LogiCoT: Logical Chain-of-Thought Instruction Tuning
Authors: Hanmeng Liu
Zhiyang Teng
Leyang Cui
Chaoli Zhang
Qiji Zhou
Yue Zhang
Venue: EMNLP 2023 Findings (2023)
Keywords: instruction tuning
chain-of-thought
large language model
logical reasoning
GPT-4

Abstract: Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. 
Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks.
To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.

LogiCoT: Logical Chain-of-Thought Instruction Tuning
Hanmeng Liu Zhejiang University 1 Liuhanmen@ezju.cm edu. cn
Zhiyang Teng"
Leyang Cui
Nanyang Technological University
Tencent Al lab
zhiyang. tengêntu. sg
leyangcui@tencent.com
Chaoli Zhang
Qiji Zhou and Yue Zhang Westlake University
Alibaba Group
zcl@alibaba inc. com
{zhouqiji, JhangyueJJwwesttie edu. cn
Abstract
<Mocel: GPT4>>
Inltudioo:
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thouuht rea- soning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhanc- ing the general proficiency of models. These instructions enable the model to achieve per- formance comparable GPT-3.5 general tasks like open-domain text generation and paraphrasing. However, they fall short of help- ing the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction- tunning dataset for Logical Chain-of-Thouglee reasoning with GPT-4. We elaborate on the process of harvesting instruc- tions for prompting GPT-4 generate chain- of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
Consider the fallowing premises. Jessica is playing game and only if cloudy, it lane, then Jessica playing game. If Jessica plays a game, then It cluudy Can we infor the following from them? we can, name the inference rule being used.
fact
late does imply that is dloudyy
Example output: From the fact Jossica playing a game only if dloudy can infer that if cloudy, then Jessi playing a game via bicend eliminatio.
From the fact that if is cloudy. then Jossios playing a game, and that If Jessice plays game, then it dlouty we can infer that is cloudy il and only if Jessica playing game via bioonditional introduction.
From the fact that it cloudy and only If Jossicc is playing game we can then cloudy biconditiona elimination. Jessica playing game, and cloudy via transibvity, which contradi andiiaiio The fact late does imply cloudy.
infor that Jessica that doauty
Finall, from Jessica plays game,
Therefore, the ansoer GPT 1-1 reasoning step by step:
Glvan
is playing game only dlaudy. (Bicordliooal)
Jessica playing a game. (Candiional)
3. Jessicca plays game, then il ckouty (Conditoona))
We can break down the biconditionl in premize via Bicondiional Elimination:
ta. If is cloudy, than Jessica is playing a gama.
1b. if Jessina is playing game, then is cloudy.
Premise 1b is the same premize 3.
Now, from premise 2 (If it laso, then Jossica is playing game) and promiso tb (if Jessica playing a game, then it cloudy), we can infer via Hypathhticall Syllgiam: dlauty.
1 Introduction
it inference sentraditt the input claim fast is late does imply input claim from provided
Instruction tuning Large Language Models (LLMs) has become a popular paradigm for Natural Lan- guage Processing (NLP) in recent years (Ouyang et al., 2022; Sun et al., 2022). prominent line of research is the development of OpenAl's ChatGPT and GPT-4 (OpenAI, 2023). LLMs demonstrate multi-step chain-of-thought (CoT) reasoning abil- ity with proper prompting (Kojima et al., 2022; Huang and Chang, 2022). CoT instruction tuning has drawn attention for its potential to encourage complex, step-by-step reasoning. For example, Wei et al. (2023) and Kojima et al. (2022) have demon- strated the ability of LLMS to generate a coherent sequence of reasoning steps leading to the final an- swer through CoT prompting. Moreover, ChatGPT and GPT-4 have shown remarkable zero-shot com- plex reasoning abilities on several logical reasoning datasets (Liu et al., 2023a).
that clouyy" Therofore, cannor pramises. Henoe, the answer
Figure 1: A showcase of using GPT-4 and existing inference data to generate CoT rationales for logical reasoning.
and instruction engineering, a process that has, thus far, been largely kept private. Recent research en- deavours have begun to explore the distillation of the instruction data using self-instruct techniques (Wang et al., 2022; Peng et al., 2023), where GPT-3 or GPT-4 are used to generate instroction-folloiinng examples. This technique represents a promising avenue for reducing the human labour involved in instruction tuning, offering a more economical way to produce community models trained with instruc- tional data. A paradigmatic example is the pipeline by Wang et al. (2022) for cultivating instruction data, where initial instructions are authored by humans, and LLMs are then used extend this instruction data. This pipeline has been used to produce multiple open-sourced, instruction sinduc
Yet, developing such proprietary models as GPT- 4 and ChatGPT often necessitates intensive data
Coorresponding Authors

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models, such as Alpaca (Taori et al., 2023) and Vi- cuna (Chiang et al., 2023). The instruction tuning data share a common pool instructions and fixed task templates. However, the scope of these instruc- tions is limited and does not encapsulate diverse, complex reasoning scenarios such as multi-step logical reasoning.
matical CoT reasoning, and generating code snip- pets (Gao al., 2022; Liu et al., 2023b; Fu et al., 2023). By applying a similar specialization ap- proach to the broader and more complex domain of logical reasoning, we aim to bring these capabilities into the mainstream, furthering the development of Al systems with advanced logical reasoning skills.
Logical reasoning is crucial in human cognition, embodying the ability to infer conclusions based structured progression of premises. The dearth of such abilities in community models (Liu al.. 2023a) presents a significant gap, inhibiting the development of open LLMs with strong chain rea- soning. While GPT-4 has demonstrated its ability to produce high-quality CoT reasoning output, the potential of generating CoT instruction tuning data using this model remains largely unexplored. The need to cover more diverse and complex reason- ing scenarios, particularly multi-step logical rea- soning, represents a significant gap the current instruction-tuning landscape.
We release both the training data and model weights.
Related Work
Instruction tuning LLMs. Instruction-tuning of Large Language Models (LLMs) has become a thriving research area in Natural Language Process- ing (NLP), aiming enable zero-shot generaliza- tion on unseen tasks (Zhong al.. 2021; Ouyang et al., 2022; Wei et al., 2022). This involves fine- tuning LLMs to perform diverse tasks by following a set of instructions, making the task source an es- sential component of instruction tuning (Longpre et al., 2023). Most existing instruction tuning meth- ods rely heavily on human-crowdsourced tasks or model -eneer-geeerated tasks. Human tasks originate from variety of sources, such as TO (Sanh al., 2022), FLAN (Wei al., 2022), and NaturalInstructions (Mishra et al., 2022). These tasks, although high-quality, rely on substantial hu- man effort and are often limited in quantity. contrast, model-generated tasks involve leveraging powerful language model, such as GPT-3 and GPT-4, to generate a diverse set of instructions, task inputs, and task outputs based on seed set (Wang al., 2022; Peng al., 2023). We seek leverage GPT-4's chain-of-though reasoning capa- bilities in instruction tuning. By introducing the LogiCoT dataset and incorporating symbolic tasks, we aim to advance the quality and scalability of instruction-followin data and thereby enhance the overall performance of instruction-tuned LLMs.
We aim address this gap by scaling up the instruction set (Chung et al., 2022), paving the way for more nuanced and sophisticated instruction- tuned models. To this end, we introduce LogiCoT, a chain-of-thoughh (CoT) instruction-tuning dataset designed explicitly for logical reasoning. Our ap- proach involves repurposing existing logical rea- soning datasets, constructing logical reasoning CoT instructions from these resources, and leveraging the capabilities of GPT-4 to generate high-quality outputs. The resulting instruction data features both symbolic reasoning and multi-step CoT reasoning. providing a comprehensive and nuanced resource for enhancing the logical reasoning abilities of Al models.
Human-crowdsoune crowdsourced
Using LogiCoT, we fine-tune LLaMA-7b (Tou- vron et al., 2023) model with instruction-tuning scheme, resulting small-sized instruction-tumed open model. Results on both logical reasoning benchmarks and general human-centric bench- marks indicate remarkable performance elevations compared to state-of-the-ar instruction-tum -tuneddon-tddddddee mod- els, showing the promise of leveraging complex CoT instruction data in the instruction-tunie tuniing pro- cess of LLMS.
PINTO (Wang al., 2023) offers two-fold approach to elucidate the reasoning process of large language models (LMs). The core innova- tion lies in its prompt-based learning combined with counterfactual regularization to ensure faith- fulness in the reasoning over generated rationales. While PINTO excels in achieving superior perfor- mance across in-distribution and out-of distri-iittiitttonn test sets and ensuring that rationales are faithful, its primary motivation is different from LogiCoT. Whereas PINTO emphasizes rationale transparency and faithfulness, LogiCoT is geared towards using
Our work is in line with recent research indicat- ing that smaller language models can achieve com- petitive multi-step reasoning abilities when special- ized on targeted COT tasks. Examples of these tasks include the execution of SQL commands, mathe-

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logic-related chain-of-thoughh data achieve ad- vanced logical reasoning skills.
mar, 2019). Logical reasoning tasks can require multi-step. complex reasoning, which makes them an ideal target for CoT instruction tuning. To our knowledge, we are the first to consider this method for logical reasoning, making use of rich reasoning chain data finetuning LLMs, increasing their task performance.
Teaching small language models to reason is an- other research direction (Magister et al., 2023; Li et al., 2023; Hsich et al., 2023) that underscores the significance of knowledge distillation in port- ing reasoning capabilities from mammoth mod- els to their relatively compact counterparts. Cen- tral to this approach is the fine-tuning of smaller models on the chain-of-thought rationales spawned by larger models. Such strategy has demon- strated marked improvements in various reasoning datasets. Yet, this is where LogiCoT diverges. Our approach uniquely employs logical reasoning ratio- nales and capitalizes on the generative prowess of GPT-4.
Dataset
The data construction of LogiCoT a multistage process that uses GPT-4 as teaching assistant, which is illustrated in Figure 1. We first establish a foundation by choosing suitable seeding data with gold output and optional CoT reasoning chains. With seeding data in place, we proceed formulate instructions. The process involves translating the intended tasks into clear, unambiguous prompts that clicit GPT-4's capacity for logical reasoning rationales. We then combine the seeding data and the corresponding instructions and feed them into GPT-4 to generate responses. GPT-4's output is guided by the gold label and the reasoning chain. We use both the gold output and GPT-4 response as our instruction data. Figure illustrates this process.
In essence, while the broader arena is populated with innovative methodologies aiming to amplify the reasoning prowess of LMs, LogiCoT distin- guíshes itself by synergizing logical reasoning with the generative might of GPT-4, setting a precedent in logical reasoning tasks.
Chain-of-thought rationales. Large Language Models (LLMs) can conduct complex reasoning tasks by generating intermediate reasoning steps through process called chain-of-thought (CoT) prompting (Wei et al., 2023). Zero-Shot-CooT prompting uses simple instruction (like "Let's think step by step") elicit step-by-step reason- ing before answering a question. LLMs have ex- hibited reasonable zero-shot reasoning capabili- ties, generating outputs that inherently reflect CoT reasoning (Zhou et al., 2023). This notion in- spired researchers to use self-generated rationales for demonstrations. In particular, Zelikman et al. (2022) demonstrated the practicality of using LLMs generate rationales. They prompted GPT-J (Wang and Komatsuzaki, 2021) to generate ratio- nales and then selected the ones leading to the cor- rect answer. We adopt this method for data collec- tion using GPT-4. Our approach, however, focuses on complex logical reasoning scenarios using ques- tions with annotated answers.
3.1 Seminal Data Selection
Sclecting the seminal data for CoT instruction tun- ing of logical reasoning models involves choos- ing high-quality datasets that adequately cover the range of skills required for logical reasoning. The datasets should present challenges representing real-world logical reasoning tasks and be designed to support CoT instruction tuning. Below are the seminal instruction data we choose:
LOGICINFERENCE (Ontanon et al., 2022) is synthetically generated sequence-tor ceaticeett-sqquenee dataset teaching models to perform logical infer- ence using propositional logic and subset of first- order logic. Figure 2 in Appendix A shows example. The input is a question, varying in types of problems ranging from language-to-logic trans- lation to multi-step inference chains. The output provides the answer, including the reasoning chain to generate it. The output, in some cases, even pro- vides the name of the inference rule used in each step.
Logical reasoning. Logical reasoning is a key aspect of human cognition and a critical capability for AI systems. Researchers have been exploring various approaches to achieve this goal, including rule-based methods and symbolic systems (Mac- Cartney and Manning, 2007), fine-tuning large lan- guage models (Wang et al., 2018), and combining both neural and symbolic approaches (Li and Sriku-
EntailmentBank (Dalvi et al., 2021) is an open- domain question answering data with rationales as an entailment tree. It uses multiple-choice ques- tions from grade school science. The entailment

