahours, (C) dayfor leas.
Annwer Cretions:
data available
U1 have tommenta this
Exolanation:
On Touaday, Clhvr workee 2houuss
Wednesday houre
Monday,
for
(10) On Th Thursday, af home, Cliver trained twibe much Tueaday, so
far2 2 hou's
[11) Oliver trained for 4 hours.
(12)
10
Figure 4: Screenshot of the web-based annotation tool designed to author the reasoning graphs. The fields the left contain the TLUs from the question components. The fields the right are used by annotators to select the premises for the current rationale (i.c., reasoning step). For AR-LSAT, the annotators also had to write the rationales in addition to selecting the premises.
or two children, (10) and each child must be assigned to exactly one locker. (11) Each shared locker must be assigned to one girl and one boy. (12) Juan must share a locker, (13) but Rachel cannot share locker. (14) Nita's locker cannot be adjacent to Trisha's
locker. (15) Fred must be assigned to locker 3. (16) Which one of the follewing is complete and (17) accurate list of the children who must be among those assigned to shared lockers? (18) A) Fred, Juan (19) B) Juan, Paul (20) C) Juan, Marc, Paul (21) D) Juan, Marc, Trisha (22) E) Juan, Nita, Trisha
Sproofs = (1) (3) & (5) -> (23): Four boys and three girls will assigned to five adjacent lockers; (10) (11) & (9) -> (24) Each locker can be assigned to max two children, one girl and one boy, and one child must be assigned to exactly one locker; (12)
share lockers Juan, Nita, Trisha; (13) -> (26): : Kids not sharing lockers Rachel; (0) -> (27):
(14) -> (25): : Kids who
Answer is 22; (27) -> (28): The answer is E);
A.2 FURTHER DATA ANNOTATION DETAILS
Initially the expert annotators are given a guideline document containing a list instructions on how to properly label the data. Afterwards, carried out meeting to elucidate any further questions about proposed annotation tasks. The guideline documents contains few annotation examples created by the authors and also contains overall annotation instructions, summarized as follows:
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Dataset Propotion
GSMBK
0.6
SCONE
0.4
AR-LSAT
0.0
Number of Incomiing Edges
Figure 5: A histogram containing the distribution of incoming edges (a.k.a node's degree) per reason- ing step for each annotated dataset (training, development, and testing split combined), truncated to maximum value of incoming edges.
The premises (directed edges) should contain all the information needed to ensure that the conclusion nodes are entailed given the premise.
Completeness:
Edges connecting nodes should ensure the entailment correct, and no further irrelevant edges should be included.
Relevance:
Purposefulness: The nodes containing the question and the answer should always included in the final reasoning graph.
While writing the step-by-step rationales (in the case of AR-LSAT), the entailments should be fine-grained, encoding single inference or logical step.
Granularity:
As mentioned previously, human annotators were given an unlimited amount of time to complete their tasks. The education level of the experts annotators were undergraduate graduate. We believe all these factors contributed to the final quality the data.
The annotation tool used simple web-based form, with fields containing the question, context, answer choice, and answer. Each annotation represented a single entailment step. The annotators had select from check-box like input field containing premises. For AR-LSAT they required to write the step rationales as well (explaining the answer). A screenshot the annotation tool for with GSM8K example is shown in Figure
A.3 FURTHER DATA STATISTICS
The STREET reasoning tasks not only multi-step, but also multi-premisee where over 96.5% of nodes in the reasoning graphs contain two or more incoming edges. The distribution of such incoming edges is shown in Figure
A.4 DATASET ANALYSIS
We analyse the types inferences used during the reasoning steps in STREET. We sampled 100 reasoning steps, evenly distributed among the five tasks. We manually classify the reasoning steps in seven different categories, as shown in Table 3 For each category, provide reasoning step example, including the premises and the conclusion.
The majority of the reasoning steps requires "Spatial reasoning" (41%), involving concepts such as inertia, object displacement, ordering, and others. The second most common type involves "arith- metic inference" (20%), where conclusions contain simple operations such addition, subtraction, multiplication and division. In the third most common inference type, "identifying answer" (17%), the conclusion simply states the final answer, either in numerical or multiple-choice format.
