FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4
Authors: Jeonghwan Kim
Giwon Hong
Sung-Hyon Myaeng
Joyce Jiyoung Whang
Venue: EMNLP 2023 Findings (2023)
Keywords: Large Language Model
Prompt Learning
Fine-tuning
Compositional Reasoning
Question Answering
TL;DR: This study focuses on enhancing GPT-4's compositional reasoning abilities by transferring task-specific inductive biases from fine-tuned models to prompts

Abstract: Compositional reasoning across texts has been a long-standing challenge in natural language processing. With large language models like GPT-4 taking over the field, prompting techniques such as chain-of-thought (CoT) were proposed to unlock compositional, multi-step reasoning capabilities of LLMs. Despite their success, the prompts demand significant human effort to discover and validate them. Our work draws attention to the idea of transferring task-specific inductive biases from finetuned models to prompts, as a way of improving GPT-4's compositional reasoning capabilities. To leverage these inductive biases, we formulate prompt templates to ease the transfer of inductive biases. The experimental results on multi-hop question answering and numerical reasoning over text show that our proposed prompt scheme shows competitive zero-shot and few-shot performances compared to existing prompts on complicated reasoning tasks, highlighting the importance of adopting the validated biases of the previous paradigm.

FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4
Jeonghwan Kim 1* Giwon Hong Sung-Hyon Myaeng²
Joyce Jiyoung Whang University of Illinois Urbana-Chie Chanpaaiin University of Edinburgh "KAIST
jk1 000i lereiillinii...mm ..l.iiios.duu, g. hong@sms. ed. ac.uk
{myaeng, (eanaang)kaist..... ac.
Abstract
Finetuned Models
FinePrompt
Compositional reasoning across texts has been a long-standing challenge in natural language processing. With large language models like GPT-4 taking over the field, prompting tech- niques such as chain-of-thougg (CoT) were proposed unlock compositional, multi-step step reasoning capabilities of L.LMs. Despite their success, the prompts demand significant hu- man effort to discover and validate them. Our work draws attention to the idea of transfer- ring task sask-specific inductive biases from fine- tuned models prompts, way of improv- ing GPT-4's compositional reasoning capabil- ities. To leverage these inductive biases, we formulate prompt templates to the transfer of inductive biases. The experimental results on multi-hop question answering and numeri- cal reasoning over text show that our proposed prompt scheme shows competitive zero-shot and few-shot performances compared to exist- ing prompts on complicated reasoning tasks, highlighting the importance of adopting the idated biases of the previous paradigm.'
Task
2.71 (10.87
Attribute- Infused Prompt
specific Attributes
Mexit, 71
Pipeline
ro Lo
Pipeline Infused Prompt
Structural Reasoning
Graph Infused
Prompt
Figure 1: FinePrompt transfers the existing specifc inductive biases into natural language prompts, guided by the transfer template proposed in this work.
strategies such as Chain-of-Thought (CoT) (Wei et al., 2022), Self-Ask (Press et al., 2022) and Least- to-most Prompting (Zhou et al., 2022). These prompting techniques have effectively unlocked the compositional, multi-step reasoning capabili- ties of LLMs by generating step-by-step rationales or breaking down end task into series of sub- problems. Regardless of their efficacy in improving
LLM reasoning, these prompting techniques still entail (i) significant amount of human effort to dis- cover the right prompting strategy, and (ii) lack task specificity that takes into account the characteristic differences between end tasks.
1 Introduction
Large language models (LLM) such as GPT-4 (Ope- nAI, 2023) have demonstrated impressive capabil- ity to solve textual understanding problems level parallel or surpassing state-of-the-art task- specific models (Brown et al., 2020; Chowdhery et al., 2022). However, one of the characteristic pitfalls of LLMs that they exhibit poor zero-shot and few-shot performance in tasks such as multi- hop reasoning (Press et al., 2022) and numerical reasoning over text (Brown et al., 2020; OpenAl, 2023), both of which involve compositional, multi- step reasoning across multiple referents in text.
Prior to LLMs and prompt learning, many task- specific finetuned LMs proposed a novel of in- ductive biases improve the compositional reason- ing capabilities finetuned LMs (Min et al., 2019; Groeneveld et al., 2020: Tu et al., 2020; Fang et al., 2020; Ran et al., 2019; Geva et al., 2020; Chen et al., 2020a,b) on tasks like multi-hop question answering (MHQA) (Yang et al., 2018; Ho et al., 2020; Trivedi et al., 2022) and numerical reasoning over (Dua al., 2019). For example, NumNet (Ran et al., 2019) injected the strict inequality in- ductive bias into LMs to significantly improve its performance on DROP (Dua et al., 2019), while De- compRC (Min et al., 2019) divided the multi-hop
To overcome such limitation of LLMs, previ- ous works proposed various elicitive prompting
"Equal contribution
Cooresponndigg author
'Code at: http:///gthhbboommii//hs//////o/