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trees are constructed with human labour. Figure in Appendix A shows an example. Entailmen(Bank provides a unique combination of open-domain question answering and logical reasoning in a for- mat that closely aligns with our instruction tuning objectives. The structure of its rationales, presented entailment trees, gives our model the opportu- nity to learn from and adapt to complex logical relationships in a controlled environment.
levels of abstraction and complexity, with both natural language and symbolic language. To our knowledge, no similar instruction types exist other cnarucction-oolloiinn data.
We classify the instruction types into general inference (Section 3.2. and multi-choice reading comprehension (Section 3.2.2) tasks.
3.2.1 General Inference Task
FOLIO (Han et al., 2022) is an open-domain, logically complex and diverse dataset equipped with first-order logic (FOL) annotations. Fig- ure 4 in Appendix A shows an example. What sets FOLIO apart is the parallel FOL. annota- tions for each premise and conclusion, which are automatically verified by an FOL inference en- gine. This aspect provides a clear, precise stan- dard for logical reasoning. In addition, the human- annotated nature of the dataset ensures high-quality data input. This dataset can be easily converted into sequence-to-sequeeee soqueneeece structure, serving as www.uncciiooooloooiini data for symbolic logic rea- soning.
This category includes instruction types that de- mand general reasoning and inferential skills, often involving an understanding of logical structures and principles. The model may need to perform operations such as translating natural language formal logic, predicting possible inferences from given premises, or tracing inference chains. These tasks are designed to enhance the model's ability to think critically and logically, without relying too heavily on specific contexts or domain knowledge.
Table 7 in Appendix B shows the instruction types for general inference. An example offered to illustrate each instruction type.
Language to Logic: This instruction involves translation from natural language into a more for- mal logical notation. It presents a foundational task of understanding and interpreting logical state- ments expressed in natural language and converting them into formalized logical representation.
ReClor et al., 2020) and LogiQA (Liu et al., 2020) are datasets derived from verbal reasoning cxaminations, demanding various types of logical reasoning for answering multi-choice questions. Figure 5 in Appendix shows an example from the ReClor data. These datasets are especially valuable that they represent realistic human reasoning processes. Further, the real-world nature of the questions in these tests, which often require a mix of common sense and logical reasoning, ensures that the model is trained to tackle problems with varying degrees of complexity. We use the training set of the two datasets, keeping the test set out of the instruction tuning data. Specifically, we use the Chinese version of the LogiQA dataset.
One-Step Inference: In this case, the model is presented with a set of premises and tasked with predicting all the potential inferences that can be derived from them in a single step. This type of instruction encourages the model to exercise de- ductive reasoning based on the provided premises. The premises and inferences can be in natural lan- guage symbolic language. The latter encourages precise and abstract reasoning, while the former context simulates real-world language use scenar- ios.
These seminal datasets for CoT instruction tun- ing in GPT-4 offer balanced, comprehensive, and challenging training environment. This approach ensures that the model gains exposure to a broad range logical reasoning tasks, thus enhancing its ability to effectively handle similar tasks in real- world applications.
This instruction type takes logical reasoning a step further by requiring the model to establish whether potential inference can be proven from set of premises. The model must then provide the chain of reasoning leading to the answer. This type encourages deeper logical reasoning and the ability to construct logical argu- ments. The examples are either crafted in symbolic language or natural language.
Inference Chains:
3.2 Instruction Types
We consider different types of instructions for in- structing language models in various aspects of logical reasoning. Each type is designed to engage the model with logical inference tasks at differ-
3.2.2 Reading Comprehension Tasks
Machine reading comprehension (MRC) is the go- to task for testing LLMs' reasoning ability, where a

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to model is given a passage and a question and asked find the answer. This category involves tasks that require a deep understanding of given text, often demanding that the model identifies, extracts, or infers information from the text. The model might be asked to resolve situation described in the text, to pinpoint flaw in argument presented, to identify information that would either strengthen weaken argument.
Task
Origin
Size
Language to Logic One One-Step Inference Inference Chain Muli--hhiice
LOGICINFERENCE FOLIO
13,206
LOGICINFERENCE & FOLIO
23,943
LogicINPERENCE & EntailmenBank
26,228
LogiQA & ReClor
5,606
Table 1: Data statisties.
3,4 Data Statistics and Quality Assessment
We collected 68,983 data instances in total. We illustrate their distribution in Table 1. Table 9 in Appendix D shows the root verb-noun pairs of our instruction set. As can be seen from the figure, the instructions reasoning-centeres Figure 6 in Appendix shows the root verb-noun pairs of GPT-4 responses, which cover a wide range of sce- narios both in everyday language and symbolic language.
Table 8 in Appendix shows the instruction types and running examples for logical reading comprehension.
The distinctiveness of these instruction types lies in their combination of logical reasoning tasks with natural language processing, providing a ro- bust framework for training language models in logic-infused language understanding and gener- ation. This comprehensive approach is unique to this data generation scheme and offers innova- tive pathway for improving the logical reasoning capabilities large language models.
Together, these tasks cover various log- ical reasoning abilities. We release our LogiCoT instruction tuning dataset at
facilitate future research.
Human Evaluation of Dataset Quality We con- ducted comprehensive human evaluation of our generated reasoning chains. A random subset of 200 reasoning chains was selected. These chains were evaluated by 3 domain professional annota- tors using four key metrics: Relevance (Does the chain directly relate to the question? ), Coherence (Is the chain logically consistent? ), Completeness (Does it offer full explanation for the reasoning?) and Faithfulness Is the reasoning factual and not fabricating details? ). Each reasoning chain was rated on a scale from (poor) to 5 (excellent) for each metric.
3.3 Data Collection
We are granted early access to GPT-4 API, which provides a unique opportunity to leverage the ad- vanced capabilities of this model for generating high-quality rationales. By using the API, pass the logical reasoning tasks derived from our seminal datasets (LogiQA, ReClor, LOGICIN- FERENCE, and FOLIO) to GPT-4 and collect the model's responses. The pseudo-code in Ap- pendix C exemplifies the process for GPT-4 ratio- nales collection.
The general inference task is converted from LOGICINFERENCE, EntailmentBankk, and FOLIO. The three datasets are particularly valuable this context as they offer data instances accompanied by precise reasoning chains. These reasoning pro- cesses, whether derived from rules or written by humans, serve concrete examples for GPT-4 to learn from. Golden CoT output datasets provide GPT-4 with invaluable references, optimizing gen- eration quality.
The reasoning chains achieved average of 4.9 for Relevance, 4.7 for Coherence, 4.5 for Completeness, and 4.5 for Faithfulness. The inter-annotator agreement, measured using Co- hen's Kappa, was 0.87, indicating strong agreement among the annotators.
The human evaluation underscores the quality and reliability of the reasoning chains generated by our model. While the scores were high for rele- vance, we acknowledge room for improvement faithfulness, and future work will aim to refine the generation process to address this.
The machine reading comprehension task is de- rived from LogiQA and ReClor. These two datasets are not sequence-ter to--uuuecc--seeeeeee they do not offer step-by-step reasoning processes. However, GPT-4 scores well on these two datasets (Liu et al., 2023a) without in-context examples, GPT-4's commend- able performance on these datasets assures quality generation.
4 Experiments
We use LogiCoT to fine-tune an open L.LM and test the resulting model on logical reasoning and general human-centric benchmarks.

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Datasel
LQ
zQ
RC
LQ ood
CT
TN
Overall
Size
1572
1594
500
230
1354
805
35891
10071
52017
LLaMA-7b-base
18.04
19.06
15.83
13,91
20.25
32.40
25.20
37.35
20.22
19.31
26.35
17.81
17.98
18.41
24.10
32.26
41,91
24.78
Falcon-40b-instruct
23.21
19.77
26.77
12.70
17.33
16.13
28.49
44.66
23.63
LLaMA-7b-logicol
50.25
32.77
57.60
16,96
38.79
35.68
35.44
58.05
40.69
Table 2: LogiEval results. LQ: LogiQA 2.0, RC: ReClor, AL: AR-LSAT, CT: ConTRoL, HL: HELP, TN: TaX All results are in %, the best ones are in bold, and the second best ones are underline.
4.1 Models
Datasct
Target
LogQA 2.0 test
4-way multi-choice
LLaMA (Touvron et al., 2023) is an open-sourced LLM developed by Meta. We adopt LLaMA-7b (Touvron et al., 2023) as the base model for instruc- tion tuming.
LugiA 2.0 zh lest
4-way multi-choice
ReClor
4-way multi-choice
AR-LSAT
5-way multi-choice
LogiQA 2.0 OOD
4-way multi-choice
ConTRoL test
C.N
HELP test
E, C.
We shuffle the collected data to ensure the base model encounters broad range of data character- isties throughout the instruction tuning phase. This approach bolsters the model's potential to gener- alize its learning to a variety of unseen situations. Following (Taori et al., 2023), we adopted their run- ning scripts and set the learning rate to 2e-5, and the batch size to 4. We trained 2 epochs. Our exper- imental setup incorporated two A A100 GPUs, run- ning the instruction tuning process over 2 epochs. We use the Microsoft deepspeed library to accel- erate the training process. The training takes 4 days. We relcase our instruction-tunee LLaMA model as LLLLMM--b--oogcooo..
TaxiNLI test
C.
Table 3:
Data statistics. ("E" refers "entailment"; "C" refers to "contradiction"; "N" refers to "neutral".)
For the assessment of our model's logical rea- soning capabilities, we select the LogiEval bench- mark (Liu et al., 2023a) suite, which is expressly designed with an instruct- prompting style rigorously test the logical rea- soning abilities of large language models (LLMs). Each dataset instance has integrated instructions which ensures the LLM comprehends the desired format and response requirements. Table illus- trates the 8 datasets and their task formats in the LogiEval benchmark.
primary testing
To compare model performance with other open- open-sourced instruction-turag LLMs, choose two top models from the Open LLM Leaderboard 2, namely -LLMA--3b--sperres instruct. The former a LLaMA-30b model tuned on the SuperCOT dataset, 3 which is also a CoT dataset. The latter is an instruction-tuned Falcon- 40b model (Almazrouei et al., 2023) tuned on mix of datasets.
The LogiEval benchmark consists of two types of tasks. Firstly, encompasses multi-choice read- ing comprehension tasks that examine the model's 's ability to interpret, analyze, and make decisions based on given texts. Secondly, it includes natural language inference tasks, presenting opportunity for the model to demonstrate its understanding of logic and coherence in language constructs. This combination of tasks within LogiEval provides ex- tensive coverage of logical reasoning tasks, making an idcal choice for our testing purposes.
and Falcon-40b-
4.2 Benchmarks
Logical reasoning The purpose of this exercise is to directly measure how effectively our model has incorporated the logical reasoning skills Icarned during the instruction tuning phase. This evaluation helps us understand the strength of our model in the domain it was explicitly tuned for.
General Tasks We further go beyond the con- fines of logical reasoning tasks. We probe the model's capabilities on general human-centric lan- guage model benchmarks. This broad-hased testing strategy is aimed at gauging the generalizability our model. It enables us to measure the perfor- mance of our model on tasks that span a wider array of human language processing tasks, beyond its specific training focus.
/tttps://githhee com/microsofi htt./iicrssotteeppeeee / aace. co/ co/spaacss/uuggingge aceti4/ boadd
apen_llm_leade board
httts:://huggnngffte
SuperCOT-dataset
To conduct a comprehensive evaluation of our model, we employ the Massive Multitask Language
hattps://hugging: calee.co/tiie co/tiiuue/
falcon-40b-1 Dalcon-4bb-instruce