A.5 TEXTUAL LOGICAL UNIT EXTRACTION ALGORITHM
Algorithm contains pseudo-code for the script used to extract TLUs from the components of the questions. The algorithm uses Python's 's module "re" style of regular expression and matching to determine boundaries between Ts.
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Published as a conference paper at ICLR 2023
Inference Type
%
Example(s)
pl
"position 3 has person in purple shirt and purple hat" "the man with the purple shirt and hat moves the right end" "position 3 has no person"
spatial reasoning
p2
41
step
pl
"There are 30 students" "he wants to give Valentine to 60% of them" "He needs valentine's s cards because 30 * 0.6 18"
arithmetic inference
20
p2
step
pl
"What their speed miles per minute?"
identifying answer
p2
"A) 32400"
17
p3
"Speed in miles/mmnntes = 60 540 32400;" "The answer is A)"
step
pl
"a plant cell is a kind cell" "a cell nucleus is part of cell" "a plant cell contains a nucleus"
logical inference
12
p2
step
pl
"Four boys"
paraphrasing question context
p2
"and three girls"
5
p3
"will be assigned to a row five adjacent lockers"
step
"Four boys and three girls will be assigned to five adjacent lockers"
algebraic manipulation
pl
1//1 7/K"
4
step
"k = 91"
stating common
step
"In hour there are 60 minutes"
knowledge
Table Types of inference in the reasoning steps for the different tasks in STREET. p/ through p3 represent the premises, while step represents the conclusion. Some of the conclusions do not have any premises (e.g., "stating common knowledge" inference type).
B FURTHER RESULTS
We perform further experiments to understand: (1) how model sizes influence the final results (2) if models can generalize when using examples from other tasks. The experiment results with GPT-3 model are summarized in T
First, we compare the results with the GPT-3 [curie] (few-shot) model. This model performs significantly worse than GPT-3 [davinci] (few-shot), even though it is advertised as the next best model [6] This phenomenon has been previously observed, with Wei et al. (0022) reporting that large models perform qualitatively better than smaller models. Second, the GPT-3 [davinci] (few-shot, MT) model selects one examples from each STREET task and use them as "few-shot" examples in order simulate multi-task setting. We observe that the results comparable to GPT-3 [davinci] (few-shot) except for GSM8K, which has significantly worse results. For the most part, these results suggest that GPT-3 can adapt and learn from other reasoning domains.
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end for Published as a conference paper at ICLR 2023
Algorithm Textual Logical Unit Extraction
procedure EXTRACT- EXXTRACT-TLUS(ter" (LUs(tezt)
2:
patterns + "(. G OY ){? ( and ( then )"
> TLU boundaries REGEX
3: matches text.reger match (patterns)
4: tlus t-
5: last. pllspos last-pos- 0
6: for match malches do
7:
ll + seaat.substring(llss.pss match.end())
8:
if matchh.eextl & [",", "and", "then"] then
9:
tokens siiz)) 2 then tlus. (uts.
10:
11:
last_pos t- match.end
end if end
13:
15: return tlus
end procedure
Model
ARC SCONE
GSM8K
AQUA-RAT
AR-LSAT
Answer Accuracy
Random Guess
25.0
20.0
20.0
GPT-3 [davinci] (few-shot)
72.9
02.3
34.8
40.2
19.0
GPT-3 [davinei] (few-shot, MT)
88.1
01.2
07.0
37.1
20,0
GPT-3 [curie] (few-shot)
29.4
00.0
0.37
24.8
21.0
Graph Similarity
GPT-3 [davinci] (few-shot)
15.1
01.9
16.0
05.2
01.1
GPT-3 [davinci] (few-shot, MT)
25.3
01.2
0.21
05.8
00.9
GPT-3 [curie] (few-shot)
00.9
00.0
00.0
00.3
00.0
Table 4: Results the test across the different tasks and different evaluation metrics for variations of the GPT-3 model. Numbers are in percentage. The "Random Guess" results are included facilitate visualization since different tasks have different answer types.