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You will be given document preceded by "Document"" question
You will be given set of evidence documents, hop question
You will be given of evidence documents, multi hop question
Numbers have specific relationshiis. where the "<" symbal represents
Decompose the given multi-hap question into three types of Generate
The sentences prefixed with paregreph sentence
symbol represents greater
sub-questions Ganerate the plausible answer with question type .M.
The prafixes can connact sentonces, There are three connection types:
1) "Question":
57
Attributes that can be useful for the glven task
"Intre": ...
Nodes
2.71 10.87
producer Dangerous Ground study or work? [Bridging]
Question: Where
"Inter":
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P255 linter: P4S2): We
Subrquestion Who is the producer of Dangerous Ground?
Callege,
points ware the most in franchise history until Week & of the 2017 season, when they again def defeated the Titans 57-14..
Document: and
John useemn PP522 (Question:
Answer: John Houseman
Inter: P255): On gerous Groun produced by John touseman.
Sub question Where John
Houseman study or work?
Question: Which team scored more points, Texans Eagles?
Answer: Clifton Collage, London
Auducer Dangerous
[Question: P4S2]; Where
Question Type: Bridging
study Dn
Answer: Texans
Answer: Clifton College, London
Answer: Clifton College
(a) Attribute-Infused Prompt (b) Pipeline-Infused Prompt
(c) Graph-Infused Prompt
Illustration of FinePrompt. Each box includes Task-specific Instruction, Finetuned Instruction, and In-context Samples & Test Input. (a) Attribute- Infused Prompt injects set of end task task-specific features. (b) Pipeline- Infused Prompt guides the model to break down complex end task into series of subtasks and solve them iteratively. (c) Graph-Infused Prompt infuses the graph's 's connectivity information within the input text.
Figure 2:
questions into set of decomposed sub-problems to improve the performance on MHQA. However, these finetuning-base based methods are difficult to im- plement in LLMs like GPT-4 because such LLMs are massive in size and their parameters are of- ten inaccessible due to the proprietary nature. In this work, we show that the finetuned models, or more specifically, the set of inductive biases used in such models, can serve as prompt materials to improve GPT-4's compositional reasoning as illus- trated in Figure 1. Our contributions are threefold; (i) Reproducibilityy:: adopting the previously val- idated finetuned features into prompts to improve (ii) Systematicity: providing a template process to turn a finetuned model into a prompt. (iii) Enhanced performance: some of the transferred models exhibit strong zero-shot and few-shot capabilities in MuSiQue and DROP.
Sections 2.1 to 2.3 as shown in Figure 2.
In each section, we describe which character- istic of a finetuned model aligns with one of the three prompt-infusion strategies; AttributeIInfused ($2.1), Pipeline- Infused ($2.2) and Graph-Infused Prompts ($2.3). Note that inductive biases of fine- tuned models can be manifested in various forms, because they can be derived from one of the fol- lowing strategies: (i) integrating additional, task- related features into the models through training (e.g., learning to solve basic arithmetic problems prior to solving complex textual numeric problems (Geva et al., 2020)). (ii) formulating pipelined process that decomposes a complex reasoning task into sequential set of sub-problems, and (iii) in- corporating external graph structures to leverage the connected, structural inductive bias.
the LLM reasoning.
Our work, which aims to transfer the central inductive biases into prompts, directly adopts the previous works (Geva et al., 2020; Ran et al., 2019; Tu et al., 2020; Chen et al., 2020a) to minimize the human effort of extracting the features previously leveraged in these models. For example, as shown in Figure 2, while we have to manually construct the Task Instruction and Finetuned Instruc- tion chunks in the prompts, we can simply adopt the code bases of previous models to extract the necessary features used in In-context Samples.
2 FinePrompt
We propose transfer validated finetuned features into prompts (hence, the name FinePrompt) to investigate whether (i) the finetuned features, in forms of prompts, have the same effect of improv- ing the performance of GPT-4 on textual compo- sitional reasoning tasks, and (ii) how the various models/approaches can be effectively transferred to structured prompts. To transfer the features into prompts, we divide models by their properties from
2.1 Attribute-Infused Prompt
Thee term reproducibility in our work is used to emphasize our contribution that the previously validated finc-tuned ideas can directly he "reproduced" in form of prompts.
Attributes are a set of task-specific features con- ducive to the end task that provide prerequi-

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site knowledge. For instance, in order to per- form numerical reasoning over text, the model needs to know beforehand how to perform addi- tion/subtraction (Geva et al., 2020) or the definition of strict inequality (Ran et al., 2019) in order to perform a higher-order, compositional reasoning over numbers appearing in text. We define such task-specific features as attributes and formulate them as follows. Given language model fo(X;;), 0), our prompt imput can be defined as:
into their corresponding pipeline-infused prompt. Note that includes the decomposition process directly adopted from the existing code base of previous works, providing the decomposed sub- questions, sub-answers and evidences to form c(s;).
2.3 Graph-Infused Prompt
Graphs are often used by finetuned LMs through GNN-based modules (Tu al., 2020; Chen et al., 2020a) to exploit the connectivity information among textual units (e.g.. entities, sentences) that help the LM perform multi-step reasoning. To pro- vide the features conveyed by graphs, we transfer the graph into prompts by identifying nodes within texts and directly inserting an edge preceded by each node as shown in Figure 2(c). Our graph prompt X is defined as follows:
X (LI || Pattr (x),2;))
(1)
h 0 { (1 82: 8k)
S₁
(2)
if = 0
where 7 is task-specific and finetuned instruc- tion (for full instruction see Appendix D), Pattr is the task-specific attribute (e.g., Net (Ran et al., 2019)), and Sy is the optional k- shot in-context sample from the end tasks' train- ing dataset. Note, Pattr can either be in-context sample dependent or not, depending on how the model being transferred used these features. For instance, NumNet leverages the relative magnitude difference between numbers extracted from each sample, whereas GenBERT trains the model on an array of arithmetic tasks (e.g.. 19517. 44 17484 10071. 75 1013.27 21 -7025. 14). z; is the 7th end task input. denotes the concatenation opera- tion. Unlike CoT or Self-Ask, which require man- ual human annotation of the rationale for the few- shot samples, our prompt simply provides Pattr and 8; the LLM without any manual annotation.
in Num-
X = ([I (s)),
(4)
if > 0 S (o(5))) ((5s)) if = 0
(5)
0
where g is a text conversion function that di- reetly injects node-to-node information into the in-context sample 8; and test input xq. It is worth noting that we do not manually construct the graph or identify nodes present in texts; directly adopt the graph structures provided by previous finetuned models (Tu et al., 2020; Chen et al., 2020a) and the code bases thereof, an automatic process that does not necessitate manual annotation. The nodes (c.g., sentences) supplied by previous works are directly injected into texts in the form of an indicator token, e.g., P2S5 along with the edges which are con- structed proposed in the finetuned models and appended to each node, e.g., connecting sentence nodes based on entity overlap (Tu et al., 2020), connecting an entity with a number if they appear within sentence (Chen et al., 2020a).
2.2 Pipeline-Infuue Infused Prompt
Pipelines that break down complex end task into a series of sub-tasks take the necessary inductive bias (i.e., decomposition) into account. Such biases are especially useful when addressing complicated, multi-hop QA tasks such as MuSiQue (Trivedi et al., 2022). While existing prompting techniques (Press et al., 2022; Zhou et al., 2022) also decom- pose questions into tractable sub-questions, our pipeline-infused prompts derive directly from ex- isting pipelines implemented by previous works (Min al., 2019; Groeneveld et al., 2020), reusing the already validated approach as prompt. The pipeline-infuse infusee--dceddd prompt input X can be defined as:
Experiments
3.1 Settings
Models We use GPT-4 as our target LLM in this work, as is the most powerful LLM on composi- tional reasoning tasks among other LLMs (OpenAI, 2023). Our experiments are conducted on GPT-4 using OpenAl API, so there is no finetuning of the model involved in this work. We consider the fol- lowing finetuned models. GenBERT (Geva et al.,
= (17 St). 24)
(3)
where S₁ = (c(s1), cl.2)....( c..,((())) and e is the conversion function that converts sho- samples
Sp2S5 denotes the fifth sentence in the second paragraph.