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Task
LLaMA-7b-base
LLaMA-7D-logicot
accuracy, the second best compared 17.98% ac- curacy of the LLaMA-30b-superro model. Notice that the AR-LSAT dataset a 5-way classification task, contrast to our instruction tuning data's s 4-way multi-choice reading comprehension.
Math
25.1
29.0
Health
34.0
42,9
Physics
29.4
34.2
Business
34.8
57.2
Biology
32.4
46.5
Chemistry
25.4
Computer science
28.2
The results on the MMLU benchmark are shown in Table 4. Overall, the average accuracy of LLaMA-7b-logicot is 43.3%, compared to the 31.8% accuracy of the LLaMA-7b base model. The improvement salient given that our model has not been specifically tuned on the tested subjects. In detail, our model surpasses LLaMA-7b- every subject. The best improvements are seen business, computer science, economics, etc., with over 10 points accuracy boost.
Ecomomics
Engineering
25.5
32.4
Philosophy
30.8
37.6
Other
40.4
50.8
History
38.2
55.2
Geography
28.8
52.5
Politics
32.1
53.4
Psychology
33,2
50,9
Culture
37.3
on
STEM
27.6
34.8
Humanities
31.6
41.8
Social Sciences
31.5
49.2
Other (misc.)
36.4
47.7
Average
31.8
43.3
4,4 Discussion
Table 4: MMLU results for LLaMA-7b base and logicot.
4.4.1
Compare to ChatGPT and GPT-4
All results are in %. the best results are in bold.
As our model is tuned on instruction data distilled from GPT-4, we therefore compare their perfor- mance the LogiEval benchmark with model. The results are shown in Tabel 5. LLaMA-7b- logicot performs on par with ChatGPT on datasets like LogiQA 2.0 and AR-LSAT. Particularly, on ReClor, LogiQA OOD, and TaxiNLI, our model outperforms ChatGPT by a small margin. Given that LLaMA-7b-logicol is a small model with only 7 billion parameters, the results are quite inspiring, However, the Chinese version of LogiQA, and ConTRoL, the performance of our model lags ChatGPT by 20 points. This performance discrep- ancy suggests that our model may exhibit weak- nesses in handling Chinese corpus and passage- level texts.
Understanding (MMLU) benchmark (Hendrycks et al., 2021). This benchmark assesses a large lan- guage model's 's capabilities across a diverse range of domains, spanning from foundational knowl- edge sectors such as mathematics and history, to more specialized fields like law and ethics. We remain consistent with the protocols from previous evaluations.
Our evaluation approach emphasizes exact matching. We instruct LLMs to generate the pre- cise answer in accordance with the given prompt. The first token of the output is deemed the final answer, which tests the LLM's proficiency in fol- lowing instructions. The instruction prompts for the two tasks are detailed in Appendix E.
GPT-4 outperforms LLaMA-7b-logico on each dataset, giving the best performance on the Lo- giEval benchmark except for the ConTRoL dataset, where the accuracy is lower than ChatGPT by 2 points. The results show that there is still gap between our model and the best proprietary model.
4.3 Results
The results on the LogiEval benchmark are shown in Table 2. With the varies of different data sizes, we represent the data size of each incorporated dataset in the second row. We report the perfor- mance of LLaMA-7b base model one of the baselines.
4.4.2 Ablation Study
To better understand the specific contributions of various reasoning types employed in our instruction-tuning process, we conduct an abla- tion study. This involves evaluating model per- formance by selectively removing reasoning type time and observing the resultant change in the model's ability handle logical reasoning tasks.
Compared to the LLaMA-7b base model, LLaMA-7b-logicn surpasses the base model on all datasets. Specifically, I.LaMA-7b-logien performs much better the multi-choice reading compre- hension task, except for the AR-LSAT data, where all four models give relatively low performance. Compared the two leading open-sourced models, our model gives the best performance except for the AR-LSAT data, where our model yields 16.96%
The ablation study uses the instruction canuee LLaMA-7b model. For each reasoning type, we

---

LQQ
IQQ
CT
Overall
LLaMA-7b-logicot
50.25
32.77
57.60
16.96
38.79
35.68
35.44
58.05
40.69
ChatGPT
52.37
$3.18
$7.38
20.42
38.44
58.45
42.13
57.30
47.46
GPT4
72.25
70.56
87.20
33.48
58.49
56.40
46.01
60.08
60.56
Table 5: ChatGPT and GPT-4 LogilEval. LQ: LogiQA 2.0, RC: ReClor, AZ:: AR-LSAT, CT: ConTRoL, III:
HELP, TN: TaxiNLI All results are in %, the best ones are in bold, and the second best ones are in underline.
Removed
LogiEval
MMLU
4.4.3 Case Study
None (Full data)
40.7
43.3
Language Logic
32.4
38.5
To further understand the reasoning ability of LLaMA-7b-logicol, we provide case study exam- ples in this section. Rather than generating single output token, we ask the model to generate both the answer and rationales by prompting it with "An- swer and reason:
One-step Inference
38.1
37.7
Inference Chain
30.8
35.0
Multi-choice
35.6
30.9
Table 6: Ablation study by removing reasoning types. Results in accuracy (%)
train the model without that specific type while retaining the others. This is performed iteratively for all reasoning types.
Figure in Appendix G shows an example of LLLMA--b-logiie solving the MRC task correctly. In this case, our model is asked to find the option that strengthens the given argument. The key idea in this example is that to save costs, the company needs to abandon rule that leads to higher costs. Our model successfully identifies option A as proof of the costly rule. It also generates coherent and convincing explanation.
We report the overall average score of LogiEval and MMLU, respectively, to provide a comprehen- sive understanding of the respective impacts of these reasoning types. The results are shown in Table 6. We elaborate the results of ablating "multi-choice" in Table 10 of Appendix F to give audiences clear view.
Figure 8 in Appendix G shows an example of LLaMA-7b-logicot solving the MRC task incor- rectly. The example is difficult analytical question involving complex logical relations and numerical calculations. The reasoning steps generated by our model are sensible in (1), (2), and (3). However, it came up with new constraints, as in (4), (5), (6), and (11). Moreover, the model does not examine all the options to decide the correct answer. Our model made series of mistakes in this example which led the wrong conclusion.
Language to Logic: Excluding this type led to a decline in performance on the LogiEval dataset. This underscores its significance in mapping lin- guistic constructs to logical expressions, which is foundational for reasoning tasks.
One-step Inference: The drop in accuracy sug- gests that simple inferences play vital role, especially in tasks where direct conclusions are drawn from given premises.
Inference Chain: The model's performance drop on both datasets highlights the importance of chained logical deductions. Tasks that require multi-step reasoning particularly benefit from this type.
Conclusion
We collected LogiCoT, set of CoT instruction- tuning data using GPT-4 through the lens of logi- reasoning tasks. With 70K training instances, LogiCoT offers diverse challenges in logical rea- soning. Using LogiCoT, we instruction-tunes an open-sourced LLaMA-7b model and the experi- ments on our model demonstrate competitive log- ical reasoning and general inference abilities. To our knowledge, are the first to construct instruction-tuning dataset with rich and diverse logical reasoning steps, showing its potential enhance a generative L.M.. Future work includes the integration of our dataset to enhance dialogue- oriented LLMs such as Alpaca.
Multi-choice: Removing multi-choice reason- ing impacted performance on MMLU more than on LogiEval, emphasizing its role in tasks where choosing among alternatives based on logical grounds is essential.
This ablation study reaffirms the unique contri- butions of each reasoning type to the model's per- formance. While all reasoning types contribute to enhancing the model's 's logical understanding, their impacts vary based the nature of tasks and the datasets used. Future research can delve deeper into optimizing instruction-tumie processes based on specific reasoning type requirements of datasets.

---

Acknowledgement
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Ja- cob Steinhardt, 2021. Measuring mathematical prob- lem solving with the math dataset. arXiv preprint arXiv:2103.03874.
We thank all the anonymous reviewers for their constructive suggestions. This publication has em- anated from research conducted with the finan- cial support of the Pioneer and "Leading Goose" R&D Program of Zhejiang under Grant Number 2022SDXHDX0003. Zhiyang Teng is partially supported by CAAI-Huawei MindSpore Open Fund -AADXXJLUU-2021-04650 Yue Zhang and Zhiyang Teng are the corresponding authors.
Cheng- Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen Yu Lee, and Tomas Pfister. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes.
Jie Huang and Kevin
Chang, 2022. To- wards reasoning in large language models: A survey. arXiv preprint arXiv:2212.1044
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Input:
What can be inferred from the following premises in single inference step (ignoring inferences that add new predicates constants)?
David is working and only if it is cloudy. David is not working.
Output:
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David works, then it is cloudy. is cloudy, then David is working. It not cloudy.
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Figure 2: An instance from the LOGICINPERENCE dataset.
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A Datasets Examples
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. GLUE: multi-task benchmark and analysis platform for nat- ural language understanding. In Proceedings of the
We illustrate data examples mentioned in Section 3 here.

---

Context:
NL premises
sent1: leo a kind of constellation
sent2: to be found means to be contained
1. There are six types of wild turkeys: Eastern wild turkey,
around sent4; if something is part of something else then that
revolve
Osceola wild turkey, Gould's wild turkey,
Merriam's wild turkey, Rio Grande wild turkey, and the Ocellated wild turkey.
something else contains that something
sent5: being around something means surrounding that something
Tom is not Eastern wild turkey.
sent6: circle is kind of shape
an Osceola wild turkey.
sent7: revolving around something means orbiting that something
Tom is also not a Gould's wild turkey, or a Merriam's wild turkey, or a Rio Grande wild turkey.
sent8: motion is
event / action
sent9: including means containing
sent10: motion is when moves an object something to direction
5. Tom is a wild turkey.
planets
system orbit
NL Conclusions >> Labels
sent12: approximately means about
A. Tom is an Ocellated wild turkey. True Tom is Eastern wild turkey. False wild turkey. Unknown
sent13: planets orbit stars
something appears
something looks
sent15: throughout
sent16: motion/ / movement means moving / to move
sent17: a constellation contains stars
Premises
years (y) generally used for values between 1 and 14000000000
metric used for measuring time
Ux(WiilTurkkeykk + (Eastem(x) Osceola(x) Goulds(x)
V Merriams(x) v Riogrande(x) v Ocellated(x)))
sent19: similar means in common
2. -(WildTurkey(tom) A Eastern(tom))
system kind system
3. (WildTurkey(tomm) A Osceola(tom))
sent21: moving in circle means moving in a circular mation
sent22: a celestial body travelling around another celestial body
4. WildTurkeytoom) + -(Goulds(tom) v Merriams(tom) V
means
body completes a cycle around
Riogrande(tom)) WildTurkey(tom)
celestial body
sent23: rotation is the circular movement of an object around center / axis
conclusions Labels
sent24: orbit kind regular path
Ocellated(tom) >> True
sent25: the earth revolving around the sun causes stars to appear in different areas the sky at different times of year
Eastern(tom) False
c. WildTurkey(joe))> -> Unknown
Question:
Stars are organized into patterns called constellations. One constellation
Figure 4: An instance from the FOLIO dataset.
Which statement explains why
appears different of the sky throughout the year?
output:
Context:
Earth revolves around
In jurisdictions where use of headlights optional when visibility is good, drivers who use headlights at all times are less likely to be involved in collision than are drivers who use headlights only when visibility poor. Yet Highway Safety Department records show that making mandatory at all times does nothing to reduce overall number of collisions.
Based on the context, we infer the hypothesis: the earth revolving around the sun causes leo to appear in different areas sky at
of year
Reasoning chain: sent1 sent17 int1: leo constellation
containing stars; int1 & sent25 >> hypothesis;
Proof: [BECAUSE] leo
constellation [AND] a constellation contains stars [INFER] int1: lea constellation
headlights
containing stars [BECAUSE] int1 [AND] the earth revolving around the sun causes stars to appear different areas in the sky different
year [INFER] int2: earth revolving around the causes leo appear in different areas in the sky different times of year"
Question: Which one the following, if true, most helps to resolve the apparent discrepancy in the information above?
Options:
Figure 3: An instance from the EntailmentBank dataset.
A. In jurisdictions where use of headlights is optional when visibility good, driver four uses headlights for daytime driving good weather. B. Only very careful drivers use headlights when their use not legally required.
B The Instruction Types and Examples for the MRC Task
C. The jurisdictions where use of headlights is mandatory at all times are those where daytime visibility frequently poor.
The instructions below further cultivate the model's critical thinking and argument analysis skills.
D. law making use of headlights mandatory at all times especially difficult enforce.
Identify the Necessary Claim: This instruction tasks the model to pinpoint the claim that must be true or is required for an argument to work. It is essentially training the model to identify essential assumptions premises in an argument, thus hon- ing its ability understand argument structures. Strengthen an Argument: Under this instruc- tion, the model must identify information that would strengthen an argument. requires the
Answer:
Figure 5: An instance from the ReClor dataset.
model not just understand the argument, but also anticipate what additional information could make the argument more convincing. This helps the model to improve its capability to enhance log-

