# Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering
Based on Human Reading Process ††thanks: This paper has been accepted at LREC-
COLING 2024

###### Abstract

Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to
simulate human reasoning and inference processes, achieving proficient
performance in multi-hop QA. However, a gap persists between PLMs’ reasoning
abilities and those of humans when tackling complex problems. Psychological
studies suggest a vital connection between explicit information in passages
and human prior knowledge during reading. Nevertheless, current research has
given insufficient attention to linking input passages and PLMs’ pre-training-
based knowledge from the perspective of human cognition studies. In this
study, we introduce a Prompting Explicit and Implicit knowledge (PEI)
framework, which uses prompts to connect explicit and implicit knowledge,
aligning with human reading process for multi-hop QA. We consider the input
passages as explicit knowledge, employing them to elicit implicit knowledge
through unified prompt reasoning. Furthermore, our model incorporates type-
specific reasoning via prompts, a form of implicit knowledge. Experimental
results show that PEI performs comparably to the state-of-the-art on HotpotQA.
Ablation studies confirm the efficacy of our model in bridging and integrating
explicit and implicit knowledge.

Keywords: Multi-hop QA, Prompt, Implicit Knowledge

Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering
Based on Human Reading Process ††thanks: This paper has been accepted at LREC-
COLING 2024

Guangming Huang1, Yunfei Long1, Cunjin Luo1, Jiaxing Shen2, Xia Sun3
---
1School of Computer Science and Electronic Engineering, University of Essex,
United Kingdom
2Department of Computing and Decision Sciences, Lingnan University, Hong Kong,
China
3School of Information Science and Technology, Northwest University, China
{gh22231, yl20051<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

Multi-hop question answering (QA) represents a challenging task that demands
intricate reasoning and inference across diverse sources to predict a coherent
and precise answer (Yang et al., 2018; Cao et al., 2023). Chain-of-thought
(CoT) emulates the process of human reasoning and inference to generate a
sequence of intermediate natural language reasoning steps that lead to the
final results for complex reasoning problems. By employing prompt-based
learning with CoT on pre-trained language models (PLMs), several studies have
demonstrated proficient performance for multi-hop QA (Trivedi et al., 2022a;
Deng et al., 2022; Zhang et al., 2023).

However, a substantial disparity remains evident between the reasoning
abilities of PLMs and human cognition when it comes to addressing complex
problems. Current studies overlook the establishment of a direct link between
the input passages and the knowledge assimilated by PLMs during the pre-
training phase, considering the cognitive framework of humans (Wang et al.,
2022; Deng et al., 2022; Atif et al., 2023; Cao et al., 2023).

In human reading comprehension studies, Smith (1971) believes that information
sources are often repeated during reading comprehension, leading to redundancy
at linguistic levels, including letter-to-letter, word-to-word, sentence-to-
sentence, and text-to-text. Consequently, readers can reduce their reliance on
explicit information within the reading text by incorporating alternative
sources of information, such as world knowledge (Hagoort et al., 2004).
According to Clarke and Silberstein (1977), readers engage in the
comprehension and question-answering process while reading, drawing upon both
the explicit information conveyed in the text and their pre-existing language
knowledge, background knowledge, and world knowledge derived from that
explicit information. Certain studies have pointed out that a critical factor
in reading ability is what the reader brings to the text, or what is usually
referred to as prior knowledge (Yin, 1985; Baldwin et al., 1985; Abdelaal and
Sase, 2014). The experimental results demonstrate a significantly high
correlation between the high prior knowledge and reading comprehension
(Abdelaal and Sase, 2014).

As an example shown in Figure 1, given a question "Was Morris Lee born in the
capital of Democratic Republic of the Congo ?", human retrieves the
information in the given passages to make a prediction. According to the
auxiliary verb "was" in the yes-no question, human can predict the answer as
"yes" or "no" beforehand, drawing upon linguistic knowledge (as part of
implicit knowledge), even in the absence of information regarding the capital
of Congo.

Figure 1: An example of the significance of implicit knowledge in reading
comprehension.

Consequently, an inseparable and inherent linkage prevails between the
explicit information within the passages and the pre-existing prior knowledge.
The prior knowledge diminishes the reliance on explicit information, thereby
reducing the need for its extensive utilization. Moreover, the harmonious
integration of prior knowledge and explicit information enhances the efficacy
of the reading process, leading to improved comprehension and engagement.