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Zero-shot
Few-shot (k 3)
Ans. EM
Ans. F1
Ans. EM
Ans. F1
GPT-4
46.41 +0.29
67.90 #0.32
73.20 +2.27
80.50 +1.45
Baselines
Self-Ask
49.14 10.51
62.82
10.51
61.17
12.77
75.76 12.45
CoT
69.99 +0.45
81.16 +0.31
65.44 +4.52
77.89 +2.05
GenBERT
77.81 +0.63
84.61
+0.43
77.51
+2.57
83.34 1183
AttrbbuteInfused Prompt
NumNet
61.79 +0.29
75.46 +0.37
75.06
+0.89
81.72 +0.41
Graph-Infused Prompt
QDGAT
52.73 +0.66
70.36
+0.42
70.86
+3.17
75.58 +5.58
Table 1: Results of FinePrompt on our sampled DROP dev (256 instances). We provide the averaged score and standard deviation over 5 different iterations, each with different few-shot sample set.
2020) finetunes on synthetic datasets composed of tasks like additiion/subraation and argmax/argmin. NumNet (Ran et al., 2019) finetunes on number rep- resentations to infuse the strict inequality bias into the model. DecompRC (Min et al., 2019) decom- poses multi-hop question into different types of decomposition, generates answer and evidence per type, and scores them get the final answer. QUARK (Groeneveld al., 2020) independently generates sentence per retrieved paragraph and uses the sentences as context for the final answer. QDGAT (Chen et al., 2020a) is a model with entity- number and number-number graphs to leverage the relationships among numbers and entities. SAE (Tu et al., 2020) is model with graph of sentence nodes that uses the sentence-level connections be- tween (and within) paragraphs. Details about the models including CoT and Self-Ask, and hyperpa- rameters are given Appendix A.
MuSiQue (Trivedi al., 2022), the task requires the model to perform both the answer generation and the evidence paragraph prediction. To accom- modate both tasks and accurately measure whether the generated output sequence contains the answer string, we use answer F1 (Ans. F1) and supporting paragraph Fl (Sup. F1). The supporting para- graph F1 adopts the implementation by Trivedi et al. (2022).
3.2 Results
In Tables and we provide our results on both the zero-shot and few-shot settings over the two compositional reasoning tasks. Our evaluations span two axes: reproducibility of the prompts and their effect on compositional reasoning capability.
Reproducibility On both datasets, all our pro- posed prompts improve markedly over the base GPT-4, demonstrating the same effect the finetuned models exhibit when they incorporate the same in- ductive biases into their models. Although this work does not exhaustively explore all finetuned models, this result hints at the possibility of incor- porating other previously effective inductive biases into prompts to improve the LLM reasoning ability.
The datasets used in this experiment are a multi-hop QA dataset, MuSiQue (Trivedi et al., 2022), and numerical reasoning over text dataset, DROP (Dua et al., 2019). Due to the heavy penses incurring from evaluating on the full evalu- ation datasets, we sample 256 instances from each dev set as in previous works (Le et al., 2022) and iterate over them for 5 times to address variance. To investigate both the zero-shot and the few-shot performance of the prompt schemes, we test our proposed schemes and baselines along the two axes; the number of shot,hot, h = 3 follows previous work's setting on DROP (OpenAI, 2023).
Datasets
As shown in Table 1, attribute-infun prompts, especially GenBERT, excel in both the zero-shot and few-shot settings on DROP. While Self-Ask and CoT im- prove GPT-4's performance in the zero-shot setting, they show increased variance in the few-shot set- ting. This provides stark contrast the attribute- infused prompts, as they outperform other base- lines in the few-shot setting. The graph-infused prompt also improves the numerical reasoning abil- ity, demonstrating that graphs' usefulness, such as
Compositional Reasoning on DROP
Metries The metrics used for DROP (Dua et al.. 2019) are answer exact match (Ans. EM) and F1 (Ans. F1), as the task is essentially a QA that deals with the text match of the generated answer. For

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Zero-shot
Few-shot (k 3)
Ans. F1
Sup. F1
Ans. F1
Sup. F1
GPT-4
62.41 =0.50
82.21 +0.21
65.34 #0.36
75.44 +0.29
Baselines
Self-Ask
26.63 -0.57
69.50 11.20
CoT
56.40 1.44
66.33 +0.73
DecompRC QUARK
76.67 =1.0
94.18
+0.62
71.74
=0.71
86.70 +0.57
Pipeline-Infused Prompt
40.17 =0.74
53.73
+0.31
58.53 11.64
75.60
+0.62
Graph-Infused Prompt
SAE
71.90 =0.64
80.00
+1.36
71.11 11.54
72.75 +2.76
Table Results of FinePrompt on our sampled MuSiQue dev set (256 instances). Self-Ask and CoT not perform supporting paragraph prediction. We provide the averaged score and standard deviation over 5 different iterations, each with a different few-shot sample set.
connections between different textual units, can effectively be infused to LLMs via prompt.
ing it does not deal with compositional reasoning in textual understanding setting.
Compositional Reasoning MuSiQue For multi-hop reasoning Table 2, both the pipeline and graph prompts outperform other baselines, ex- cept for QUARK. The performance drop after ap- plying QUARK's pipeline prompt suggests that, unlike DecompRC which decomposes the question into a series of sub-questions, QUARK indepen- dently interprets a paragraph using the multi-hop question, which is not helpful in reducing the com- plexity of multi-hun hopti-hop reasoning. Moreover, SAE's performance improvement after injecting the graph suggests that even without the lengthy pipeline ap- proach, textual graph prompts better elicit the com- positional reasoning ability from the LLM.
5 Conclusion
This work studies the transfer of validated induc- tive biases from finetuned models to prompts and their effectiveness compositional reasoning of GPT-4. Our empirical results suggest that (i) end task-related attributes and graphs help elicit robust multi-step reasoning capability from LLMs, and (ii) previous finetuned model pipelines, if they involve decomposing a task into smaller sub-problems, are also effective for prompting. Our work sug- gests that more can be exploited from the previous pretrain-then-finetie
models and our proposed template can incorporate those features seemlessly into LLM prompting paradigm. We hope future works can explore further this direction and eas- ily leverage the power of LLMs with FinePrompt.
4 Related Works
Task-Specifie Models On tasks such as MuSiQue (Trivedi al., 2022) and DROP (Dua al., 2019), numerous models have been proposed enable multi-step, compositional reasoning (Min et al., 2019; Groeneveld et al., 2020; Tu et al., 2020; Fang et al., 2020; Ran et al., 2019; Geva et al., 2020; Chen et al., 2020a,b). These models, prior to the LLM prompt learning paradigm took their place, proposed various novel ideas on this matter.
Limitations
Limited Dataset Size Using GPT-4 for our study incurs substantial cost because of its price ($0.03 per 1,000 tokens), which led us to randomly sam- ple 256 instances from the evaluation sets of both datasets instead of evaluating on the full dataset. Following other previous works (Bai et al., 2023), we deal with reduced dataset size to address the high-cost of using OpenAI GPT model APIs.
Prompt Learning With prompting techniques like CoT (Wei et al., 2022), Self-Ask (Press et al., 2022) and Least-to-most prompting (Zhou al., 2022) having shown to improve LLMs' composi- tional reasoning ability, our work explores how these prompts compare against the FinePrompt scheme. We do not deal with Least-to-nes mostt prompt-
Symbolic Compositional Reasoning Datasets While our work deals with compositional reasoning datasets within the textual understanding environ- ment, there are other tasks like last-letter concate- nation task (Wei et al., 2022) and action sequence prediction such as SCAN (Lake, 2019). However,