---

Instiittion
Inpur
Ouiput
untation
Trunslate the fnllowing inference to legic
All have four sádes
VA (ggaadta)) - FunsShicess))
What inferred from the following
David working bing and only ifil clodd. warkiy.
DrDiid welks,
Dasid woeking.
stop
were antix,
sleeping con interted the universal
1,9, x au attis a'a if 5
biccualionall ciuiuaiio
can infernel from the following prentions in etngle inference step? Name the inference rule being used
sieeps
There is kowt ore y which if were aetid, then 9 stepping can inferml via the extsennial Nccndrional elimination rale.
There in leae one for which 9 antir and only X will Seep.
Fix all 2 deeps, lien x uniii vae be infered vis the usiver biccnditional cimiieii rule
There is loed une for which il 2 sleape, than x_9 as artos cas be iniined via the eccarenial hicondrional sliminttion mile
Caneike the fulloringg
From the fas that if mine, then Paraen curaese, and the Isancia camasce, than infor if mine, Chadlee raling plane
Dories then
Choriee taking cassiliitty
curkes, Charles
From the thes rains, Charles taking plane, is a'a Chaslos laking
Charles
Cousiler followiug them? infermnee being
thes Patricia
Iurles plane,
hiccedenenal
Trom the
takes plone, then
not reading
reading book.
coaling
can iniex Charlos sor taking plane sunles tillee.
Can infer following same the infermnce nie being used.
con.
Hiodlly from fact thee naminng if and only Charle taking planc. and that Charies is soe sicing plane we can inter that is nining via nicceditonal eliminatiin Therefie. lhe asswer yos
It voiiiig
Table 7: The instruction types and illustrating examples for generating general inference CoT rationales.
ical arguments.
This type is the oppo- site of Strengthen an Argument mentioned above. Here, the model is tasked with identifying informa- tion that would weaken an argument. This helps the model develop a nuanced understanding of argu- ment structures and cultivate the ability to critique and dismantle arguments.
Weaken an Argument:
Resolve a Situation: This instruction requires the model to identify information that would ex- plain or resolve situation. This is about identify- ing missing information or finding potential solu- tions to problem, further expanding the model's s problem-solving capabilities.
Figure 6: Root verb-noun pairs of GPT-4 generated responses. The inner circle of the plot contains the most frequent root verbs of GPT-4 outputs, and the outer circle displays the direct noun objects.
Identify Flaw in Arguments Reasoning: In this type, the model must identify a flaw in an argument's reasoning. This instruction cultivates the model's critical thinking skills, as it needs to scrutinize the argument and pinpoint any logical fallacies or inconsistencies.
'instruction': instruction, 'input": input,
3: output wwwnai. Chh.aCCCommletiio.creeeeee model = "gpt-4",
By incorporating these instruction types, the data generation scheme is broadened to more complex logical reasoning tasks, particularly in the realm of argumentation and critical thinking, thereby en- hancing the language model's ability to engage with more sophisticated and nuanced logical rea- soning tasks.
messages [[ ('role': 'system', 'content': instruction), ('role': 'user', 'content': input).
('role': 'assistant', 'content': cot
D Data Statistics
C Pseudo-Code for GPT-4 Generation
E Prompting Instruction-Tunel Models
1: prompt_winn
LogiEval is an prrmmptionn-rompttnng style bench- mark where each dataset instance has integrated instructions. This ensures the LLM comprehends the desired format and response requirements.
'instruction': instruction, 'input': input, 'output': output,
Below is the LogiEval instruction for the reading comprehension task:
2: crmmpt_wihouutccot

---

Ligal
Verb
Noun object
Count
Ioan sox prieed ooe undi pipc: tnlomarddi cvry
consider translate
premise
24388
a.le. Thl ansoo of davv au cos Unvaa Lille
inference
13206
Matth IN
<lac que bong concppne to undeociity hodis
Aexncc 31 Altas allef the pacoo particec the Brooe the poisgge sarmu hell ons dirtt to qoa your pedeepper and eetrr acikl puper bu delirn dhanii and d, 1nd years uetggnl ou deving
answer
question
1840
to
an dedeed in dey puo yoay
Table 9: Root verb-noun pairs of instructions.
diisked
thtyon
af book tialis and pliad
You will be presented with a pas- sage and a question about that passage. There are four options to be chosen from, you need to choose the only correct option to answer that question. If the first option is right, you generate the answer A', if the second option is right, you generate the answer 'B', if the third option is right, you generate the answer 'C', if the fourth option is right, you generate the answer 'D', if the fifth option is right, you generate the answer 'E'. Read the question and options thoroughly and select the correct an- swer from the four answer labels. Read the passage thoroughly to ensure you know what the passage entails.
Instructions:
(orry can voacey
Anxnce A the then inl thar
Tapkiio oun cambec towcalthal
fnewo bis ual by tree people
Bnowe the arg gank destlee soe
Musth
dedeecdy nonigg in fntlvvl That
lounse
a bechcoo inllvrt The witch vis
umegten
saveghiee
then foaed sia Relee lamaaae
de bel
Laudo snllo of ricees bhac lifles likig sined.
deve day ulte lokig aneed kolls, Thrr
how
Alta moues day Hiuey was diada:
talk ln du maka decar bat
and
conour cromm conicrs deteced oelo tephiies ver
spdioe on thee
pouste pouple with the informat ofe the an loiking
boe eeeeeeeen speciiuueen up he die sodly maee bnre
salo will and se thee sertins
Quecki Whih tue icloniag. frme,
Bisu and destcoal sone cell coplee bain aimpuqee due
tatrmation
sepon for informatio fanc diggg
Beecca ffrass poogle dirictio boowigg
And the instructions for the NLI task: Instructions: You will be presented with set of facts and rules as premises, and hypothesis about it. You need to decide whether the hypoth- esis is entailed by the premise by choosing one of the following answers: "Yes': The hypothesis follows logically from the information contained in the premise. 'No': The hypothesis is logically false from the information contained in the premise. 'Neutral': It is not possible to determine whether the hypothesis is true or false without further in- formation. Read the passage of information thor- oughly and select the correct answer from the three answer labels. Read the premise thoroughly to ensure you know what the premise entails.
Bt la moding wnm in people oy look be
fur in bannaill laghes iit fir dow oporices fee dulrssen Tak tha creut
hoal suics. caden da or
dustoiicc rigu varkkc an diicci
allonn and alestonii suea
ruploe aevey pers bemmune ne oe mau de broning
ofter pe dy when mey chrig fer aucmsas
tay aver.
du perputtttoon that somppeei rake will declic
conggn of setwrriigg
ind decommie rgeil cse mueck retuempacn it
a siop by siee
dk kons vnia sake callect ta bedku
cood ai loaet the un paliip of
dist cnde
y layt hene pir tee dinin
allof Boie coetie foclos Du Julp Six dess mai mike bedu
loccald Eupi Sis piik is
cartag tee Whidhal de Belesigg tua
A, the aod auta bounis populc dikee
explan nor opuare meveiing vevry in lo the
andd to petteee
The bockaakk bocam pepelar arovyy the cillircs
scola Eitc cualing in acceis coppk
pooeiic the ond yoal
all
wodi
ane teys fio
und enmimatd corinng mnre-
undira
twos has with poait owl ee dalll could
dilbse ollovrd beese mas ma tho
cpacigriosaa y spy de broowing and
wate muse
aptiie imphhe
brids ane
on af un wore bes howts, lkl fost new of
of cow bocini pive prical
la lus
gloca puriid andrd scriially dlagaii childin
aalleeg liberry begeor evret neoe wang
In the case of the MMLU dataset, we remain consistent with the protocols from previous eval- uations. The prompt, "The following are multi- ple choice questions (with answers) about subject. question Answer:", provides both context and clear response expectation.
flae poosie we o mumiiii thne the pore
di oegiae why the whew oflie
bode ierraed taos catitaii
Avaret and sasoaiig alp by sock.
iould rablis
lopivaaly de troevtecnog in sorata clide thee tepliys
sasi intirneeen wes amang
and now in
proceceett you yourdll
uno cotibaatien Herod
pard Blnndtree contrreeeee mg the llace rescomegg mige
Darrcs becie infeeand lbeal dioscoki prodiied
ca accapagii dalyy tue
intercattrrss Sera
dual anaana ofis aoe thing liddia is abodirr
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you own Lake you oe siquee your las.
and's cutio,,
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foo puslltits radk au piuil mede Bocatos Bork aeaae busita
G
Case Study Example
nurli of Lanaues you villi grvat mesta
& wias Lata
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contact calaac and the coprocatccs ecctunge amago
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Table 8: The instruction types and examples of generat- ing CoT rationales for multi-choice reading comprehen- sion.

---

Dataset
LQ
LQ zh
RC
AL
LQ ood
CT
HL
TN
Overall
Accuracy
43.83
28.94
45.40
19.82
29.57
33.09
31.47
53.00
35.64
Table 10:
The performance on LogiEval by ablating "multi-choice", All results are in %.
participants. It known that :(]) At least 5 young teachers are female; (2) At least female teachers are over middle age; (3) At least seven young women are teachers;
Passage: One seminar
Question: According to the above information, which of the following can concluded about the participants?
Passage: A major health insurance company in Lagolia pays for special procedures prescribed by physicians only if the procedure first approved medically necessary company-appointed review panel. The rule is intended the company the money might otherwise spend medically unnecessary procedures. company has recently announced that order reduce costs, will abandon this rule.
young teachers
women
Some young women are not teachers
There are at least young teachers
D. There are at least 13 female teachers Answer and reason:
(1) least young teachers are female This means there least young female teacher.
Question: Which of the following, if true, provides the strongest justification for the company's decision?
(2) least female teochers
middle age This means there are least two femole teachers over middle age.
A.The review process is expensive and practically always results in approval of the prescribed procedure.
B. Patients often register dissatisfaction with physicians prescribe nothing for their ailments.
(3) At least seven young women are teachers - This means there is at least two young women who are teachers. (4) All young teachers are male female This is not possible,
C. The number members the company-appointe review panel who review a given procedure depends on the cost of the procedure.
have two female teachers and young teachers.
women
D. Physiclans often prescribe special procedures that are helpful but not altogether necessary for the health of the patient.
(5) femole teachers are over middle age they are not teachers This means that oll female teachers are over middle age or they are not teachers.
Answer and
Now, considering that there is at least two young women who are teachers, we can infer the following:
A. The review process is expensive and practically always results in opproval of the prescribed procedure.
(6) There at least one young teocher who female This becouse there least young women who teacher.
Reason: This option provides strong justification because if the review process is expensive and almost always approves the prescribed procedure, makes continue with it, especially could simply approve the procedures without a review process, leading to higher costs. Therefore, correct answer A.
(11) Young teachers are either mole or female- This direct result of the fact that there is at least one young teocher who is femole.
money. The company
Now, we have established that there is at least one young female teacher.
(2) Young female teachers either male female This statement incorrect because we have already concluded that there
young teacher who female.
Figure An example where LLaMA-7b-logicoo an- swers the question correctly. Model output is in Italic.
(1) At least young teachers are female This incorrect as there ore only young teachers in the seminor. Thus, the correct answer is A.
Figure 8: example where LLaMA-7b-logicon 7b-logico an- swers the question incorrectly, Model output is in Italic.