Incorporating insights from the aforementioned human cognition theories, we
propose a novel framework called Prompting Explicit and Implicit knowledge
(PEI) to address the challenges in multi-hop QA. Within this framework,
readers are analogized to PLMs, their prior knowledge represents implicit
knowledge acquired during the pre-training, and the explicit information in
the passages serves as the input context conveying explicit knowledge. While
acknowledging existing disparities between language models and human, and
recognizing potential biases in considering readers as language models, Jin
and Rinard (2023) believe that language models transcend being mere
"stochastic parrots" (Bender et al., 2021), possessing the capability to
acquire semantics during the pre-training.

To effectively utilize these knowledge sources, we employ prompts to capture
explicit knowledge and invoke implicit knowledge. This facilitates the
bridging of these two types of knowledge, thereby improving the performance of
our proposed PEI model for multi-hop QA tasks. Moreover, this approach reduces
the reliance on explicit knowledge present in the input passages by allowing
selective filtering of irrelevant information or "redundancy" unrelated to the
corresponding questions, as per Smith (1971)’s theory. To further substantiate
the contribution of implicit knowledge to our model’s performance, we conduct
an ablation study, the results of which affirm our hypothesis (see Section
4.5).

Illustrated in Figure 2, our proposed PEI framework consists of three
components: 1) a type prompter for identifying and learning the weights of
reasoning types for multi-hop questions; 2) an encoder-decoder PLM is employed
to acquire implicit knowledge by leveraging explicit knowledge, mirroring the
cognition process of human reading process.; 3) a unified prompt-based PLM
integrating both explicit, implicit knowledge and question types for multi-hop
QA.

Our contributions are summarized as follows:

  * •

The proposed PEI framework provides an effective approach for multi-hop QA
based on human reading process, by modeling the input passages or context as
explicit knowledge and PLMs mirroring with human prior knowledge as implicit
knowledge.

  * •

Our proposed PEI model achieves comparable performance with state-of-the-art
on HotpotQA that is a benchmark dataset for multi-hop QA. Notably, our
approach also maintains robust performance on corresponding single-hop sub-
questions and other multi-hop datasets.

  * •

Ablation studies confirm that implicit knowledge enhances the model’s
reasoning ability, supporting our hypothesis for the PEI model, grounded in
the human reading process.

Figure 2: The overview of our proposed PEI framework for multi-hop QA. The
right green dashed block is the type prompter; the top blue dashed block
refers to the knowledge prompter; and the bottom orange dashed block is the
unified prompter.

## 2\. Related Work

### 2.1. Chain-of-Thought Prompting

Prompt tuning has been recognized as an effective mechanism for conditioning
PLMs to utilize relevant knowledge for specific downstream tasks (Lester et
al., 2021; Liu et al., 2023). CoT prompting, a prompt-based approach, has
emerged as a method to recall implicit knowledge from large-scale language
models (LLMs) for complex reasoning. It emulates the sequential and coherent
reasoning process of human thinking by generating intermediate natural
language reasoning steps that lead to the final outcome (Chu et al., 2023).
Manual-CoT (Wei et al., 2022) is designed to elicit the CoT reasoning ability
through manual demonstrations. Subsequently, Kojima et al. (2022) demonstrated
that LLMs can serve as competent zero-shot reasoners, as they generate
rationales that already embody CoT reasoning, by incorporating the incantation
"Let’s think step by step" to facilitate a step-by-step thinking process
before answering questions. An automatic CoT prompting, AutoCoT (Zhang et al.,
2022), leverages diverse question sampling and reasoning chain generation to
construct demonstrations, reducing the needs for human effort. Some studies
explored CoT prompt learning for multihop QA (Wang et al., 2022; Trivedi et
al., 2022a). Inspired by these advancements, our work explores the utilization
of CoT prompting to elicit implicit knowledge from PLMs. In contrast to CoT’s
generation of intermediate natural language steps, our model generates
continuous embeddings as representations of implicit knowledge.

