言語処理学会 第30回年次大会 発表論文集（2024年3月）

Exploring the Challenges of Multi-Step Logical Reasoning withLanguage Models: A Few-Shot Approach to Explainable
Entailment Trees
Bowen Gao1

Shotaro Kitamura1 Naoya Inoue1,2
1 JAIST 2 RIKEN{gaobowen,s2210055,naoya-i}@jaist.ac.jp

Abstract
An Entailment Tree is a type of explainable entailment
task that requires Large Language Models ( LLMs ) toproduce intermediate hypotheses and reasoning steps. Previous studies have focused on using ﬁne-tuned models,including the T5 model, for this purpose. However, ﬁnetuning typically involves high computational training costs
and necessitates a comprehensive dataset to be eﬀective.
This paper explores the use of a decoder-only model combined with few-shot learning. This technique is, facilitatedby a prompt, and is implemented to generate the intermediate hypothesis. Our experiments show that it is challenging
to achieve results superior to those obtained using ﬁnetuned models, including the T5 model. We conducted ananalysis to understand why decoder-only models do not excel in this area, and we hope that our ﬁndings can aid otherresearchers in investigating the potential of decoder-onlymodels for explainable entailment tasks.

1Introduction

The Entailment Tree [1] is an advanced explainableentailment task, incorporating multi-premise steps fromfacts to hypothesis, oﬀering more meticulous and rigorous reasoning than prior tasks like reading comprehension [2, 3], explainable fact veriﬁcation [4, 5] or opendomain QA [6, 7]. Its main challenge lies in methodicallytracing reasoning from facts to conclusion, rather than justpresenting textual evidence.
Initially, researchers utilized the All-At-Once generation method [8], based on the ﬁne-tuned T5 model [9].
However, this approach often failed to generate valid intermediate hypotheses at each step, leading to ineﬀective Entailment Tree construction. To address this, a step-by-stepmethod was considered, incorporating veriﬁcation mechanisms like the Veriﬁer [10], Retrieval-generation Reasoner [11] or Reinforcement Learning [12] to enhance thevalidity and overall performance of each step in the Entailment Tree.
Additionally, extensive research has focused on ﬁnetuning these models with smaller datasets and applying
transfer learning to new problems. Employing few-shotlearning enhances performance on speciﬁc tasks with limited data. Hence, we plan to use GPT-3.5 for in-context
learning in Entailment Tree tasks, anticipating that GPT3.5 will be eﬀective for this purpose.
Inspired by the Chain-of-Thought [13] approach, we believe that instruction ﬁne-tuned language models are wellsuited for multi-step reasoning tasks. Therefore, we aimto employ these ﬁne-tuned models for the Entailment Treetask, which involves more reasoning steps and a more structured approach.
The experiment was conducted using GPT-3.5 to construct the Entailment Tree. After evaluating with the Tree
Alignment Algorithm, the results, although not outperforming the ﬁne-tuned model, were notable. Our method,
utilizing only few-shot learning, remains comparable tothe ﬁne-tuned model in terms of computational and training cost eﬃciently.
Our main contributions are below:• We utilize the use of decoder-only model with fewshot learning for constructing the Entailment Tree.
• We provide analysis and exploit challenges of multistep logical reasoning with Language Models.
We hope that our research can oﬀer a feasibility to use
few-shot in-context learning to deal with explainable entailment task. Besides, we hope that our insights could help
other researchers explore the use of few-shot in-context― 1731 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

learning in dealing with the explainable entailment task.

Lastly, to ensure that the ﬁnal Entailment Tree was the op-2Methodology

timal solution, we employed the beam search algorithm for

2.1

All-At-Once

iteratively generation.
We are also show the part of our prompt to make GPT3.5 generate the intermediate hypothesis for chosen facts:

We employed an instruction ﬁne-tuned language model’’’to generate the Entailment Tree using our speciﬁc prompt.