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they do not deal with textual context understanding, Future works may explore other models on these end tasks as well.
learners. Advances neural information processing systems, 33:1877-1901.
Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xi- aochuan, Yuyu Zhang. Le Song. Taifeng Wang, Yuan Qi, and Wei Chu. 2020a. Question directed graph attention network for numerical reasoning over text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6759-6768, Online. Association for Computa- tional Linguistics.
Extension to other LLMs While there are other LLMs available such as Alpaca (Peng et al., 2023) and Vicuna (Chiang et al., 2023), which are LLAMA- (Touvron et al., 2023), instruction- tuned models, they use GPT-generated instruction data to finetune their models. We also note that such LLMs are too compute-heavy to train in our local environment.
Xinyun Chen, Chen I Liang, Adams Wei Yu, Denny Zhou, Dawn Song, and Quoc V Le. 2020b. Neural sym- bolic reader: Scalable integration of distributed and symbolic representations for reading comprehension. In International Conference on Learning Representa- tions.
Additional Finetuned Models We are aware that there are numerous pretrain- finetundd LMs for MHQA and DROP. Nevertheless, since we cannot exhaustively consider every single model that has been proposed to date, we select a few models that share commonalities as in Sections 2.1 to 2.3 to investigate their impact LLMs prompts.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang. Yonghao Zhuang. Joseph E. Gonzalez, lon Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality.
Manual Annotation Manual annotation is un- avoidable in FinePrompt since it requires a human annotator to understand the central inductive bias of model and translate them into textual prompts. Nonetheless, one of the main contributions of this work, which is to reduce the human effort in search- ing for an effective prompting strategy like CoT (Wei et al., 2022) and Self-Ask (Press et al., 2022) by transferring previously validated inductive bi- ases, holds regardless of the manual effort. More- over, FinePrompt adopts the code and data bases of previous finetuned models to further mitigate hu- man effort of extracting the inductive bias features.
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung. Charles Sutton, Sehastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.
Dheeru Dua, Yizhong Wang, Pradeep Dasigi. Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019. Drop: A reading comprehension benchmark requir- ing discrete reasoning over paragraphs. In Proceed- ings of NAACL- pages 2368-2378.
Yuwei Fang, Siqi Sun, 7he Gan, Rohit Pillai, Shuo- hang Wang, and Jingjing Liu. 2020. Hierarchical graph network for multi-hop question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8823- 8838, Online. Association for Computa- tional I. Linguisties.
Acknowledgements
This research was supported by an NRF grant funded by MSIT 2022R1 (Extendable Graph Representation Learning) and an IITP grant funded by MSIT 2022-0-00369 (Development of AI Technology to support Expert Decision-making that can Explain the Reasons/Grounds for Judg- ment Results based on Expert Knowledge).
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020. Injecting numerical reasoning skills into language models. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguisties. pages 946 946-958, Online. Association for Computa- tional Linguistics.
Dirk Groeneveld, Tushar Khot, Mausam, and Ashish Sabharwal. 2020. A simple yet strong pipeline for HotpoiQA. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing (EMNLP), pages 1939-8845, Online. Association for Computational Linguistics.
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A Additional Settings on Experiments
Zero-shot
Few-shot (k 3)
Self-Ask
26.63 10.37
69.50 =1.20
Hyperparameters & Datasets We explain de- tailed settings such as the hyperparameter setting of GPT-4 and changes to the existing data settings accommodate the LLM. GPT-4 hyperparameters used in this work is as follows: the temperature of GPT-4 set to 0.0 offset randomness during generation. For MuSiQue, there are originally 20 question-related documents for each QA instance. However, due to the context length limit (8K) of GPT-4 and delayed API response, we consider 5 documents; all of which are randomly sampled and consist of 2 gold documents (we adopt the 2- hop question tasks from Press et al. (2022)) and non-gold documents. This setting is unanimously applied to all basclines. For DROP, there no ad- ditional change to the experiment setting as it deals with single paragraph and a question.
w/o context
10.95 10.26
37.85 =0.32
CoT
56.40 +1.44
66.33
=0.73
w/o context 27.36 +0.21
36.84 1.77
Table 3: Additional results on the Self-Ask and CoT performance with and without providing the question- related documents in sampled MuSiQue dev set.
EM
F1
GenBERT finetuned
59.38
71.48
GenBERT 0-shot
77.81
84.61
GenBERT 3-shot
77.51
83.34
SAE finetuned
55.61
54.76
SAE 0-shot
71.90
80.00
SAE 3-shot
71.11 72.75
Table 4: Additional results on the original finetuned GenBERT on DROP and SAE on MuSiQue against FinePrompt counterparts compare the original finetuned models and FinePrompt. Each FinePrompt variant denoted by the number of shohot samples used.
Models We adopt the Self-Ask (Press et al.. 2022) and CoT (Wei et al., 2022) prompt tech- niques directly from their papers. While exper- iments conducted by Press et al. (2022) do not take into account the provided question-related doc- uments (contexts) from MuSiQue (Trivedi et al., 2022), for the fairness of comparing the effective- ness of prompts, report in Table 2 the results of prompting with the contexts.
Few-shot In-Context Samples For our Few- shot (A: 3) setting in Tables 1 and 2, we ran- dom sample k instances from the training datasets of DROP and MuSiQue for 5 times. With total of 15 randomly sampled instances, construct k-shot in-context samples for CoT and Self-Ask as both requires humans to provide an intermediate rationale to a question cach sample.
For QDGAT, as the official code the Github repository of the model was not replicable, we implement the QDGAT graph generation with Stanza (Qi et al., 2020); it is used to extract en- tities and numbers from each document. Note that "DURATION" number type is removed in the process as Stanza does not support the num- ber type. This leaves 7 number types (NUMBER, PERCENT, MONEY, TIME, DATE, ORDINAL. YARD). Moreover, order adopt the two es- sential connections from the QDGAT graph, entity- number and number-nun number-number edges, without having to modulate a prolonged text, we denote number- number edges as group after NUMBER-NUMBER: (e.g., HOMBER-NUMBER YARD).
manually
B
Additional Experiments: FinePrompt and finetuned Models
While our work seeks to investigate the effective- ness of the validated, finetuned inductive biases in the form of prompts, we provide additional exper- iments on how the finetuned models used in this work fare against their FinePrompt counterparts. We have conducted additional experiments with GenBERT on DROP and SAE on MuSiQue to com- pare the original finetuned models and FincPrompt (shown in Table 4). The results demonstrate that our FinePrompt scheme outperforms its original finetuned counterparts, exhibiting the potential understudy the finetuned models in a low low-resource setting by leveraging LLMs.
On NumNet, we note that while it uses GNN to finetune number representations, it does not the connectivity inductive bias in other graph leveraging models like SAE and QDGAT, There- fore, we add number-specific features of NumNet as catribute-infusee prompt, not Graph-infused prompt.