### 2.2. Prompt-based Learning for Multi-hop QA

Recent studies have made significant progress by employing prompts for multi-
hop QA. For example, PromptRank (Khalifa et al., 2023) constructs an
instruction-based prompt that includes a candidate document path and then
computes the relevance score between a given question and the path based on
the conditional likelihood of the question given the path prompt according to
a language model. IRCoT (Trivedi et al., 2022a) interleaves CoT generation and
knowledge retrieval steps to guide the retrieval using CoT prompting. Wang et
al. (2022) introduced an iterative CoT prompting framework, which elicits
knowledge from PLMs step by step utilizing a sequence-to-sequence BART-large
model to recall a sequence of natural language statements for multi-hop QA.
They employed a simple template to convert each triplet (subject entity,
relation, object entity) in the evidence path into a natural language
statement, which collectively formed the final statement. Inspired by their
work, our approach also utilizes an encoder-decoder architecture PLM, i.e.
BART-large, to recall implicit knowledge through iterative prompting. However,
our proposed method differs distinctly from their study in three aspects: 1)
our method does not necessitate the conversion of triple evidence paths into
natural language statements; 2) we leverage input passages as explicit
knowledge to elicit implicit knowledge from the PLM, which was not explored in
their approach; 3) the recalled implicit knowledge in our model is represented
as continuous embeddings rather than natural language statements or lexical
knowledge Huang et al. (2023). Hence, our model does not rely on the natural
language statements derived from the evidence paths.

## 3\. Methodology

### 3.1. Overview

Shown in Figure 2, our PEI framework comprises three components: 1) the type
prompter identifies and acquires the weights associated with specific
reasoning types for multi-hop questions; 2) the knowledge prompter elicits
implicit knowledge through harnessing of explicit knowledge; 3) the unified
prompt-based PLM integrates explicit and implicit knowledge, as well as
question types, providing a comprehensive approach for multi-hop QA.

Pre-training on Single-hop QA. In order to understand the performance
characteristics of current QA models at each step of the reasoning process, we
built our works on a PLM foundation model, i.e. ELECTRA 111The Pre-trained
language models can also be replaced by more competent models. In line with
previous works on prompt learning, we choose ELECTRA. (Clark et al., 2020) on
SQuAD (Rajpurkar et al., 2016), which is a single-hop QA dataset.
Subsequently, the pre-trained ELECTRA model is employed as the backbone PLM
for our type prompter module. By leveraging the trained ELECTRA model on
single-hop, we aim to explore the interaction between the model’s behaviour
and the reasoning process across various hops.

### 3.2. Type Prompter

The type prompter is crafted to facilitate the training process for the
acquired weights of soft prompts, enabling them to effectively capture the
distinctive features associated with various question types. As shown in
Figure 2, yellow blocks $P_{t}$ refer to trainable prompt embeddings; blue
blocks are embeddings stored and frozen PLM.

Given question $Q$, we construct an input sequence consisting of soft prompts
denoted as $\\{P_{t},[CLS],Q\\}$, where $P_{t}$ represents the trainable soft
prompts of the type prompter module. P-tuning v2 (Liu et al., 2021) is
employed to train soft prompts $P_{t}$ acquiring the weights of specific-type
information of the given questions. As shown in Type Prompter of Figure 3, we
initially freeze the PLM and optimize the trainable soft prompt $P_{t}$. After
training, we obtain the trained $P_{t}$ and establish a connection between it
and the unified prompter, ensuring its fixed nature throughout subsequent
operations.

We denote $d$ as the embedding dimension of the language model ELECTRA, $h$ as
the number of layers within the PLM, and $l$ as the length of the prompt
tokens. In this component, the total number of trainable parameters can be
calculated as $\Theta(d\cdot h\cdot l)$. Compared with the fine-tuning
paradigm, the type prompter module based on p-tuning can reduce training
parameters while ensuring that it can capture the type-specific information.
Secondly, it facilitates to transfer the trained weights of $P_{t}$ with the
type-specific information to unified prompter module. Moreover, through the
application of the p-tuning v2, we can effectively capture and learn a broader
range of features than prompt tuning (Lester et al., 2021).

### 3.3. Knowledge Prompter

By capitalizing on the textual information input, the knowledge prompter seeks
to activate and engage the innate prior knowledge possessed by individuals,
thereby facilitating the assimilation of these two information sources for
comprehensive reading comprehension. As illustrated in Figure 2, the knowledge
prompter, an iterative encoder-decoder PLM, retrieves implicit knowledge
through prefix tuning (Li and Liang, 2021). The trainable prompt embeddings,
denoted as $P_{k}^{E}$ and $P_{k}^{D}$, are integrated into each layer of the
encoder and decoder of the PLM, respectively. This incorporation of prompt
embeddings facilitates the efficient retrieval and utilization of explicit
knowledge throughout the iterative encoding and decoding phases.