Tasks

Our objective was for this ﬁne-tuned model to generate theWe are trying to prove a hypothesis from facts

Entailment Tree in an All-At-Once manner. The primary

step-by-step.

content of our prompt is as follows:

Create an intermediate hypothesis based on’’’supporting facts.

Given a hypothesis, generate a proof tree fromRules

given context.

1. The created intermediate hypothesis should

Exampleshypothesis: the difference between atomic mass

be as short as possible.
2. The created intermediate hypothesis must be

and atomic number is the number of the neutrons

based on inferences.

in an element’’’
We want to make GPT-3.5 conclude the intermediate

context: sent1: atomic mass is determined by
the number of protons and neutrons sent2: atomic
number is only determined by the number of protonof an element

3Evaluation
We evaluated our methods and used the evaluation re-proof: sent1 & sent2 -> hypothesis;

sults to compare with ﬁne-tuned T5 output as shown in’’’2.2

hypothesis based on inferences.

Table 1. The gold tree is obtained from dataset, Entail-Step-by-stepmentBank [1].

Due to the meticulous nature of the steps in the Entailment Tree, we hypothesized that all Entailment Trees could

3.1

Setup

be constructed as binary trees. This means that each step

In Entailment Tree task, it consists of average 7.6 nodescould be generated from a pair of supporting facts using
GPT-3.5 ( Figure 1 ).

( supporting facts ) across 3.2 entailment steps ( multihop tasks are usually only consist of 2-3 supporting facts

Besides, inspired by previous work on generating the). Besides, Entailment Tree’s reasoning steps are more

Entailment Tree step-by-step [9, 10, 11, 14], we aimed to

meticulous which means in each supporting fact, it is al-establish rules to verify the validity of generated interme-ways a simple short sentence, and this sentence is typical indiate steps. Additionally, we planned to utilize the beam

proof generation. It is mainly focusing on two dimensions.

search algorithm to iteratively generate the Entailment Tree

The one is about the quality of the created intermediate

step-by-step.

hypothesis and another is about the ﬁnal Entailment TreeAs illustrated in Figure 2, our ﬁrst step was to combine

structure.

and arrange all possible pairs of facts as candidates. Then,As the result, we would like to evaluate these two di-using a prompt, we instructed GPT-3.5 to deduce an inter-mensions ( Intermediates and Steps ) with F1 score and All

mediate hypothesis for each pair of facts. To ensure that

Correct score. It is same with the evaluation metric which

each step brought the generated intermediate hypothesis

is always applied on the Entailment Tree task [1].

closer to the ﬁnal hypothesis, we intended to use SentenceBert to calculate the similarity score between the intermediate and ﬁnal hypotheses. We planned to extract thetop-K intermediate facts with the highest similarity scores.

3.2

Results

Before evaluating our methods, we used GPT-3.5 to evaluate the single step’s reasoning ability to verify its capacity― 1732 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Figure 1

We assumed that all the Entailment Trees’ step can be explained by the binary tree

Figure 2

The structure of step-by-step generating the Entailment Tree with GPT-3.5

to handle explainable entailment task. We extracted all single steps from the gold tree and used GPT-3.5 to generateintermediate hypotheses based on given facts for each step.
We assumed that in each step, the facts are always samewith the gold one, we only used GPT-3.5 to generate intermediate hypotheses. According to the previous work’s

Table 1

Evaluation result. The evaluation result mainly focus
on leaves, steps, intermediates and overall proofs. ( L=Leaves ,S=Steps, I=Intermediates, O=Overall Proofs.
LS
IO
Methods
F1
F1
F1 AllCorrect
Fine-tuned T5
0.99 0.52 0.71
0.35
All-At-Once (GPT-3.5) 0.96 0.36 0.65
0.27
Step-by-step (GPT-3.5) 0.96 0.38 0.59
0.28