---

C CoT and Self-Ask without Contexts
Base Instruction for DROP (Dua et al., 2019)
You are a question answering machine that answers question based on a given document. You will be given a document preceded by "Document:" and a question preceded by "Question:". When you generate the answer, simply generate the answer after "Answer:"
Press et al. (2022), the base settings of Self- Ask and COT do not take question-related docu- ments (contexts) into account on the multi-hop question answering task, and do not perform sup- porting evidence paragraph prediction either; they use their parametric knowledge to decompose the given questions and generate answer rationales. However, our models applying FinePrompt re- quire contexts provided by documents, we present the performance of CoT and Self-Ask with contexts for fair comparison in Table
Document:
Question:
Answer:
Instruction for GenBERT al.
To evaluate how the previous elicitive prompt- ing strategies perform in our experimental setting when contexts not provided, we provide additional experiments in Table 3. Providing the question- related documents shows substantial increase in both Self-Ask and CoT, notably in the few-shot setting.
2020)
You question answering machine that answers question based on a given document, You will be given a document preceded by "Document:" and a question preceded by "Question:". When you generate the answer, simply generate the answer after "Answer:".
You will also be given set of related task examples to help you acquire the necessary knowledge answer given question based on the document.
D
Full Prompts
Document:
Question:
Here we provide the actual prompts used in our work. Each prompt, divided into three distinct groups (Attribute, Pipline, Graph), consists of the following format: Task Instruction, (ii) Fine- tuned Instruction, (iii) In-context Samples Test Input. For the Attribute-Infused Prompt, we also inject the Input-related Attributes (see Figure 2 for details). At the end of each instruction, the few- shot samples (optional in case of zero-shot) and the end task query (Question:) will be appended for GPT-4 to responsd to.
Answer:
Related Examples:
19517.4 17484 10071.75 1013.21
-7025.14 14
2) most(1072.1, 17938, 5708.65, 14739.16)
= -1998
3) argmax(toppy 8105.5, cockney 7111.0, nickelic 1463.16, tiredom 6929) toppy
4) most recent(July 16, 134; June 23, 134;
24 July 134; 28 October 134) 28 October
134
In the following, the DROP task instructions will be denoted by blue boxes, whereas the MuSiQue task instructions will be denoted by red boxes. The baselines like Self-Ask (Press al., 2022) and CoT (Wei et al., 2022) are denoted by green as their zero-shot setting shares the same prompt in both datasets.
5) difference in days(April 21, 1381; 13 April 1381)=7
6) percent not photochemist, floodgate, retir-
ingly :: photochemist 0.82%. morningward 54.4%, floodgate 2.0%, reline 0.78%, retir-
ingly 42% = 55.18

---

Instruction for QDGAT (Chen et al, 2020a)
7) Document: "The commander recruited 16426 asian citizens and 15986 asian voters.
You a question answering machine that answers a question based on a given document. You will be given a document preceded by "Document:" and a question preceded by "Question:". When you generate the answer, simply generate the answer after "Answer:"
The commander borrowed foreign groups from the government. The government passed 3 foreign groups to the commander."" Question: How many foreign groups did the commander recruit?
Answer: 10
Some entities and numbers in the provided document can have special connections. There are total of two connection types. I) "ENTITY-UUMEE": Connections between entity and number in the same sentence.
2) "NUMBER-NUMBER". Connections between numbers of the same type. A NUMBER-NUMBER connection rep- resented by specifying the corresponding number type.
Instruction for NumNet (Ran et al. 2019)
Document: ENTITY1 (ENTITY-
You are question answering machine that answers a question based on a given document. You will be given document preceded by "Document:" and question preceded by "Question:". When you generate the answer, simply generate the answer after "Answer:"
NUMBER: NUMBERI) NUMBERI
(ENTITY-NUMBER, ENTITYI).... NUM- BER2 (ENTITY-NUMBER: ENTITY2, ENTITY3 NUMBER-NUMBER YARD)
Question:
Answer:
Numbers have specific relationships as shown in the following examples, where the "<" symbol represents "a<h b" (a is less than b), the ">" symbol represents "a> b" (a is greater than b), and the "=" symbol represents "a b" (a is equal to b):
Document:
Question:
Answer:
5<6
10>6
117 25
978 <79
0=0
1.6
9.0 8.9
2.9

---

Base Instruction for MuSiQue (Trivedi et al, 2022)
PN: Instruction for DecompRC (Min et al,, 2019)
You question answering assistant. You will given a set evidence paragraphs, multi-hop question and you will be asked to do the following:
You are question answering assistant. You will be given set of evidence paragraphs, a multi-hop question and you will be asked to do the following:
1) You will read list of paragraphs (PI,
P2, PN) and multi-hop question ("Question:").
First, decompose the given multi-hop question ("Question:") into all three differ- ent versions of single-hop, sub-question sets (" ""Sub-question 1:", "Sub-question 2:"). The three different question types are follows:
2) You should give the paragraph id you used to derive the answer after "Evidence:".
3) You should provide the answer to the multi-hop question after "Answer:".
1) Bridging Type: requires finding the first-hop evidence for Sub-question 1 to find the evidence to answer Sub-question 2.
Paragraphs:
Pl:
P2:
2) Intersection Type: requires finding an entity that satifies two independent conditions of the two Sub-questions.
Question:
3) Comparison Type: requires comparing the property of two different entities in the Sub-questions.
Evidence: Pi, Pj,
Answer:
Then, given a question, generate the sub-questions, the corresponding answer and the evidence paragraph ids for each sub-question in the following format:
Paragraphs:
Pl:
P2:
PN:
Question:
[Bridging] Sub-question Sub-question 1 Answer:
Evidence: Pi, Pj. ...
Sub-question ... Sub-question 2 Answer:
Evidence: Pi, Pj.
[Intersection]
Sub-question 1: (Sub-question I Answer:
Evidence: Pi, Pj. ...
Sub-question 2: ... Sub-question 2 Answer:
Evidence: Pi, Pj...