Given a multi-hop query $Q$ and a sequence of supporting sentences
$S_{n}=[s_{1},s_{2},...,s_{i},...,s_{n}]$, our objective is to retrieve a
sequence of knowledge $K_{n}=[k_{1},k_{2},..,k_{i},...,k_{n}]$ that provides
sufficient information for determining the response to both $Q$ and $S_{n}$,
where $n$ represents the number of supporting sentences. Our focus lies in the
development of prompting methods, where we aim to construct prompts
$P_{k}^{E}$ and $P_{k}^{D}$ to guide the encoder-decoder language model in
recalling the desired knowledge $K_{n}$. Notably, we maintain fixed parameters
for the encoder-decoder PLM, allowing us to direct its retrieval process
through the strategic construction of prompts.

Motivated by the sequential nature observed in multi-step reasoning tasks
(Wang et al., 2022), we adopt an iterative approach as below:

$\displaystyle P(k_{j}|Q,S_{j},K_{j-1})=\prod_{j=1}^{n}P(k_{j}|Q,s_{1},...,$
$\displaystyle s_{j},$ (1) $\displaystyle k_{1},...,k_{j-1})$

$\displaystyle decoder(k_{j})=encoder(Q,S_{j},K_{j-1})$ (2)

where at each step $j$, PLM recalls the next piece of knowledge $k_{j}$
conditioned on the query $Q$ and supporting sentences $s_{1},...,s_{j}$ and
gathered knowledge $k_{1},...,k_{j-1}$.

More specially, when $j=1$, it is written as following based on Equation (1)
and (2):

$\displaystyle decoder(k_{1})=encoder(Q,s_{1})$ (3)

### 3.4. Unified Prompter

As described in Figure 2, we concatenate the unified prompter module and
$P_{t}$, which is with the weights of type-specific reasoning. Moreover, the
implicit knowledge $K_{n}$ from the knowledge prompter module is the
additional inputs. Intuitively, this amalgamation of two information is
capable to enhances the model’s reasoning ability.

In the unified prompter, the trainable prompt embeddings are denoted as
$P_{u}$, while we freeze the trained promopt embeddings $P_{t}$. To preserve
the learned weights of $P_{t}$ from the type prompter, we adopt the identical
architecture of the language model (ELECTRA), allowing for seamless
concatenation of $P_{t}$ with the unified prompter. Subsequently, we perform
joint fine-tuning and prompt-tuning of both the language model and the prompt
$P_{u}$ to optimize the overall performance of the model in the context of
multi-hop question-answering (QA) prediction.

## 4\. Experiments

### 4.1. Dataset and Metrics

HotpotQA (Yang et al., 2018) contains a collection of 113k question-answer
pairs drawn from Wikipedia. Additionally, HotpotQA offers sentence-level
supporting facts that are essential for the process of reasoning, which allows
QA systems to infer with robust supervision and explain the predictions.

2WikiMultiHopQA (Ho et al., 2020) has over 192k samples, including 167k
training, 12.5k evaluation, and 12.5 test samples. The format of the dataset
primarily follows HotpotQA (Yang et al., 2018), but it provides additional
enhancements, including a broader range of reasoning types for questions and
comprehensive annotations of evidence paths associated with each question.

MuSiQue (Trivedi et al., 2022b) contains 25k 2-4 hop questions samples. This
dataset involves the systematic selection of composable pairs of single-hop
questions demonstrating logical connections to generate a set of multi-hop
questions.

Sub-question QA dataset (Tang et al., 2021) was created to facilitate the
analysis of the reasoning capabilities of multi-hop QA models at each step of
the reasoning process. To evaluate the performance of these models, the
authors curated a specialized dataset consisting of single-hop sub-questions.
This dataset comprises 1000 samples manually verified from the development set
of HotpotQA, ensuring high-quality evaluation resource for the study.

In order to ensure consistency and comparability across the datasets used in
our experimental evaluations, we categorize the question types of the three
datasets under investigation into the broader categories of comparison and
bridge. This categorization of question types facilitates a standardized
approach to handling diverse question structures across the datasets subjected
to our evaluation.