C IMPLEMENTATION DETAILS
The T5 model is fine-tuned in an auto-regressive manner. We select the AdamW (Loshchllov Hutter 2019) the optimizer. During training, we use batches containing two data points. The learning rate starts at zero and is gradually increased its maximum value of 3 10-5 After 1000 steps, the learning rate is decreased following a cosine function scheduler. The weight decay is set to 10
D REASONING GRAPH SIMILARITY
The textual similarity function (a, a) a binary function that maps the text two nodes (a and b) to TRUE FALSE value. We use different textual similarity functions for each of the tasks in STREET. The description of the function are found Table 5
Note that our comparison function disregard reasoning steps that contain antecedents as they are mostly used as supporting facts during reasoning (for instance, "one hour has 60 minutes" is node that might not have antecedents in the reasoning graph).
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Published as a conference paper at ICLR 2023
STREET Tasks
Text Similarity a(a, b) Description
SCONE
The nodes in scone follow well defined textual structure, therefore the node similarity returns TRUE if and only a
GSM8K and AQUA-RAT
We parse the node text and extract all the mathematical values inside the node. For instance "Natalia sold 48 May" would be converted to the set {48, 2, 24}. Then the similarity function return TRUE if the sets extracted from a and are equal.
24 clips in
ARC and AR-LSAT
Since the conclusion nodes in these are in free text format, we follow Dalvi et al. (202I) and use the BLEU RT (Sellam et al., 2020) text similarity function. BLEU RT is trained metric that uses the BERT language model and was shown to have better correlation to human judgment than standard metrics like BLEU and ROUGE. We define that BLEU RT > 0.25 is the threshold that decides if nodes and are similar.
Table 5: Definition of the text similarity function for each the STREET tasks.
20


Reviews:
[{"review_id": "vWuFpeYOT6", "reviewer": "Reviewer_3BMb", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "\nThe authors define a reasoning task, that of providing a graph that\ncorresponds to the reasoning steps leading to the correct answer (from\npremises provided in the question), and build an annotated dataset\nwith question and answers and the graphs from several domains. The\nauthors present several properties of the dataset (histograms of\nnumber of steps required, number of premises, etc). They explain how they use existing\ntechniques to produce their reasoning, how they evaluate (graph\ncomparisons), and report that existing techniques appear to\nsubstantially lag behind human performance (even though, the correct\nanswer rate may be relatively high).\n\n", "summary_of_the_review": "\nA useful challenging task that will help advance language understanding and research on reasoning/explanations.\n", "strength_and_weaknesses": "Strengths:\n\n-- A challenging task with much annotations in several domains\n\n-- An analysis of performance of several advanced techniques\n\nWeaknesses:\n\n-- A few minor clarity issues\n\ntypos, etc:\n\nPage3: \"The TLUs for the rationale L_R can be thought of .. \" is\nconfusing ..  (I don't think L_R is defined yet, though L_T is, and\nperhaps you meant to say: 'The TLUs for the rationale R, L_R, can be\n...\", that is introduce the notation L_R pehraps ..) (L_R is used in\nthe next paragraph.. ) (On re-reading, I now understand T can be any of\nL, O, etc.. perhaps stress that, early, when you start with for any T\nin \\bf{T}..  )\n\npage2: why not use 'correct' instead of 'expected' answer.\n\nA couple of questions:\n\nquestion 1: [Assessing the quality of graph matching] Especially when\nthe system gets the answer right, and the graph generated is perhaps\nsomewhat similar but not perfect, how often are the steps actually\ncorrect or close to correct (is it near 0%?).  It would be good to\nunderstand the calibration of your graph matching, or to get a\nfunction mapping from similarity score to correctness or degree of\nconnectedness, by manually examining a few such cases, perhaps 10s to\n100s is sufficient to get an estimate (you may have done this\nanalysis, but I missed it) (I looked at appendix C but didnt find\nsuch). It is possible that a partial graph similarity score is almost\nnever correct (with probability 90% or 99% depending on how many you\nmanually evaluate).\n\nquestion 2: Is there any need for negation (does that happen, or is\nthe percentage of such basically 0)?  For instance, a part of the\nquestion or context negating one of the multiple choice answers,\nthereby rendering it incorrect.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "p1Fgj3KmQO", "reviewer": "Reviewer_4X1J", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "1. This paper presents STREET, a new question answering benchmark that also contains step-by-step structured explanation annotations.