---

Instruction for QUARK (Groeneveld et al. 2020
[Comparison]
Sub-question 1: Sub-question Answer:
Evidence: Pi, Pj.
You are a question answering assistant. You will be given a set of evidence paragraphs, a multi-hop question and you will be asked to do the following:
Sub-question 2: Sub-question Answer:
Evidence: Pi, Pj...
Using the previously generated infor- mation about the sub-questions, the answers and evidence paragraphs, generate the most plausible answer to the question ("Question:")) after "Answer:", and also generate which question type your answer is from as follows:
1) You will read list of paragraphs (P1, P2, .... PN) and multi-hop question ("Question:").
2) Find one question-related sentence for each paragraph ("Paragraph:") and write that sentence after "Evidence Sentences.".
Question Type: Answer:
3) Read the given set of sentences after "Evidence Sentences for Pi:", where "i" refers to the paragraph id. This set of predicted sentences will serve your new context to help you answer the question. You should provide the answer the multi-hop question after " "Answer:".
Paragraphs:
Pl:
P2:
PN:
Question:
Evidence Sentences for Pl: Si
Evidence Sentences for P2: Sj
Evidence Sentences for PN: Sk
Answer:
Evidence Paragraphs: Pi, Pj,

---

Instruction SAE (Tu et al. 2020)
Instruction for Self-Ask (Press et al., 2022)
You question answering assistant. of evidence paragraphs, a multi-hop question and you will be asked to do the following:
You will be given
You are a question answering assistant.
1) You will be given a question ("Ques- tion:").
2) Figure out any follow up question is needed ("Are follow up questions needed here:") with "Yes" or "No" answer.
1) You will read a list of paragraphs (P1, P2, PN) and multi-hop question ("Question:").
3) For each follow up question, give the corresponding "Intermediate answer.".
2) You should provide the answer to the multi-hop question after "Answer:".
When you generate the final answer, generate the answer after "So the final answer is:".
3) You should give the paragraph id you used to derive the answer after "Evidence:".
The provided paragraphs and sentences within are prefixed with paragraph numbers and sentence numbers. For example, the prefix "P2S1" indicates the 1st sentence of the 2nd paragraph.
Question:
Are follow up questions needed here:
Follow up:
Intermediate answer:
So the final answer is:
Also, if sentences are related other sentences, prefixes can connect them each other some form of connection. There are a total of three connection types:
Instruction for CoT (Wei et al. 2022)
I) "Question": Connections between sentences that are related to the question.
You are a question answering assistant.
1) You will be given a question ("Ques- tion:").
2) "Intra": Connections between sentences within the same paragraph.
2) Generate an explanation for your answer after "Answer:".
3) "Inter": Connections between sentences that are related but belong to different paragraphs.
When you generate the final answer, generate the answer after "So the final answer is:".
Paragraphs:
PISI (Inter: P2S2 Intra: P1S2):
Question:
P1S2 (Intra: P1S2):
Answer:
P2S1 (Intra: P2S2): P2S2 (Question: Q.
So the final answer is:
P3S2 Inter: P1S1 Intra: P2S1): ...
P3S1 (Intra: P3S2): P3S2 (Question:
P2S2 Intra: P3S1):
PNSI (Question: Q Intra: PNS2):
PNS2 (Intra: PNS1):
Q (Question: P1S1, PNSI):
Answer:
Evidence: P1, P3,