Metrics. We employ Exact Match ($EM$) and Partial Match ($F1$) to evaluate the
efficacy and performance of our proposed framework concerning both answer and
supporting facts prediction. Furthermore, the joint $EM$ and $F1$ are used to
assess the overall performance.

### 4.2. Implementation Details

Inspired by the studies of Wang et al. (2022) and Deng et al. (2022), we adopt
BART-large (Lewis et al., 2020) as the language model in the knowledge
prompter module. ELECTRA-large (Clark et al., 2020) serves as the foundation
language model for both the type prompter and unified prompter modules. Our
implementation is built upon the Huggingface platform (Wolf et al., 2020). For
model optimization, we employ the AdamW optimizer (Loshchilov and Hutter,
2018) along with a linear learning rate scheduler with a warmup ratio of
$0.05$.

In terms of hyperparameters, we conduct a search for the optimal batch size.
For the HotpotQA, 2WikiMultiHopQA, and MuSiQue datasets, we explored batch
sizes of $\\{4,8,12,16,32\\}$ respectively. Additionally, we performed a
tuning process for the learning rate, considering values from
$\\{2e-5,4e-5,8e-5,2e-4,4e-4,8e-4,2e-3,4e-3,8e-3,2e-2,4e-2,8e-2\\}$. Moreover,
we conducted tuning experiments for the length of the encoder/decoder prompts
$P_{k}$ and the type prompts $P_{t}$, exploring values from
$\\{15,30,45,60,75,90,100\\}$.

### 4.3. Main Results and Analysis

Models | Ans | Sup | Joint
---|---|---|---
EM | F1 | EM | F1 | EM | F1
Baseline Model (Yang et al., 2018) | 45.60 | 59.02 | 20.32 | 64.49 | 10.83 | 40.16
DecompRC (Min et al., 2019) | 55.20 | 69.63 | - | - | - | -
OUNS (Perez et al., 2020) | 66.33 | 79.34 | - | - | - | -
QFE (Nishida et al., 2019) | 53.86 | 68.06 | 57.75 | 84.49 | 34.63 | 59.61
DFGN (Qiu et al., 2019) | 56.31 | 69.69 | 51.50 | 81.62 | 33.62 | 59.82
SAE-large (Tu et al., 2020) | 66.92 | 66.92 | 61.53 | 86.86 | 45.36 | 71.45
C2F Reader (Shao et al., 2020) | 67.98 | 81.24 | 60.81 | 87.63 | 44.67 | 72.73
Longformer (Beltagy et al., 2020) | 68.00 | 81.25 | 63.09 | 88.34 | 45.91 | 73.16
HGN (Fang et al., 2020) | 69.22 | 82.19 | 62.76 | 88.47 | 47.11 | 74.21
AMGN (Li et al., 2021) | 70.53 | 83.37 | 63.57 | 88.83 | 47.77 | 75.24
S2G (Wu et al., 2021) | 70.72 | 83.53 | 64.30 | 88.72 | 48.60 | 75.45
iCAP † (Wang et al., 2022) | 68.61 | 81.82 | 62.80 | 88.51 | 47.02 | 74.11
PCL (Deng et al., 2022) | 71.76 | 84.39 | 64.61 | 89.20 | 49.27 | 76.56
Beam Retrieval (Zhang et al., 2023) | 72.69 | 85.04 | 66.25 | 90.09 | 50.53 | 77.54
PEI (Ours) | 72.89 | 85.32 | 65.03 | 89.81 | 49.91 | 77.84
Table 1: Results on the blind test set of HotpotQA in the distractor setting.
“-” denotes the case where no results are available. † denotes that we
implement the codes. “Ans” represents the metrics for answer; “Sup” denotes
the metrics for supporting facts; “Joint” is the joint metrics that combine
the evaluation of answer spans and supporting facts. HotpotQA Leaderboard:
https://hotpotqa.github.io/.

We compare our PEI model with other published baselines on the test set of
HotpotQA in the distractor setting, including the baseline model of HotpotQA
(Yang et al., 2018), SOTA model on the leaderboard Zhang et al. (2023), iCAP
Wang et al. (2022) and PCL Deng et al. (2022) which we inspired by, and other
baselines. As shown in Table 1, our proposed PEI model demonstrates superior
performance across all evaluation metrics compared to all other baselines
(except Beam Retrieval), and achieves comparable performance with Beam
Retrieval Zhang et al. (2023) on the HotpotQA dataset, highlighting the
significant progress made by PEI for multi-hop QA.