Reviews:
[{"review_id": "ZnbeUDEVVo", "reviewer": "Reviewer_D4Qn", "summary": "", "questions": "A. In the experiment, how did you prompt the instruction-tuned models and how is the answer extracted?\n\nB. Was the LogiQA Chinese version included in the dataset? If not it’s interesting to see the large improvement on LQ zh, can you show some example reasoning chains on that dataset?\n\nC. It would be good to provide hyperparameters used for training LLaMA-7b-logicot for better reproducibility.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments. We appreciate the opportunity to further clarify our methodologies and decisions. Below are the elaborations to your questions:\n\n​            A.     **Prompting and Answer Extraction from Instruction-Tuned Models:** \n\n> A. In the experiment, how did you prompt the instruction-tuned models and how is the answer extracted?\n\n**Response:** We will add relevant details to the Appendix. Below we list the final chosen prompts for each of the tasks.\n\n**LogiEval** is an instruction-prompting style benchmark where each dataset instance has integrated instructions. This ensures the LLM comprehends the desired format and response requirements.\n\nBelow is the LogiEval instruction for the reading comprehension task:\n\n```Instructions: You will be presented with a passage and a question about that passage. There are four options to be chosen from, you need to choose the only correct option to answer that question. If the first option is right, you generate the answer 'A', if the second option is right, you generate the answer 'B', if the third option is right, you generate the answer 'C', if the fourth option is right, you generate the answer 'D', if the fifth option is right, you generate the answer 'E'. Read the question and options thoroughly and select the correct answer from the four answer labels. Read the passage thoroughly to ensure you know what the passage entails.```\n\nAnd the instructions for the NLI task:\n\n```Instructions: You will be presented with a set of facts and rules as premises, and a hypothesis about it. You need to decide whether the hypothesis is entailed by the premise by choosing one of the following answers: 'Yes': The hypothesis follows logically from the information contained in the premise. 'No': The hypothesis is logically false from the information contained in the premise. 'Neutral': It is not possible to determine whether the hypothesis is true or false without further information. Read the passage of information thoroughly and select the correct answer from the three answer labels. Read the premise thoroughly to ensure you know what the premise entails.```\n\nIn the case of the **MMLU dataset**, we remain consistent with the protocols from previous evaluations. The prompt, \"The following are multiple choice questions (with answers) about {subject}. {question} Answer:\", provides both context and a clear response expectation.\n\nOur evaluation approach emphasizes exact matching. We instruct LLMs to generate the precise answer in accordance with the given prompt. The first token of the output is deemed as the final answer, which tests the LLM's proficiency in following instructions.\n\n​            B.     **Inclusion of LogiQA Chinese Version:**\n\n> B.     Was the LogiQA Chinese version included in the dataset? If not it’s interesting to see the large improvement on LQ zh, can you show some example reasoning chains on that dataset?\n\n**Response:** Indeed, we integrated 1683 instances from the training set of **LogiQA Chinese version** as our seeding data. Given LogiQA's origin as a logical reasoning dataset curated from the Chinese internet, it inherently exists in Chinese. Thus, the Chinese version of the dataset was our default choice.\n\nWe've undertaken refinements to our paper to enhance clarity. Our script at L260 has been revised from \"We use the training set of the two datasets, keeping the test set out of the instruction tuning data.\" to \"We use the training set of the two datasets, excluding the test set from the instruction tuning data. The Chinese version of the LogiQA dataset is used.\"\n\n​            C.     **Hyperparameters for Training LLaMA-7b-logicot:**\n\n> C.     It would be good to provide hyperparameters used for training LLaMA-7b-logicot for better reproducibility.\n\n**Response:** We acknowledge the significance of reproducibility. Following the excellent work of the Stanford Alpaca (https://github.com/tatsu-lab/stanford_alpaca), we adopted their running scripts and set the hyper-parameters as follows:\n\n​                ● learning rate: 2e-5\n\n​                ● batch size: 4\n\n​                ● training epochs: 2\n\nWe have added the above details in our revision. In line with this, we are fully prepared to open-source our training script along with the specific hyperparameters used.\n\n##### **Response to \"Reasons to Reject\":**\n> **Reasons To Reject:**It’s concerning that while all other reading comprehension datasets get significant improvement, the improvement on AL isn’t clear (where ChatGPT and GPT-4 still show similar performance advantage). Since LQ and RC are included in the training set, it’s concerning that AL’s result is an outlier, despite it should benefits a lot from the type of reasoning included in the instruction set. Can the author give some explanation on why that happens? A better experiment to run is to remove RC from the training set and evaluate on LQ, and vice versa, to make sure the model trained on the dataset can generalize well.\n\n**Response:** We recognize the concerns about the improvement in the AL dataset, particularly when benchmarked against the other reading comprehension datasets. We have in our revision added several potential reasons that might explain this variance:\n\n​                ● As you rightly pointed out, the LQ and RC datasets operate on a 4-option multiple-choice format, whereas the AL dataset has a 5-option format. The answer might be biased toward the first 4 choices, A, B, C, and D, which frequently appear in the training corpus. This structural difference can introduce added complexity for the model, as the potential solution space for each question is larger in AL, making the task inherently more challenging.\n\n​                ● Even beyond the format difference, the intrinsic nature of AL questions could differ from LQ and RC. While they have been included in the training set, there might be specific nuances within the AL dataset that aren't well-covered in the instruction set, affecting performance. \n\nTo understand more about the roles of different types of training data, we conducted an ablation study as follows:\n\n**Ablation Study**\n\nTo better understand the specific contributions of various reasoning types employed in our instruction-tuning process, we conduct an ablation study. This involves evaluating model performance by selectively removing one reasoning type at a time and observing the resultant change in the model's ability to handle logical reasoning tasks. \n\nThe ablation study uses the instruction-tuned LLaMA-7b model. For each reasoning type, we train the model without that specific type while retaining the others. This is performed iteratively for all reasoning types.\n\nWe report the overall average score of LogiEval and MMLU, respectively, to provide a comprehensive understanding of the respective impacts of these reasoning types. The results are shown in Table. \n\n| Removed            | LogiEval | MMLU |\n| ------------------ | -------- | ---- |\n| None (Full data)   | 40.7     | 43.3 |\n| Language to Logic  | 32.4     | 38.5 |\n| One-step Inference | 38.1     | 37.7 |\n| Inference chain    | 30.8     | 35.0 |\n| Multi-choice       | 35.6     | 30.9 |\n\nBelow is our detailed analysis:\n\n**Language to Logic:** Excluding this type led to a decline in performance on the LogiEval dataset. This underscores its significance in mapping linguistic constructs to logical expressions, which is foundational for reasoning tasks.\n\n**One-step Inference:** The drop in accuracy suggests that even simple inferences play a vital role, especially in tasks where direct conclusions are drawn from given premises.\n\n**Inference Chain:** The model's performance drop on both datasets highlights the importance of chained logical deductions. Tasks that require multi-step reasoning particularly benefit from this type.\n\n**Multi-choice:** Removing multi-choice reasoning impacted performance on MMLU more than on LogiEval, emphasizing its role in tasks where choosing among alternatives based on logical grounds is essential.\n\n\nThis ablation study reaffirms the unique contributions of each reasoning type to the model's performance. While all reasoning types contribute to enhancing the model's logical understanding, their impacts vary based on the nature of tasks and the datasets used. Future research can delve deeper into optimizing instruction-tuning processes based on specific reasoning type requirements of datasets.\n\nApart from the ablation study, we also added data quality human evaluation to our LogiCoT data.\nwe conducted a comprehensive human evaluation of the generated reasoning chains to assess their quality, faithfulness, and diversity.\n\nThe evaluation procedure can be described as follows:\n\n1: **Sampling Data:** Randomly sampled a subset of the generated reasoning chains for evaluation. We made sure this sample is representative of the entire dataset by randomly selecting 50 instances from each of our defined instruction types.\n\n2: **Setting Evaluation Metrics:** For reasoning chains, we have considered:\n\n ● **relevance:** Does the chain directly relate to the question?\n\n ● **coherence:** Is the chain logically consistent?\n\n ● **completeness:** Does it offer a full explanation for the reasoning?\n\n ● **faithfulness:** Is the reasoning factual and not fabricating details?\n\n3: **Evaluation Protocol:** We provided annotators with clear guidelines regarding each metric. They should rate each reasoning chain according to the metrics on a scale of 1 to 5.\n\n4: **Analysis:** We calculated the average scores and inter-annotator agreement using Cohen's Kappa to ensure consistency among annotators.\n\nWe outsourced 3 annotators to rate the quality and relevance of the generated chains. They are assigned with the same evaluation set, each sample is assigned with 3 scores to ensure reliability. The process took two days.\n\nThe evaluations, coupled with their findings, are added to the revised version of the paper, fortifying our claims.\n\nThe added human evaluation results are as follows:\n\n**Human Evaluation of Dataset Quality**\n\nWe conducted a comprehensive human evaluation of our generated reasoning chains. A random subset of 200 reasoning chains was selected. These chains were evaluated by 3 domain professional annotators using four key metrics: Relevance (Does the chain directly relate to the question?), Coherence (Is the chain logically consistent?), Completeness (Does it offer a full explanation for the reasoning?), and Faithfulness ( Is the reasoning factual and not fabricating details?). Each reasoning chain was rated on a scale from 1 (poor) to 5 (excellent) for each metric.\n\nThe reasoning chains achieved **an average score of 4.9 for Relevance, 4.7 for Coherence, 4.5 for Completeness, and 4.5 for Faithfulness**. The inter-annotator agreement, measured using Cohen's Kappa, was 0.87, indicating strong agreement among the annotators.\n\nThe human evaluation underscores the quality and reliability of the reasoning chains generated by our model. By conducting new experiments and evaluations to our method, we'd like to increase the soundness of our paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- A good instruction-tuning dataset is something the community eagerly need in order to close the instruction-following ability gap between open source models and close models. On top of that, the paper considered the logical reasoning aspect, which is new and has the potential to enable more interesting model applications.\n- The paper spent good effort on designing various instructions that includes various important aspects of logical reasoning.\n- The performance of resulting model on MMLU is boosted significantly, which is a good sign that the dataset is helpful for learning general reasoning ability and can preserve past knowledge.", "reasons_to_reject": "It’s concerning that while all other reading comprehension datasets get significant improvement, the improvement on AL isn’t clear (where ChatGPT and GPT-4 still show similar performance advantage). Since LQ and RC are included in the training set, it’s concerning that AL’s result is an outlier, despite it should benefits a lot from the type of reasoning included in the instruction set. Can the author give some explanation on why that happens? A better experiment to run is to remove RC from the training set and evaluate on LQ, and vice versa, to make sure the model trained on the dataset can generalize well.", "questions_to_authors": "A. In the experiment, how did you prompt the instruction-tuned models and how is the answer extracted?\n\nB. Was the LogiQA Chinese version included in the dataset? If not it’s interesting to see the large improvement on LQ zh, can you show some example reasoning chains on that dataset?\n\nC. It would be good to provide hyperparameters used for training LLaMA-7b-logicot for better reproducibility.", "paper_topic_and_main_contributions": "The paper presented an instruction-tuning dataset with 70K instances, by collecting chain-of-thought logical reasoning responses from GPT4 based on existing datasets. The instruction focused on logical inference, symbolic logic, and reading comprehension.\n\nThe model trained on this dataset had large performance improvement on the reasoning benchmarks compare with the baseline.", "excitement": 4, "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": {}, "extra_text": {}}, {"review_id": "DzOkrplMOg", "reviewer": "Reviewer_Gyqk", "summary": "", "questions": "A: The purpose of Table 3 and figure 2 is unclear to me. What does \"drink water\" and \"take plane\" have to do with logical reasoning and why is it important to have this diversity in chain of thought responses?\n\nB: The \"pseudocode\" provided is not clear as it does not show any algorithm but only what I assume is inputs. Could authors provide what is the significance of prompt_with_output vs prompt_without_output? \n\nC: Table 5: Why are improvements in subjects like Social Sciences and Culture (up more than 50%) significantly higher than improvements in subjects like STEM, Engineering (around 26% improvement) or computer science (11%) tests for which would probably require more reasoning ability? (as for humanities, test would require more memorization as these subjects are usually tested in essay form)?\n\nD: Table 4: Why does 30B model fine-tuned on supercot perform worse than base 7B model? Perhaps the supercot actually hurts the model's ability to reason or this model is not as suitable for your task?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your diligent examination of our work and the subsequent queries. \n\n\n\n​            A.     **Table 3 and Figure 2 Clarification:**\n\n> A: The purpose of Table 3 and figure 2 is unclear to me. What does \"drink water\" and \"take plane\" have to do with logical reasoning and why is it important to have this diversity in chain of thought responses?