\n2. Established a number of baselines on this dataset including GPT3 few-shot prompting.", "summary_of_the_review": "This paper presents a valuable benchmark for pushing forward research in structured explainability, and I would love to see it accepted in the end.  In the current version, 1) authors spent most effort in collecting annotations alone and have done little analysis for the annotations, and 2) although being a multi-task benchmark, this is a lack of analysis in the multi-task aspect because experiments have mostly been done on each task individually. For these two reasons, I don't feel this paper is ready to be presented.", "strength_and_weaknesses": "Strengths:\n1. explainable NLP is an active research area, and this benchmark can help the community push forward the progress\n2. Some baseline results are estalished\n\nWeaknesses:\n1. There is a lack of detail in annotation process.  Specifically, AR-LSAT seems like an extremely difficult reasoning dataset, and yet in section 2.2, very limited information about how the annotation process is carried out.\n2. There is a lack of dataset quality verification process / dataset analysis.  There are many papers showing that the agreement rate could be low for intermediate reasoning steps annotations.  An in-depth study in this regard is important.  Examples include sec 3.4 in [1] and and sec 4 [2].\n3. As a multi-task dataset, there is a lack of analysis in the multi-task aspect.  At the bare minimum, it would nice to see how fine-tuning on all of the tasks improves or degrades the performance. See [3] for an example of what people do to analyze multi-task models.\n\nQuestions / suggestions:\n1. What is the rationale of not including previous multi-hop reasoning dataset such as ProofWriter?  Are the authors only selecting datasets that contain pure natural language but not synthetically generated language?\n2. It would be nice if the authors could discuss what more can this paper offer than [4], which has a more comprehensive survey on the structured explanation dataset.  I guess the unifying metrics established in this benchmark is valuable for future work to evaluate different NLP systems under the multi-task setting.\n\n[1] WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge\n[2] HOTPOTQA: A Dataset for Diverse, Explainable Multi-hop Question Answering\n[3] UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark\n[4] Teach Me to Explain: A Review of Datasets for Explainable NLP", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "ivs_HrIjIJ", "reviewer": "Reviewer_KKP2", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This work introduces a new benchmark (STREET) to evaluate the reasoning capacity of language models. The benchmark contains 5 different reasoning datasets (ARC, SCONE, GSM8K, AQUA-RAT, AR-LSAT) requiring on average 7.8 reasoning steps per answer.\nThis benchmark evaluates 3 metrics: Answer Accuracy, Reasoning Graph Accuracy, and Reasoning Graph Similarity.\nThe paper evaluates a t5-large model fine-tuned on individual tasks and a GPT3 model in a few-shot procedure. Results show that large language models still struggle in these multi-step reasoning tasks.\n", "summary_of_the_review": "This is a good paper, however some things need to be added: see **W1**, **W2**, and **R1**, **R2**, **R3** paragraphs in the above sections. In addition, after looking at the few examples of the dataset in the paper, I question the quality of the reasoning graphs automatically generated (see **CQ1**, **CQ2** and **CQ3** above.)", "strength_and_weaknesses": "**Strengths**:\nThe paper is clear and easy to read. The motivation of the work is well defined.\nThe introduction of an additional multi-step reasoning benchmark is very welcomed and can have a large impact as many previous multi-hop datasets were either only a few hops (mainly 2), or lacking the natural language steps to explain complex answers.\n\n**Weakness (W1)**: However, some previous datasets propose multi-step reasonings with logical explanations in natural language: CLUTRR (Sinha et. al, 2019) and AI2 ProofWriter (Tafjord et. al, 2020). The paper must compare their benchmark to these previous work.\n\n**Proposition (P1)**: It would be nice to include CLUTRR and ProofWriter in the STREET benchmark or at least document how the community can contribute to STREET and add new relevant multi-step reasoning datasets in time.\n\n**Proposition (P2)**: Can the authors compare models’ performance when the candidate answers are present and missing from the input to the model.\n\n**Recommendation (R1)**: The STREET benchmark should also propose a split based on the number of reasoning steps per dataset. That would give users the opportunity to evaluate (1) a multi-task setup, and (2) a compositional generalization setup: can a model trained on k reasoning steps perform well on k+-n reasoning steps. \n\n**Question (Q1)**: The reasoning graph’s steps are ordered according to a valid topological order. However, there can exist multiple topological orders. For instance if we have A+B->D followed by B+C->E, followed by D+E->Final Answer, we can also first prove B+C->E then A+B->D then E+D->Final Answer. When linearizing the graph into a sequence of tokens, did the authors shuffle the premise order? Otherwise trained models could pick on some weird premise order biases. Something to consider.