evaluation metric, GPT-3.5’s score is 0.879 for intermediate hypothesis generation, indicating its capacity to manage
the Entailment Tree task.
We compared our method which used the GPT-3.5 modelwith All-At-Once generation method using the ﬁne-tuned
T5 model. As shown in Table 1, neither the All-At-Oncenor the Step-by-Step method with GPT-3.5 could outperform the ﬁne-tuned T5 model.
We then utilized our methods in Python to generate theEntailment Tree with the GPT-3.5 model. Additionally,we used Graphviz library to visualize the Entailment Tree
(Figure 3) for ease of analysis of its structure and content. Regard Figure 3, we observed that step1,2 and 3 allused conjunctions to infer new intermediate hypotheses,

ultimately generating a well-structured Entailment Tree.

4Analysis

From Experiment and its results, we concluded that theGPT-3.5 model has certain limitations in dealing with explainable entailment tasks like the Entailment Tree, andthus it cannot surpass the performance of the ﬁne-tuned T5model.
To understand why GPT-3.5 could not outperform theﬁne-tuned T5 model, we manually evaluated the quality ofconclusions generated by the GPT-3.5 model and identiﬁed errors with speciﬁc reasons. These analyses led us toseveral conclusions.

― 1733 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Figure 5

Figure 3

The visualized Entailment Tree

5

Example of the invalid reasoning

Conclusion and future work
Contribution

In our research, we exclusively used in-struction ﬁne-tuned language models for building the Entailment Tree. While our method didn’t outperform ﬁnetuned models, it oﬀers comparable results to the T5 modelin terms of computational eﬃciency. We’ve analyzed andFigure 4

identiﬁed reasons for its limitations and plan enhancements

Example of the common sense problem

in future work. Recognizing the complexity of the EntailCommon sense

Based on the results of Experiment2, we observed that approximately 27% of the samples
showed GPT-3.5 not performing logical reasoning solely
based on the given premises/facts. Instead, It used its own
knowledge to draw conclusions.
For instance, as shown in Figure 4, the model could

ment Tree task, which demands careful step-by-step reasoning.
Future work

To rely solely on instruction ﬁne-tuned

models for generating the Entailment Tree and potentiallyoutperforming ﬁne-tuned models like T5, we need to address speciﬁc challenges:

conclude without an important fact - ”earth is a kind of• Establish a strict and clear mechanism that enables

planet.” - derived from supporting facts. It was challenging

the model to discern which pairs of facts can produce

to make GPT-3.5 understand the importance of this fact

a valid intermediate hypothesis, avoiding invalid rea-for the Entailment Tree’s generation, mainly because thesoning or mere replication of facts.

model did not consider it relevant.

• Implement a rule that helps the model recognize in-

Based on the results from Experi-stances where relying solely on common sense leads

ment 2, among the 40 samples tested, we found that approx-

to errors, guiding it towards more accurate and logicalimately 27% of the samples demonstrated that GPT-3.5

reasoning.

Invalid reasoning

does not perform valid reasoning. As evidenced in Fig-

we believe that the instruction ﬁne-tuned language modelure 5, the intermediate hypothesis generated by GPT-3.5

will show improved performance in handling complex rea-

was found to be unreasonable.
Useless conclusion Based on the results of Exper-

soning problems, such as those involved in the Entailment

iment 2, among 40 samples, we found that approximately

Acknowledgements

20% of the samples indicate that the intermediate hypotheses generated by GPT-3.5 are ineﬀective for further generation in the Entailment Tree task. Specially, these hypotheses are unhelpful because they merely replicate one of thegiven facts. For example, when provided with the facts[’mercury is a kind of planet’] and [’mercury is locatedin the solar system’], GPT-3.5 generated the conclusion[’mercury is located in the solar system’], which is essentially just a repetition of the fact [’mercury is a kind ofplanet’].

Tree task.

This work was supported by JSPS KAKENHI Grant
Number 19K20332.
We thank Surawat Pothong, Yufeng Zhao and Irfan Robbani for their valuable comments.

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― 1735 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).