\n\n\n\n**Response:** We have modified the draft to improve understanding. Table 3 and Figure 2 aim to spotlight the distinctive features of our generated dataset. LOGICINFERENCE, a large-scale logical inference dataset, is birthed through rules-based generation. This method reuses simple noun-verb phrases to spawn varying conditions under differing rules. This gives rise to frequently recurring noun-verb pairs, such as \"drink water\" and \"take plane\", which you observed being prominently counted in Figure 2. The sunburst chart in Figure 2 is adopted by multiple instruction-tuning papers to illustrate their data coverage such as the famous Self-Instruct paper.\n\nTo make it more clear, here I showcase an example from the LOGICINFERENCE dataset, which contains \"drink water\" and \"take plane\", to give you an illustration how they are integrated into logical reasoning:\n\n*INPUT*\n\n*Consider the following premises.*\n\n*If Charles takes a plane, then it is raining.*\n\n*If Patricia were curious, then Charles is taking a plane.*\n\n*If it rains, then Patricia is curious.*\n\n*If Charles takes a plane, then John is not reading a book.*\n\n*John is reading a book.*\n\n*Can we infer the following from them? If we can,*\n\n*name the inference rule being used.*\n\n*It is not raining*\n\n\n*OUTPUT*\n\n*From the fact that if it rains, then Patricia is curious, and that if Patricia were curious, then*\n\n*Charles is taking a plane we can infer that if it rains, then Charles is taking a plane via transitivity.*\n\n*From the fact that if it rains, then Charles is taking a plane, and that if Charles takes a plane,*\n\n*then it is raining we can infer that it is raining if and only if Charles is taking a plane via biconditional introduction.*\n\n*From the fact that if Charles takes a plane, then John is not reading a book, and that John is*\n\n*reading a book we can infer that Charles is not taking a plane via modus tollens.*\n\n*Finally, from the fact that it is raining if and only if Charles is taking a plane, and that*\n\n*Charles is not taking a plane we can infer that it is not raining via biconditional elimination*\n\n*Therefore, the answer is yes.*\n\n\n\nIn light of your feedback, we have relocated Table 3 and Figure 2 to the appendix, adding more explanations there, thereby freeing up space in the main text. This adjustment has allowed us to conduct data quality evaluation and to incorporate an ablation study addressing further investigations to the experiments.\n\n\n\n​            B.     **Pseudocode Clarity:**\n\n> B: The \"pseudocode\" provided is not clear as it does not show any algorithm but only what I assume is inputs. Could authors provide what is the significance of prompt_with_output vs prompt_without_output?\n\n\n\n**Response:** We have modified the draft to avoid confusion. The intention behind providing pseudocode is to offer a clear window into the GPT-4 generation-acquisition process. Recognizing that some of our seeding datasets (e.g., EntailmentBank) come equipped with Chain-of-Thought (CoT) processes as golden outputs, we leveraged this information within prompts to steer GPT-4 towards more insightful rationales. Here, \"prompt_with_output\" pertains to datasets enriched with CoT outputs, whereas \"prompt_without_output\" refers to those bereft of CoT outputs. We've taken measures to clarify this in the manuscript, modifying our phraseology from \"We use the pseudo-code below for GPT-4 rationales collection\" to \"The pseudo-code below elucidates the process adopted for GPT-4 rationales collection.\"; and we modified \"prompt_with_output\" into \"prompt_with_CoT\", \"prompt_without_output\" into \"prompt_without_CoT\"\n\n\n\n​            C.     **Table 5 Performance Variances:**\n\n> C: Table 5: Why are improvements in subjects like Social Sciences and Culture (up more than 50%) significantly higher than improvements in subjects like STEM, Engineering (around 26% improvement) or computer science (11%) tests for which would probably require more reasoning ability? (as for humanities, test would require more memorization as these subjects are usually tested in essay form)?\n\n\n\n**Response:** We have added discussion in Section 4.3. Logical reasoning is intrinsically tied to daily human cognitive processes—it aids us in synthesizing and analyzing available information. Subjects like Social Sciences and Culture, at their core, are geared towards human-centric reasoning. On the other hand, subjects within the domain of STEM, Engineering, or Computer Science pivot more on specialized knowledge which may not be inherently ingrained in commonplace conversations or contexts. Without domain knowledge infused into the model, the performance gain might not rival that on the social science tasks.\n\n\n\n​            D.     **Performance of the 30B Model in Table 4:**\n\n> D: Table 4: Why does 30B model fine-tuned on supercot perform worse than base 7B model? Perhaps the supercot actually hurts the model's ability to reason or this model is not as suitable for your task?\n\n\n\n**Response:** We have added discussion in Section 4.3. The performance dip observed in LLaMA-30b-supercot, especially on the NLI task, can be traced back to the nature of the NLI classification. Designed with three distinct labels - \"entailed\", \"contradicted\", and \"neutral\", NLI differs from reading comprehension and doesn't manifest routinely in CoT settings. The absence of entailment relationship tuning in the supercot dataset, combined with the fine-tuning procedure, may have potentially hampered its generalizability. Conversely, our LogiCoT tuning integrates a wealth of entailment relationship demonstrations (via FOLIO). Our empirical results underscore the value of logical entailment relationship learning in bolstering general reasoning. Echoing this sentiment, Facebook's fine-tuning of a BART-base model on the MNLI dataset, repurposed for zero-shot text classification, underlines the potential of enhancing LLMs' general capabilities via logical entailment data.\n\n\n\n\n\n##### **Response to \"Reasons to Reject\":**\n\n\n\n**1. Evaluation of Dataset Quality and Unsubstantiated Claims:**\n\n> 1. **Reasons To Reject:**Besides downstream fine-tuning performance, there is no evaluation of dataset quality. Authors do not conduct human/expert evaluation of the quality of GPT-4 generated reasoning chains that were found to be unfaithful in some cases in previous work (see missing citation). Instead, the authors make claims like \"These responses ... represent GPT-4’s reasoning process\" or \"GPT-4, with its advanced language understanding and generation capabilities, can provide diverse and detailed reasoning chains which not only answer the logical reasoning tasks but also provide insights into the underlying reasoning process\" These claims are not substantiated in the paper.\n\n**Response:**  To address this, we conducted a comprehensive human evaluation of the generated reasoning chains to assess their quality, faithfulness, and diversity. \n\nThe evaluation procedure can be described as follows:\n\n1: **Sampling Data:** Randomly sampled a subset of the generated reasoning chains for evaluation. We made sure this sample is representative of the entire dataset by randomly selecting 50 instances from each of our defined instruction types.\n\n2: **Setting Evaluation Metrics:** For reasoning chains, we have considered:\n\n​                ● **relevance:** Does the chain directly relate to the question?\n\n​                ● **coherence:** Is the chain logically consistent?\n\n​                ● **completeness:** Does it offer a full explanation for the reasoning?\n\n​                ● **faithfulness:** Is the reasoning factual and not fabricating details?\n\n3: **Evaluation Protocol:** We provided annotators with clear guidelines regarding each metric. They should rate each reasoning chain according to the metrics on a scale of 1 to 5.\n\n4: **Analysis:** We calculated the average scores and inter-annotator agreement using Cohen's Kappa to ensure consistency among annotators.\n\n\n\nWe outsourced 3 annotators to rate the quality and relevance of the generated chains. They are assigned with the same evaluation set, each sample is assigned with 3 scores to ensure reliability. The process took two days. \n\nThe evaluations, coupled with their findings, are added to the revised version of the paper, fortifying our claims.\n\nThe added human evaluation results are as follows:\n\n**Human Evaluation of Dataset Quality**\n\nWe conducted a comprehensive human evaluation of our generated reasoning chains. A random subset of 200 reasoning chains was selected. These chains were evaluated by 3 domain professional annotators using four key metrics: Relevance (Does the chain directly relate to the question?), Coherence (Is the chain logically consistent?), Completeness (Does it offer a full explanation for the reasoning?), and Faithfulness ( Is the reasoning factual and not fabricating details?). Each reasoning chain was rated on a scale from 1 (poor) to 5 (excellent) for each metric.\n\n\n\nThe reasoning chains achieved **an average score of 4.9 for Relevance, 4.7 for Coherence, 4.5 for Completeness, and 4.5 for Faithfulness**. The inter-annotator agreement, measured using Cohen's Kappa, was 0.87, indicating strong agreement among the annotators.\n\n\n\nThe human evaluation underscores the quality and reliability of the reasoning chains generated by our model. \n\n\n\n**2. Related Work and Differentiation from Current Approaches:**\n\n> 2. The related work does not seem to fully describe the difference from the current approaches. For example, authors mention the SuperCoT dataset only in the evaluation section, without describing how their approaches differs. Moreover, there are at least 2 non-contemporaneous works that are missing from related work that are particularly relevant (see missing citations). Specifically, Teaching Small Models to Reason paper employs identical approach of fine-tuning a smaller model on CoTs generated by PALM and has been publicly available since December 2022.\n\n\n\n**Response:** We have added the missing citations. The “Teaching Small Models to Reason” paper , which uses a similar approach of fine-tuning smaller models, should have been discussed in the related work. There is a line of research discussing this topic. We rectify this by providing a detailed comparison of our method with these works, highlighting the key differences, motivations, and results. \n\n\n\nWe also notice that the orca paper also uses GPT-4 to generate CoT data. We will cite this.\n\n\n\nAs for the SuperCoT (https://huggingface.co/ausboss/llama-30b-supercot/tree/main) dataset, there isn't a dedicated paper available, nor could we locate any detailed technical reports discussing its intricacies. Thus we have added a link and a brief discussion.\n\n\n\nOverall, while the arena of instruction tuning LMs with CoT data is burgeoning with fresh ideas and methodologies, LogiCoT was, to our knowledge, the first to harness logical reasoning rationales through GPT-4 and logical reasoning datasets, leading to enhanced understanding and performance in logical reasoning tasks.\n\n\n\n**3. Clarity in the Experiments Section:**\n\n> 3. The experiment section is not very clearly described. The LogiEval benchmark could be explained further, for example, AL:AR-LSAT,CT:ConTRoL,HL:HELP,TN:TaxiNLI tasks are never introduced in the paper. The LogiEval benchmark used for evaluation contains two datasets identical from fine-tuning data: LogiQA, ReClor. However, the authors don't address this data contamination issue. The authors also don't mention how the evaluation tasks were inputted: were they also using an instruction format? Perhaps the performance gains stem from instruction understanding (which smaller models are very sensitive to) and not from reasoning improvements. Authors don't conduct any evaluation of the reasoning chains themselves, but rather only of the final answer accuracy. Without this information, I believe the central claim of the paper that \"LogiCoT ... elicits general reasoning skills\" (line 017-018) is not sufficiently substantiated.\n\n\n\n**Response:** We appreciate the feedback on the clarity of the experiments section.\n\n\n\n​                ● *Description of LogiEval and Tasks:* We provide a detailed introduction and description of each task (AL: AR-LSAT, CT: ConTRoL, HL: HELP, TN: TaxiNLI) in the revised manuscript for clarity.\n\n  \n\n​                ● *Data Contamination Issue:* We understand the concerns regarding potential data contamination from using LogiQA and ReClor datasets in both training and evaluation. The potential for data contamination between training and evaluation is a pertinent concern. In our work, we have taken care to ensure that the data used for training and evaluation do not overlap, thereby minimizing any bias. Specifically, we utilized the training sets of LogiQA and ReClor for model fine-tuning, and evaluations were exclusively conducted on their respective test sets. This distinction ensures the evaluation's integrity and provides a genuine measure of the model's performance on unseen data.\n\n\n\n​                ● *Input Format of Evaluation Tasks:* The tasks were indeed inputted using an instruction format, similar to the one used during training. We agree that performance can be influenced by instruction understanding, especially for smaller models. To delve deeper into this, we conduct ablation studies by evaluating the model's performance with and without instructions. This would help tease apart the contribution of instruction understanding from actual reasoning improvements.\n\nThe table below shows the results of LLaMA-7b-LogiCoT on the LogiEval benchmark with and without instructions.\n\n| Instruction | LQ   | LQ zh | RC   | AL   | LQ ood | CT   | HL   | TN   |\n| ----------- | ---- | ----- | ---- | ---- | ------ | ---- | ---- | ---- |\n| with        | 51.2 | 33.4  | 57.9 | 20.9 | 38.9   | 35.4 | 35.8 | 43.3 |\n| without     | 49.1 | 32.8  | 57.2 | 20.6 | 38.8   | 30.7 | 35.4 | 38.5 |\n\nOverall, the performance of LLaMA-7b-LogiCoT is not affected significantly by the absence of instructions.\n\n\n\n​                ● *Evaluation of Reasoning Chains:* \n\n\nTo ascertain the competence of the LLaMA-7b-LogiCoT in generating reasoning chains, we employed a similar evaluation methodology as we did for our LogiCoT data. Using the LogiEval benchmark as a yardstick, we examined a subset of 160 instances, obtained by picking 20 output examples from each dataset.\n\nThe ensuing evaluation revealed that LLaMA-7b-LogiCoT performed commendably, achieving a mean score of 4.7 in Relevance, 4.5 in Coherence, 4.0 in Completeness, and 4.4 in Faithfulness.  \n\nThe table below compares the output quality of LLaMA-7b-LogiCoT with other popular models.\n| Model   | Coherence | Completeness | Faithfulness | Relevance |\n| ------- | --------- | ------------ | ----------- | --------- |\n| LLaMA-30b-supercot   | 3.4       | 3.5          | 3.1         | 4.5       |\n| Falcon-40b-instruct  | 3.2       | 3.4          | 3.1         | 4.5       |\n| ChatGPT | 3.9       | 4.8          | 3.7         | 4.7       |\n| GPT-4   | 4.5       | 4.8          | 4.5         | 4.9       |\n|LLaMA-7b-LogiCoT | 4.5 | 4.0 | 4.4 | 4.7 |\n\nIn juxtaposition with our LogiCoT data assessment, there are evident distinctions. While the LLaMA-7b-LogiCoT trails slightly in categories like Relevance, Coherence, and Faithfulness, a more pronounced gap of 0.5 is discernible in the Completeness category. The drop in the Completeness score indicates smaller models are not competitive with GPT-4 on the length of outputs. However, compared to other community models, the quality of the reasoning chain yield by LLaMA-7b-LogiCoT is competitive, given that it is a much smaller model.\n\n\n\n**Typos, Grammar, Style, and Presentation Improvements:**\n\n\n\n> ● On Line 489: \"language learning models\" -> \"large language models?