\n\n**Recommendation (R2)**: The text similarity function for GSM8K and AQUA-RAT should probably include the operator characters. If a reasoning step contains 48*2=24 and the golden step was 48/2=24 the current evaluation will consider the predicted step correct.\n\n**Weakness (W2)**: The paper should include previous best performance on each individual dataset. This can be done by adding a “best model” line to the Answer Accuracy section of Table2, and detailing in the text of the paper or the caption of the table which ‘best’ model refers to for each of the dataset.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "P6am4pfXcfL", "reviewer": "Reviewer_xxyE", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper presents STREET, a multi-task benchmark for structured reasoning and explanations in NLP.  \n* STREET is composed of 5 existing datasets: ARC, SCONE, GSM8K, AQUA-RAT and AR-LSAT. \n* The authors introduced a unified reasoning formulation involving textual logical units (TLUs) and reasoning graphs.\n* The authors used expert annotation or automatic programatic annotation to annotate the reasoning process on a subset of the 5 datasets mentioned above. \n* The authors introduce three metrics (answer accuracy, reasoning graph accuracy, and reasoning graph similarity) to evaluate a system trained on STREET.\n\nEmpirically, the authors evaluate T5-large fine-tuning and GPT-3 in-context few-shot learning performance on STREET, and explain errors on a per-dataset basis.", "summary_of_the_review": "The reasoning formalism and resources introduced in the paper will be useful to the research community. \nHowever, the paper could benefit from more detailed empirical analysis on the proposed benchmark. \nAlso, some experiment settings and designs need to be further justified or explained (e.g., what's the motivation of multi-tasking?)", "strength_and_weaknesses": "Strength:\n* The paper studies an important problem: reasoning capabilities when answering complex questions.\n* The STREET benchmark, especially the reasoning graphs that are newly annotated, is a useful resource for studying structured reasoning and explanations. (Thanks to the authors for these huge efforts!)\n* Paper is written clearly and is easy to follow. \n\nWeaknesses:\n* Lack of comparisons and justifications\n  * Since multi-tasking and a unified reasoning formulation is one of the main contribution, it is necessary to include non-multitask performance of the five datasets in order to justify whether multi-tasking is useful.\n  * To justify the proposed unified reasoning formulation is useful, it is necessary to include a multi-task baseline with no reasoning and proofs. \n  * To justify that the proposed reasoning graph is more flexible, it is necessary to compare performance (on ARC, where annotations are available for both methods) of reasoning graph and entailment tree. Also please consider including examples that entailment trees are not able to represent but reasoning graphs are.\n* Findings are mostly comparing performance on the surface-level. \n  * In sec 4.2.1, error analysis is conducted on each dataset _separately_. As a multi-task benchmark, it is important to show what is lacking from current best methods with a holistic view, and what are the potential directions for future research.\n  * I will be very interested in a case study on reasoning graphs generated by the models. How often do they generate a valid reasoning graph in the output?\n  * I will also be very interested in quantifying performance by grouping test set with reasoning steps. Does the model struggle more on test cases with more reasoning steps?\n* More details needed on annotation process\n  * Since the resource of reasoning graphs is one main contribution of the paper, it is important to provide more information about how expert annotators are selected, whether inter-annotator agreement is measured (as mentioned, \"there are multiple ways one can explain the answer\"). \n  * This is less important but please provide more information on the user-interface mentioned in Sec 2.3.\n\nQuestions:\nI have some questions regarding some choices in STREET.\n* In Table 3, different similarity function is used for different dataset. I suspect that extracting and comparing numbers or using BLEURT > 0.25 may be over-simplified, but I may be wrong. Do you observe any false negative/positives? How often does this happen?\n* Is there any reason that an adapted version of HotpotQA (e.g., using the gold context sentences, so that information needed to answer the question is present) is not included in STREET. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: notable-top-25%