Reviews:
[{"review_id": "AWWEb0W4zs", "reviewer": "Reviewer_cDaD", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable time and effort in reviewing our paper.\n\n> **“This work does not exhaustively explore other fine-tuned models and LLMs, and that there is still room for improvement in incorporating other previously effective inductive biases into prompts to improve LLM reasoning ability.”**\n\nOur study focuses on improving the compositional reasoning ability of GPT-4 in a textual understanding setting, specifically in numerical and multi-hop reasoning. Within the scope of these tasks, most models generally fall within the three categories specified in our work (Attribute, Pipeline, Graph). Furthermore, we aim to uncover the possibility of transferring the fine-tuned inductive biases of previously validated models to natural language prompts and verify if these biases help improve the compositional reasoning ability of GPT-4 in a textual understanding scenario (as demonstrated in Tables 1 and 2). We have elaborated on the concerns of incorporating different fine-tuned models and LLMs in our Limitations section (line 279-293), underscoring the infeasibility of exploring all available fine-tuned models and LLMs in the field. It is also worth noting that this is a short paper and exhaustively considering every single model that has been proposed to date is outside the scope of our work.", "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": "Firstly, it addresses an important challenge in natural language processing, namely compositional reasoning, which is a crucial aspect of language understanding. \nSecondly, the paper proposes a novel approach to improving the performance of GPT-4 on complex reasoning tasks, using prompt templates to transfer task-specific biases. This approach has the potential to enhance the efficiency and effectiveness of natural language processing models. \nThirdly, the paper presents experimental results that demonstrate the effectiveness of FinePrompt on multi-hop question answering and numerical reasoning tasks, and compares it to existing prompt techniques. This provides valuable insights into the strengths and limitations of different prompt strategies. ", "reasons_to_reject": "This work does not exhaustively explore other finetuned models and LLMs, and that there is still room for improvement in incorporating other previously effective inductive biases into prompts to improve LLM reasoning ability. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper addresses the challenge of compositional reasoning in natural language processing, which involves understanding the meaning of complex sentences by combining the meanings of their constituent parts. The main contribution of the paper is the exploration of prompt templates as a way to transfer task-specific biases and improve the performance of GPT-4 on complex reasoning tasks. The paper presents experimental results that demonstrate the effectiveness of FinePrompt on multi-hop question answering and numerical reasoning tasks, and compares it to existing prompt techniques.", "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": {}}, {"review_id": "GjRPF3wCU8", "reviewer": "Reviewer_fz2V", "summary": "", "questions": "", "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": "Thank you for your constructive and thoughtful comments on our work. In the following, we would like to answer any questions you posed.\n\n> **“The claim in lines 148-149 about the performance of GPT-4 is not supported by any references”**\n\nThe Technical Report by OpenAI (OpenAI, 2023) suggests that GPT-4 consistently exhibits superior performance in various fields, including textual compositional reasoning, compared to other LLM variants like GPT-3.5 and PALM. Although we have already cited this in our paper, we will also add the citation in Section 3.1 (lines 148-149) for clarity.\n\n>**“The proposed method is based on the inductive biases of the task-specific fine-tuned models, but the paper does not provide a comparison between different FinePrompt configurations and the original fine-tuned models based on which the prompts were created. Also, the general assessment of the FinePrompt method would benefit from the comparison with the current state-of-the-art methods on MuSiQue and DROP datasets.”**\n\nAs for comparing different FinePrompt configurations, if you were referring to experiment settings such as zero-shot, few-shot ($k$=3) (lines 178-182), we first would like to emphasize that our work seeks to verify the improvement over the GPT-4 baselines (GPT-4, Self-Ask and CoT in Tables 1 and 2) with the injection of the fine-tuned inductive biases. By exploring both the zero-shot and few-shot settings in this work, we demonstrate that the introduction of the inductive biases effectively improves the compositional reasoning ability of GPT-4. \n\nComparing against the fine-tuned models & SOTA models does not validate nor invalidate our approach, because: (i) we only need to compare against the base GPT-4 setting to verify if the inductive biases we extracted are useful or not, and (ii) the fine-tuned models are trained on the benchmark dataset (not a fair comparison to begin with), whereas the prompt-based approaches including FinePrompts utilize few-shot demonstrations only.\n\nWe further elaborate on the details of choosing our models. The criterion for selecting the fine-tuned models in our study is whether they are well-recognized and validated by subsequent works in the field. We also focus on the reproducibility of the fine-tuned models, which is the reason the fine-tuned models used in our work are either SOTA on the official leaderboard (at the time of our submission) or are the ones available for experiments.", "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 presents an interesting idea of improving large language models with insights from fine-tuned task-specific models and provides several experiments to support the claim.", "reasons_to_reject": "- The claim in lines 148-149 about the performance of GPT-4 is not supported by any references.\n- The proposed method is based on the inductive biases of the task-specific fine-tuned models, but the paper does not provide a comparison between different FinePrompt configurations and the original fine-tuned models based on which the prompts were created. Also, the general assessment of the FinePrompt method would benefit from the comparison with the current state-of-the-art methods on MuSiQue and DROP datasets.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes a method to construct prompts for compositional reasoning tasks based on transferring inductive biases from task-specific fine-tuned models to large language model (GPT4). The experimental results on MuSiQue dataset for MHQA and DROP dataset for numerical reasoning show that some types of the proposed prompts outperform existing elicitive prompting approaches.", "excitement": 3, "reproducibility": 5, "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": "nNbC6zrmN5", "reviewer": "Reviewer_wpwG", "summary": "", "questions": "A. How are inductive biases obtained? Is it task-specific or instance-specific? Do inductive biases differ across different fine-tuned models for the same task? If not, this prompting strategy seems to be benefiting from some intermediate annotation of the task itself, rather than having anything to do with fine-tuning.\nB. Does the Pattr you obtained for the numerical reasoning task vary depending on which few-shot examples are being presented to the model?\nC. In section 2.3 (Graph-Infused prompting) how are the nodes within the text identified? Are they manually identified or is this part of the dataset?\nD. How are pipeline-infused prompts different from existing prompting strategies like CoT, self-ask, etc.? It is unclear what you mean by \"..derive directly from existing pipelines, reusing the already validated approach as a prompt..\"\nE. Is fineprompting done on GPT-4 out-of-the-box or one that's fine-tuned for each task?\nF. As per your contribution list, can you explain how this method is more reproducible than others? \n", "limitations": "", "rating": 3, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable time and feedback for our work. We would like to open ourselves up for discussion about the core contribution of this research.\n\n>**“It is unclear to the reader what prompt-infusion method (Section 2) is to be used for which fine-tuned model properties.”**\n\nThank you for pointing this out. In the following points we would like to further elaborate on the associations between the prompt-infusion methods and their respective fine-tuned model properties. Although each method and its corresponding model property is elaborated throughout Sections 2.1-2.3, we could not explain in full detail due to the limited space. We will address your concern in the additional space of our final paper by refining our writing to clarify the method-wise associations to fine-tuned model properties.\n\n- As noted in lines 91-98, Attribute-infused Prompts are based on models that exploit task-related attributes that are conducive to improving reasoning on end tasks. For example, knowing the relative magnitude differences such as  3 < 5 is essential to solve arithmetic problems like 5 - 3.\n- For Pipeline-infused Prompts, the associated fine-tuned models should decompose a complex end task (e.g., multi-hop questions) into simpler subtasks (lines 116-123).\n- Graph-infused Prompts are for models that explicitly leverage graph’s structural information (e.g., using GNNs) along with the fine-tuned language models (lines 130-138).\n\n>**“Are these properties attributable to the model, training objective, or the task itself?”