\"\n\n  **Response:** The typo has been corrected in the manuscript.\n\n\n\n> ● Regarding the numerous figures in the abstract (do you mean appendix?) and referenced verbiage on lines 378-386:\n\n  **Response:** We have restructured the relevant section, ensuring a smoother flow of information.", "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 paper introduces a novel dataset that could be used to enhance instruction understanding for logical tasks. The dataset and resulting model weights are released which could be beneficial for further research.", "reasons_to_reject": "1. Besides downstream fine-tuning performance, there is no evaluation of dataset quality. Authors do not conduct human/expert evaluation of the quality of GPT-4 generated reasoning chains that were found to be unfaithful in some cases in previous work (see missing citation). Instead, the authors make claims like \"These responses ... represent GPT-4’s reasoning process\" or \"GPT-4, with its advanced language understanding and generation capabilities, can provide diverse and detailed reasoning chains which not only answer the logical reasoning tasks but also provide insights into the underlying reasoning process\" These claims are not substantiated in the paper.\n\n2. The related work does not seem to fully describe the difference from the current approaches. For example, authors mention the SuperCoT dataset only in the evaluation section, without describing how their approaches differs. Moreover, there are at least 2 non-contemporaneous works that are missing from related work that are particularly relevant (see missing citations). Specifically, Teaching Small Models to Reason paper employs identical approach of fine-tuning a smaller model on CoTs generated by PALM and has been publicly available since December 2022.\n\n3. The experiment section is not very clearly described. The LogiEval benchmark could be explained further, for example, AL:AR-LSAT,CT:ConTRoL,HL:HELP,TN:TaxiNLI tasks are never introduced in the paper. The LogiEval benchmark used for evaluation contains two datasets identical from fine-tuning data: LogiQA, ReClor. However, the authors don't address this data contamination issue. The authors also don't mention how the evaluation tasks were inputted: were they also using an instruction format? Perhaps the performance gains stem from instruction understanding (which smaller models are very sensitive to) and not from reasoning improvements. Authors don't conduct any evaluation of the reasoning chains themselves, but rather only of the final answer accuracy. Without this information, I believe the central claim of the paper that \"LogiCoT ... elicits general reasoning skills\" (line 017-018) is not sufficiently substantiated.", "questions_to_authors": "A: The purpose of Table 3 and figure 2 is unclear to me. What does \"drink water\" and \"take plane\" have to do with logical reasoning and why is it important to have this diversity in chain of thought responses?\n\nB: The \"pseudocode\" provided is not clear as it does not show any algorithm but only what I assume is inputs. Could authors provide what is the significance of prompt_with_output vs prompt_without_output? \n\nC: Table 5: Why are improvements in subjects like Social Sciences and Culture (up more than 50%) significantly higher than improvements in subjects like STEM, Engineering (around 26% improvement) or computer science (11%) tests for which would probably require more reasoning ability? (as for humanities, test would require more memorization as these subjects are usually tested in essay form)?\n\nD: Table 4: Why does 30B model fine-tuned on supercot perform worse than base 7B model? Perhaps the supercot actually hurts the model's ability to reason or this model is not as suitable for your task?", "paper_topic_and_main_contributions": "The paper provides an instruction dataset for logical reasoning. The authors construct the dataset by prompting GPT-4 to generate chain-of-thought explanations. Then, LLaMA-7B is fine-tuned on the data and evaluated on a reasoning benchmark of various tasks. The authors report improvement compared to the base model and 2 other instruction-tuned models. The authors also compare the fine-tuned model to ChatGPT and GPT-4.", "excitement": 3, "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": {}, "extra_text": {"Missing_References": "Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. arXiv preprint arXiv:2305.04388. (contemporaneous)\n\nWang, P., Chan, A., Ilievski, F., Chen, M., & Ren, X. (2022, September). PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales. In The Eleventh International Conference on Learning Representations.\n\nLucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2023. Teaching Small Language Models to Reason. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1773–1781, Toronto, Canada. Association for Computational Linguistics. \n\nCheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8003–8017, Toronto, Canada. Association for Computational Linguistics. (contemporaneous)\n\nLiunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, and Yejin Choi. 2023. Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2665–2679, Toronto, Canada. Association for Computational Linguistics. (contemporaneous)\n\nMukherjee, S., Mitra, A., Jawahar, G., Agarwal, S., Palangi, H., & Awadallah, A. (2023). Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707. (contemporaneous)", "Typos_Grammar_Style_and_Presentation_Improvements": "489: language learning models -> large language models?\n\ntoo many figures in the abstract that are referred to in main text, which could be shortened to fit some of these figures (eg, verbiage on lines 378-386)."}}, {"review_id": "gAmVoXpRvU", "reviewer": "Reviewer_mPz1", "summary": "", "questions": "- Figure 1 and Table 1 are very hard to read due to overly small font sizes\n- L354: Type 5 was never defined\n- L383-386: why are the instruction types unique to this data generation scheme? \n- L397-411 is confusing and probably can be presented differently, e.g. with textual descriptions. Why are there 2 cases of with and without output? What exactly is point 3 doing?\n- L420-421: why discuss whether they are seq-to-seq? What does GPT4 working well on those mean for the story of this paper?\n- L430: where is the evidence showing its diversity and detailedness?\n- Table2: why not also show the types within multiple-choice (i.e., Identify the Necessary Claim, etc.)? As the number of multiple-choice is already a lot smaller than the General Inference ones, would it be an issue that it is further classified into more fine-grained types?\n- How does Figure 2 contribute to the claims/story of the paper? If to show diversity, then what should it be compared against?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments. We appreciate the opportunity to further clarify our methodologies and decisions. Below are the elaborations to your questions:\n**Figure 1 and Table 1 Font Sizes:**\n\n> ● Figure 1 and Table 1 are very hard to read due to overly small font sizes\n\n**Response:** To enhance readability, we have increased the font size in Figure 1 and Table 1.\n\n\n\n**L354: Undefined Type 5:**  \n\n> ● L354: Type 5 was never defined\n\n**Response:** We've addressed this oversight by revising the sentence to accurately reference \"Strengthen an Argument.\"\n\n\n\n**L383-386: Uniqueness of Instruction Types:**  \n\n> ● L383-386: why are the instruction types unique to this data generation scheme?\n\n**Response:** We have modified the paper in Section 3.2 to avoid confusion. The design of our instruction types is deeply intertwined with the nuances of logical reasoning tasks. In logical discourse and debates, not all reasoning is straightforward. Sometimes, the objective is to bolster a given argument (strengthen an argument) or to challenge its validity (weaken an argument). These intricacies in logical reasoning necessitate tailored instructions.\n\nFor example, the instructions like \"strengthen an argument\" or \"weaken an argument\" are directly inspired by the practices of critical thinking, where arguments are evaluated, fortified, or refuted based on evidential support (cite: A role for reasoning in a dialogic approach to critical thinking). When training a model like GPT-4, which already has a robust grasp of language, it becomes essential to be specific in our instruction to extract the desired logical output. Generic instructions might not suffice to bring out the nuanced logical reasoning capabilities.\n\nFurthermore, the uniqueness of our instruction types ensures that the generated chain-of-thought data encapsulates a comprehensive spectrum of logical reasoning scenarios, ranging from direct inference to more complex chained deductions. This approach ensures a more holistic training process, maximizing the model's potential in various logical reasoning tasks.\n\n\n\n\n\n**L397-411: Presentation and Clarity:**  \n\n> ● L397-411 is confusing and probably can be presented differently, e.g. with textual descriptions. Why are there 2 cases of with and without output? What exactly is point 3 doing?\n\n**Response:** We've restructured it for better comprehension. The intention behind providing pseudocode is to offer a clear window into the GPT-4 generation-acquisition process. Recognizing that some of our seeding datasets (e.g., EntailmentBank) come equipped with Chain-of-Thought (CoT) processes as golden outputs, we leveraged this information within prompts to steer GPT-4 towards more insightful rationales. Here, \"prompt_with_output\" pertains to datasets enriched with CoT outputs, whereas \"prompt_without_output\" refers to those bereft of CoT outputs. We've taken measures to clarify this in the manuscript, modifying our phraseology from \"We use the pseudo-code below for GPT-4 rationales collection\" to \"The pseudo-code below elucidates the process adopted for GPT-4 rationales collection.\"; and we modified \"prompt_with_output\" into \"prompt_with_cot\", \"prompt_without_output\" into \"prompt_without_cot\"\n\n\n\n\n\n**L420-421: Relevance of Seq-2-Seq Mention:**  \n\n> ● L420-421: why discuss whether they are seq-to-seq? What does GPT4 working well on those mean for the story of this paper?\n\n**Response:** Seq-2-seq data is conducive for instruction-tuning. Golden CoT output datasets provide GPT-4 with invaluable references, optimizing generation quality. While ReClor and LogiQA don’t come with CoT guidance, GPT-4's commendable performance on these datasets assures quality generation. We rearranged this paragraph for clarity.\n\n\n\n**L430: Evidence of Diversity and Detail:**  \n\n> ● L430: where is the evidence showing its diversity and detailedness?\n\n**Response:** In response to this point, we've incorporated a generation quality assessment section to probe deeper into the reasoning chain's quality. \n\n**Human Evaluation of Dataset Quality**\n\nWe conducted a comprehensive human evaluation of our generated reasoning chains. A random subset of 200 reasoning chains was selected. These chains were evaluated by 3 domain professional annotators using four key metrics: Relevance (Does the chain directly relate to the question?), Coherence (Is the chain logically consistent?), Completeness (Does it offer a full explanation for the reasoning?), and Faithfulness ( Is the reasoning factual and not fabricating details?). Each reasoning chain was rated on a scale from 1 (poor) to 5 (excellent) for each metric.\n\n\nThe reasoning chains achieved **an average score of 4.9 for Relevance, 4.7 for Coherence, 4.5 for Completeness, and 4.5 for Faithfulness**. The inter-annotator agreement, measured using Cohen's Kappa, was 0.87, indicating strong agreement among the annotators.\n\n\nThe human evaluation underscores the quality and reliability of the reasoning chains generated by our model. While the scores were high for relevance, we acknowledge room for improvement in faithfulness, and future work will aim to refine the generation process to address this.\n\n\nA good score would mean that the chain doesn't overly rely on repetitive reasoning patterns and consistently introduces varied logical constructs. The metric \"Completeness\" evaluates if the generated reasoning chains offer a thorough explanation for the reasoning, ensuring that no critical steps are omitted, while \"Coherence\" checks the logical consistency and the flow of reasoning, ensuring that every step in the chain is explained with adequate detail. Our scores of 4.5 for both \"Completeness\" and \"Coherence\" suggest that our chains are not only logically sound but also well-elaborated with necessary details.\n\n\n\n**Table 2: Further Classification within Multiple-choice:**  \n\n> ● Table2: why not also show the types within multiple-choice (i.e., Identify the Necessary Claim, etc.)? As the number of multiple-choice is already a lot smaller than the General Inference ones, would it be an issue that it is further classified into more fine-grained types?\n\n**Response:** Given the limited number of multiple-choice types within the dataset, further breakdowns would result in sparse categories. However, these types give reader a clear view of what reasoning types it contains. To conserve space and maintain clarity, we've decided against a granular categorization in the table. We remove the discuss of different multi-choice types to the Appendix.\n\n\n\n**Role of Figure 2:** \n\n> ● How does Figure 2 contribute to the claims/story of the paper? If to show diversity, then what should it be compared against?\n\n**Response:** Recognizing the reviewer's point, we've relocated Figure 2 to the Appendix, added more discussions, and, in its place, introduced a generation quality assessment.\n\n\n\n**Typos, Grammar, Style, and Presentation Improvements:**\n\n> L414: two or three datasets?\nWe have rectified the statement to accurately mention \"The three datasets...\"\n\n> L520: varies -> variation\nThe suggested change has been implemented. \"varies\" is now replaced with \"variation.\"\n\n\n\n##### **Response to \"Reasons to Reject\":**\n\n\n\n> 1. **Reasons To Reject:**In my opinion, Section 3 requires some work for the the paper to publishable in EMNLP. Specifically,\n​                ● What is the rationale behind choosing the 8 types that you did? Some intuition, design principals, or citations to follow would be necessary to make this choice seem less random.\n​                ● Why not use the exact same task format as the datasets chosen? Why bother with introducing the finer-grained types? What is the advantage of doing so?\n​                ● How exactly are the examples from your chosen datasets (e.g., EntailmentBank, FOLIO...) converted to these types?\n\n> 2. The results in Section 4 is only half-satisfactory to me. The crux of this paper is to show that using logic-related data for instruction-tuning is superior to using other kinds of data in previous work. There are at least two issues with the current paper:\n​                ● The results in Table 5 is informative but would greatly benefit from a further comparison against other finetuned LLaMA models (not just base), such as the already considered LLaMA-30b-supercot or even more of them. It comes at little surprise that finetuning LLaMA, either on logic-related data or not, brings about better performance on tasks. But if it can be shown that logic-related data is more evaluable than other instruction data, then the story would be a lot more interesting.\n​                ● The baseline can be chosen better in two ways. First, one should clearly spell out what instruction data any model is finetuned with, so that there is a comparison between that and your logic-related data. Second, there should ideally more baselines with the same LLM (namely LLama 7B) than just 1-2. The second point is a plus and not a deal-breaker.