**\n\nThe fine-tuned properties are attributed to the model since we are seeking to exploit the useful inductive biases derived from the model architecture. For instance, QDGAT (Chen et al., 2020a) uses an external GNN to leverage the connectivity inductive bias, from which we derive our graph-infused prompts. NumNet (Ran et al., 2019), also one of the models studied in this work, incorporates the relative magnitude differences between two numbers (e.g., 3 < 5) as the central inductive bias in order to improve its numerical reasoning performance on DROP.\n\n>**“It is unclear what is being used to extract the inductive biases that are infused into the prompts. How do the authors identify such information and what makes it immune to the pitfalls of manual annotation?”**\n\nThe fine-tuned inductive biases, prior to the prompt learning paradigm, were infused into the models through manual, human labor (i.e., code). In accordance with the prompt learning paradigm, our work aims to leverage the same fine-tuned inductive biases in a prompt format while avoiding the time consuming yet useful fine-tuning steps. Furthermore, our work adopts the existing code base of the models dealt in this work, relieving our effort of having to extract the central inductive bias features by hand (e.g., the entities used in SAE are directly adopted into our study without additional work). \nAlso, note that our work provides templates (Figure 2) to ease the process of translating the inductive biases into prompts - an additional contribution of the FinePrompt framework (lines 76-77, 254-258).\n\nTransferring the fine-tuned inductive biases to prompts at this point demands manual annotation since the process requires a human to understand the central inductive bias in a model. Future work on fully automating the prompt construction step, we believe, is worth noting.\n\n>**`Question A` :** **“How are inductive biases obtained? Is it task-specific or instance-specific? Do inductive biases differ across different fine-tuned models for the same task? If not, this prompting strategy seems to be benefiting from some intermediate annotation of the task itself, rather than having anything to do with fine-tuning.”**\n\nThe inductive biases studied in this work are obtained by transferring the main idea of fine-tuned models into prompts. Therefore, each prompt is model-specific and is neither task-specific nor instance-specific. Furthermore, the inductive biases differ among fine-tuned models since each model applies different kinds of model-specific inductive biases to solve a given task (e.g., DecompRC decomposes the multi-hop questions while SAE leverages graphs’ connectivity information to answer those same questions). Various kinds of models employ different fine-tuning strategies to obtain different inductive biases. Such inductive biases, the central contributing ideas of each model, are what’s being transferred to prompts.\n\n>**`Question B` :** **“Does the $P_{attr}$ you obtained for the numerical reasoning task vary depending on which few-shot examples are being presented to the model?”**\n\nYes, the $P_{attr}$ (Equation 1; lines 105-107) obtained for numerical reasoning task depends on the few-shot examples and the input instance being presented to the model. This may result in sample variance, which was addressed by averaging our performance over 5 different iterations of randomly sampled $k$-shot samples (lines 173-182); averaging over multiple iterations is commonly applied to reduce sample variance in LLMs (Zhou et al., 2022).\n\n>**`Question C` : “In section 2.3 (Graph-Infused prompting) how are the nodes within the text identified? Are they manually identified or is this part of the dataset?”**\n\nWe adopt the code base of the existing graph-based models (namely SAE, QDGAT) in identifying the nodes within the text. Therefore, it is neither manually identified nor a part of the MuSiQue dataset. While the methods of identifying the nodes in text may vary from model to model, we directly obtain these nodes based on the existing proposed graphs (SAE, QDGAT). Note that this point also emphasizes the transferability of our scheme on many different kinds of graphs used in previous works.\n\n>**`Question D` :** **“How are pipeline-infused prompts different from existing prompting strategies like CoT, self-ask, etc.? …”**\n\nWhile CoT, Self-Ask and other related approaches provide prompt-centric approches (e.g., dependent on well-curated, few-shot examples), the Pipeline-infused prompts are directly transferred pipelines of the previously validated fine-tuned models. For instance, DecompRC (Min et al., 2019) decomposes a given multi-hop question into three disparate types of questions (Bridging, Intersection, and Comparison Types) and chooses a set of single-hop questions identified by multiple decomposition types, based on which the model derives the final answer. In contrast, CoT entirely relies on human-curated few-shot samples of explanatory steps to get to the final answer, with each explanation demanding manual adjustment to whatever task it’s being used on (lines 110-114).\n>**`Question E` :** **“Is fineprompting done on GPT-4 out-of-the-box or one that's fine-tuned for each task?”**\n\nWe do not fine-tune GPT-4 for FinePrompt. We simply use GPT-4 as a baseline to evaluate and validate our prompting strategy. Our work, given the abundance of existing fine-tuned inductive biases in the field, has a great potential to the research community because FinePrompt does not require the actual fine-tuning step while it benefits from the fine-tuned inductive biases.\n\n>**`Question F` :** **“As per your contribution list, can you explain how this method is more reproducible than others?”**\n\nThe term “reproducibility” in line 74 is used to elaborate our point that the previously validated fine-tuned ideas can directly be “reproduced” in a form of prompts. Our objective is to reproduce the benefits of well-recognized and validated fine-tuned inductive biases from previous works in this new prompt learning era instead of inventing new, human-curated, prompting strategies.", "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": "This work presents some interesting insights into how infusing task-specific inductive biases into prompts can be compelling. Furthermore, it bridges the gap between fine-tuning and prompting, a vital direction given the scale and inaccessibility of present-day LLMs.", "reasons_to_reject": "The paper lacks clarity in different places. First of all, it is unclear to the reader what prompt-infusion method (Section 2) is to be used for which fine-tuned model properties. Are these properties attributable to the model, training objective, or the task itself? Secondly, it is unclear what is being used to extract the inductive biases that are infused into the prompts. How do the authors identify such information and what makes it immune to the pitfalls of manual annotation?\n\nIn summary, I believe this paper could include more details on how fine-tuned models are differentiated and inductive biases are extracted from them. I presume it would be more comprehensible on conversion to a long paper.", "questions_to_authors": "A. How are inductive biases obtained? Is it task-specific or instance-specific? Do inductive biases differ across different fine-tuned models for the same task? If not, this prompting strategy seems to be benefiting from some intermediate annotation of the task itself, rather than having anything to do with fine-tuning.\nB. Does the Pattr you obtained for the numerical reasoning task vary depending on which few-shot examples are being presented to the model?\nC. In section 2.3 (Graph-Infused prompting) how are the nodes within the text identified? Are they manually identified or is this part of the dataset?\nD. How are pipeline-infused prompts different from existing prompting strategies like CoT, self-ask, etc.? It is unclear what you mean by \"..derive directly from existing pipelines, reusing the already validated approach as a prompt..\"\nE. Is fineprompting done on GPT-4 out-of-the-box or one that's fine-tuned for each task?\nF. As per your contribution list, can you explain how this method is more reproducible than others? \n", "paper_topic_and_main_contributions": "This paper explores the idea of transferring task-specific inductive biases from fine-tuned models to prompts, thereby reducing the human effort required for annotation and infusing task-specificity. Prompt templates are formulated to ease the transfer of inductive biases and the proposed scheme shows competitive zero-shot and few-shot performances compared to existing prompting strategies.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "typos: 518, 526\nIt would be useful to include a table that delineates which model/dataset can be attributed to which of the three prompt infusion strategies introduced in this paper."}, "extra_text": {}}]

Author rebuttal:

Meta-review: the authors present an interesting approach to help bridge the gap between finetuning and prompt instructing of models, and show the effectiveness of their approach. The reviewers however agree on the lack of suffient rigour in the evaluation, especially wrt to alternative approaches to prompt tuning to better understand the gains and tradeoff with FinePrompt. The evaluation of GenBERT and SAE in the rebutal helps to address the main concern of a comparison with the finetuned models, and should be in the main text. 
Still there is suffient material to make the paper interesting.  The comments of reviewer wpwG should be addressed in the final text to better clarify the work better.
Decision: Accept-Findings