\n\n> 3. The discussions (primarily in Section 4.4) are not enough in-depth.\n​                ● Minimally, the types introdudeced in Sec 3.2 should be discussed here as to what matters for performance and what not.\n​                ● It is more or less expected that logic-tuned models would do better on logic-tasks, but it would be very interesting to understand how do they also perform better on general-tasks like MMLU, beyond simply case studies.\n\n> 4. The paper presentation could be improved. There is a slew of issues regarding unsupported claims and unconcentrated storytelling throughout the paper. Some of the examples are illustrated in Questions for the Authors.\n\n\n\n**1. Clarity on the Rationale for Choosing Reasoning Types in Section 3:**\n\n**Reasoning Behind the Chosen 8 Types:**   \n\n**Response:** The choice of reasoning types is grounded in established logical reasoning literature. The 8 types are elaborated throughly in their original paper. As \"language to logic\" is from the FOLIO paper, \"One-step Inference\" and \"Inference Chain\" are from the LOGICINFERENCE paper, and the MRC types are from the ReClor paper.\n\nWe've augmented the manuscript with pertinent citations to substantiate our choices. Our instruction types resonate with these reasoning types, emphasizing the learning capabilities of our instruction-tuning process. These reasoning types are explored in our ablation study.\n\n\n\n**Reason for Diverging from Original Dataset Formats:**   \n\n**Response:** We modified our paper in Section 3.2 to give more explanations on our data construction process. Diverse reasoning types come with unique instructions. For each reasoning type, the instruction gives clear guidance. The granularity offers the advantage of better task specificity and addresses varied facets of logical reasoning. For example, consider the instruction: \"Consider the following premises ... Can we infer ... from them? name the inference rules being used.\", The instruction is informative with our instruction-prompting settings.\n\nThe fine-grained reasoning types give us an opportunity to conduct ablation studies on different types.\n\n\n\n**Conversion of Chosen Datasets to Reasoning Types:**  \n\n**Response:** We've categorized reasoning types in alignment with the instruction types of the datasets. The broader categories are derived by clustering based on similar instructions.\n\n\n\n**2. Concerns on Results in Section 4:**\n\n**Need for Extended Comparisons in Table 5:**  \n\n**Response:** We concur with your observation. An extended comparison with more finetuned LLaMA models, like LLaMA-30b-supercot, would indeed provide richer insights. We have updated Table 5 to reflect this additional comparison, thereby underscoring the efficacy of logic-related data in instruction-tuning.\n\n\n\nDataset| LQ| LQ zh |RC |AL| LQ ood |CT |HL |TN |Overall\n|---|---|---|---|---|---|---|---|---|---|\nLLaMA-30b-supercot | 19.31| 26.35 |17.81| 17.98| 18.41| 24.10 |32.26 |41.91| 24.78\nLLaMA-7b-logicot| 50.25 |32.77| 57.60| 16.96 |38.79 |35.68| 35.44 |58.05 |40.69\nChatGPT |52.37| 53.18 |57.38 |20.42 |38.44| 58.45 |42.13| 57.30| 47.46\nGPT-4 |72.25| 70.56| 87.20| 33.48 |58.49| 56.40 |46.01| 60.08| 60.56\n\n\n\n**Baseline Choices and Clarity:**  \n\n**Response:** \n\n  1. We've made sure to explicitly detail the instruction data every model is finetuned with, offering a clearer comparison basis against our logic-related data.\n\n  2. While we have already included multiple baselines, adding more that employ the same LLM (LLaMA 7B) could indeed be beneficial. We'll consider this in future iterations.\n\n\n\n**3. Depth of Discussions in Section 4.4:**\n\n**Analysis of Types Introduced in Section 3.2:**  \n\n**Response:** We've incorporated a deeper analysis of the reasoning types from Section 3.2, elucidating their respective impacts on performance in the ablation study. \n\nThe modification to the manuscript is as follows:\n\n\n**Ablation Study**\n\nTo better understand the specific contributions of various reasoning types employed in our instruction-tuning process, we conduct an ablation study. This involves evaluating model performance by selectively removing one reasoning type at a time and observing the resultant change in the model's ability to handle logical reasoning tasks. \n\n\n\nThe ablation study uses the instruction-tuned LLaMA-7b model. For each reasoning type, we train the model without that specific type while retaining the others. This is performed iteratively for all reasoning types.\n\n\n\nWe report the overall average score of LogiEval and MMLU, respectively, to provide a comprehensive understanding of the respective impacts of these reasoning types. The results are shown in Table. \n\n\n\n| Removed            | LogiEval | MMLU |\n| ------------------ | -------- | ---- |\n| None (Full data)   | 40.7     | 43.3 |\n| Language to Logic  | 32.4     | 38.5 |\n| One-step Inference | 38.1     | 37.7 |\n| Inference chain    | 30.8     | 35.0 |\n| Multi-choice       | 35.6     | 30.9 |\n\nFrom the table we can find that:\n\n**Language to Logic:** Excluding this type led to a decline in performance on the LogiEval dataset. This underscores its significance in mapping linguistic constructs to logical expressions, which is foundational for reasoning tasks.\n\n**One-step Inference:** The drop in accuracy suggests that even simple inferences play a vital role, especially in tasks where direct conclusions are drawn from given premises.\n\n**Inference Chain:** The model's performance drop on both datasets highlights the importance of chained logical deductions. Tasks that require multi-step reasoning particularly benefit from this type.\n\n**Multi-choice:** Removing multi-choice reasoning impacted performance on MMLU more than on LogiEval, emphasizing its role in tasks where choosing among alternatives based on logical grounds is essential.\n\n\n\nThis ablation study reaffirms the unique contributions of each reasoning type to the model's performance. While all reasoning types contribute to enhancing the model's logical understanding, their impacts vary based on the nature of tasks and the datasets used. Future research can delve deeper into optimizing instruction-tuning processes based on specific reasoning type requirements of datasets.\n\n\n\n**Performance on General Tasks like MMLU:**\n\n**Response:** It's pertinent to understand why a logic-tuned model excels not only in logic-tasks but also in general tasks. We've expanded the discussion to delve into the underlying reasons, beyond mere case studies, highlighting how the logic-based grounding enhances general reasoning capabilities.\n\nWe construct the discussion according to the following 5 aspects:\n\n*While it is anticipated that a logic-tuned model would demonstrate proficiency in tasks explicitly rooted in logical reasoning, its prowess in more generalized tasks such as MMLU is particularly intriguing. This section delves deeper into the underlying reasons, striving to highlight how logical grounding augments the model's performance in broader contexts.*\n\n**Strengthened Fundamental Reasoning Skills:** \n\n*The primary principle behind the instruction-tuning with logical chain-of-thought data is to bolster the foundational reasoning capabilities of the model. Logic, in essence, provides a structured framework for argument formulation, decision-making, and understanding relationships. When such logical constructs are internalized by the model, it naturally improves its ability to handle even those tasks that are not explicitly logic-centric but do demand a structured approach to problem-solving.*\n\n**Enhanced Sequential Processing:**\n\n*Logical reasoning inherently requires processing information in a sequential and structured manner. The skills developed during logical instruction-tuning, such as following an inference chain or deducing conclusions from premises, can be transposed effectively to general tasks. These tasks, like MMLU, although not explicitly logical, still necessitate a structured sequential processing of information, which the model is better equipped to handle post logic-tuning.*\n\n**Improved Discrimination in Ambiguity:**\n\n*Many general tasks present situations with subtle nuances or ambiguous information. Logical grounding hones the model's ability to discern these nuances by emphasizing precision and clarity. Consequently, when faced with ambiguous statements or choices in general tasks, the model is better positioned to discriminate and select the most plausible option.*\n\n**Transfer of Abstract Reasoning:**\n\n*While logical reasoning is concrete and rule-based, it also introduces the model to abstract thinking patterns. Logic-tuned models are better at identifying patterns, drawing parallels, and applying abstract concepts in varied contexts, making them well-suited for general tasks that demand similar abstract reasoning abilities.*\n\n**Reduced Susceptibility to Confounding Factors:**\n\n*Logical reasoning often necessitates filtering out irrelevant information to arrive at a valid conclusion. This ability is crucial in general tasks where the presented information might be abundant, with only a fraction being pertinent to the problem at hand. The logic-tuned model, having been conditioned to prioritize relevant details, showcases better performance by effectively navigating through confounding data.*\n\n\n\n**4. Paper Presentation and Storytelling:**\n\n**Response:** We acknowledge your concerns about presentation clarity and coherence in the storytelling. We have made concerted efforts to refine the narrative flow, back claims with evidence, and address the issues you've highlighted in the 'Questions for the Authors' section.", "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": "1. The paper identifies a critical problem in the subfield of instruction-tuning LLMs: using a shared set of tasks and instructions, neglecting important data such as logic reasoning. (assuming that the literal review is complete)\n2. The paper takes a reasonable step to tackle the issue by contributing an instruction tuning set consisting of examples from logic-related datasets.\n3. The paper shows favorable results of tuning smaller LLMs on the proposed data.", "reasons_to_reject": "1. In my opinion, Section 3 requires some work for the the paper to publishable in EMNLP. Specifically, \n  - What is the rationale behind choosing the 8 types that you did? Some intuition, design principals, or citations to follow would be necessary to make this choice seem less random. \n  - Why not use the exact same task format as the datasets chosen? Why bother with introducing the finer-grained types? What is the advantage of doing so?\n  - How exactly are the examples from your chosen datasets (e.g., EntailmentBank, FOLIO...) converted to these types? \n2. The results in Section 4 is only half-satisfactory to me. The crux of this paper is to show that using logic-related data for instruction-tuning is superior to using other kinds of data in previous work. There are at least two issues with the current paper:\n  - The results in Table 5 is informative but would greatly benefit from a further comparison against other finetuned LLaMA models (not just base), such as the already considered LLaMA-30b-supercot or even more of them. It comes at little surprise that finetuning LLaMA, either on logic-related data or not, brings about better performance on tasks. But if it can be shown that logic-related data is more evaluable than other instruction data, then the story would be a lot more interesting.  \n  - The baseline can be chosen better in two ways. First, one should clearly spell out what instruction data any model is finetuned with, so that there is a comparison between that and your logic-related data. Second, there should ideally more baselines with the same LLM (namely LLama 7B) than just 1-2. The second point is a plus and not a deal-breaker. \n3. The discussions (primarily in Section 4.4) are not enough in-depth. \n  - Minimally, the types introdudeced in Sec 3.2 should be discussed here as to what matters for performance and what not. \n  - It is more or less expected that logic-tuned models would do better on logic-tasks, but it would be very interesting to understand how do they also perform better on general-tasks like MMLU, beyond simply case studies.\n4. The paper presentation could be improved. There is a slew of issues regarding unsupported claims and unconcentrated storytelling throughout the paper. Some of the examples are illustrated in Questions for the Authors.", "questions_to_authors": "- Figure 1 and Table 1 are very hard to read due to overly small font sizes\n- L354: Type 5 was never defined\n- L383-386: why are the instruction types unique to this data generation scheme? \n- L397-411 is confusing and probably can be presented differently, e.g. with textual descriptions. Why are there 2 cases of with and without output? What exactly is point 3 doing?\n- L420-421: why discuss whether they are seq-to-seq? What does GPT4 working well on those mean for the story of this paper?\n- L430: where is the evidence showing its diversity and detailedness?\n- Table2: why not also show the types within multiple-choice (i.e., Identify the Necessary Claim, etc.)? As the number of multiple-choice is already a lot smaller than the General Inference ones, would it be an issue that it is further classified into more fine-grained types?\n- How does Figure 2 contribute to the claims/story of the paper? If to show diversity, then what should it be compared against?", "paper_topic_and_main_contributions": "The paper starts by pointing out an issue that most work that instruction-tunes LLMs use a common pool of instruction data, which neglects logical reasoning. It then proposes to obtain such a pool of logic-related instruction data which can be used to finetune LLMs. From a set of existing datasets regarding logical reasoning, the paper converts their examples into 8 types of instructions, for which GPT4 is used as an oracle/teacher to produce reponses and rationales. These responses and rationales are then used to finetune a smaller Llama-7B model, which is shown to greatly outperform other finetuned Llama models like LLaMA-30b-supercot on the LogiEval dataset. It is also shown to outperform the base model on the MMLU dataset that is more general and less logic-related. Further experiments show that GPT4, the teacher model, still dominates in performance, whereas the finetuned 7B model is comparable to ChatGPT.", "excitement": 3, "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": "- L414: two or three datasets? \n- L520: varies -> variation"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper investigated in the construction of a new instruction tuning dataset for logical chain-of-thought reasoning with GPT-4. The authors showed the competitive logical reasoning and general inference abilities with the proposed approach. This paper addressed an important research problem for instruction tuning of LLM with logical reasoning. The reviewers found the experiment results strong and convincing. On the down side, two reviewers expressed concerns on the experiment setting, related to choice of reasoning types, comparison to baseline models, and the potential data contamination. One reviewer commented on the lack of the evaluation on the quality of the proposed dataset. The authors were able to provide the human evaluation of the dataset which the author should consider add to the revised version of the paper. The paper presentation also has room for improvement as commented by two reviewers.
Decision: Accept-Findings