Specifically, our PEI achieves an improvement of $0.20$ in answer EM, $0.28$
in answer F1 score, and $0.30$ in join F1 score when compared to Beam
Retrieval. Conversely, Beam Retrieval shows an improvement of $1.22$ in
supporting EM, $0.28$ in supporting F1 score, and $0.62$ in join EM when
compared to PEI.

The difference in performance between PEI and Beam Retrieval in answer
prediction versus supporting prediction can be attributed to their respective
approaches. Beam Retrieval maintains multiple partial hypotheses of relevant
passages at each step, expanding the search space (albeit at the expense of an
exponentially complex retrieval process) and reducing the risk of missing
relevant passages. Consequently, it excels in supporting prediction. In
contrast, our PEI model leverages insights from the human reading process,
incorporating implicit knowledge and type-specific information. This approach
contributes to the model’s improvement in answer prediction. However, it may
not exhibit the same level of performance in supporting prediction as Beam
Retrieval due to differences in retrieval strategies.

Moreover, our PEI framework demonstrates a significant improvement of
$0.64/1.28$ in the Joint EM/F1 score compared to the PCL model, which PEI and
PCL both use the same backbone PLM (i.e., ELECTRA). Although PCL also
identified the reasoning type of multi-hop question as a soft prompt via a
transformer-based question classifier, our proposed model not only consider
the type-specific knowledge but also incorporate the implicit knowledge
through iteratively eliciting it from an encoder-decoder PLM. Additionally,
our proposed PEI outperforms iCAP with $2.89/3.73$ in the joint EM/F1 score,
despite both PEI and iCAP utilizing the same encoder-decoder skeleton PLM
(i.e.,BART). Compared to the graph-based model AMGN, our PEI framework
exhibits substantial gains, with an improvement of $2.14/2.6$ in the Joint
EM/F1 score. This indicates that our framework achieves better performance
employing the same backbone PLM.

### 4.4. Evaluation of Robustness

Since our proposed model employs identical backbone models (i.e., ELECTRA and
BART) and similar prompt learning framework as PCL (Deng et al., 2022) and
iCAP (Wang et al., 2022), we further evaluate the robustness of our model,
particularly in comparison to PCL and iCAP. Additionally, we extend our
evaluation to include a graph-based model, HGN (Fang et al., 2020), to ensure
a thorough assessment of its robustness.

Evaluation on Other Multi-hop Datasets. To verify the generalization, we
evaluate PEI model on 2WikiMultihopQA and MuSiQue. In Table 2, the results
show that our PEI model surpasses all comparison baselines in terms of both EM
and F1 metrics. Notably, despite both PEI and iCAP employ the same encoder-
decoder BART model, PEI achieves a substantial improvement of $4.52/26.66$ in
the answer EM/F1 score compared to iCAP on the 2WikiMultihopQA dataset.
Furthermore, our model exhibits superior performance compared to PCL, yielding
improvements of $1.29/1.14$ and $0.69/0.51$ in the answer EM/F1 score on
2WikiMultihopQA and MuSiQue, respectively.

Models | 2WikiMultihopQA | MuSiQue
---|---|---
EM | F1 | EM | F1
iCAP | 42.80 | 47.90 | - | -
HGN | 38.74 | 68.69 | 39.42 | 65.12
PCL | 46.03 | 73.42 | 41.28 | 67.34
PEI (Ours) | 47.32 | 74.56 | 41.97 | 67.85
Table 2: Results of our proposed PEI compared to PCL, HGN and iCAP on
2WikiMultihopQA and MuSiQue multi-hop QA test set.

Evaluation on Sub-question Dataset. To evaluate the PEI model’s efficacy in
the multi-hop reasoning process, specifically in composing answers from solved
sub-questions, we conducted an evaluation on sub-question QA dataset (Tang et
al., 2021). This dataset consists of 1000 multi-hop questions $q$ along with
their corresponding sub-questions $q_{sub1}$ and $q_{sub2}$. Table 3 shows
that our PEI model achieves a $97.62\%$ success rate in correctly answering
the parent multi-hop question $q$ when both sub-questions $q_{sub1}$ and
$q_{sub2}$ are answered correctly 222The calculation process:
$49.2/(49.2+1.2)=97.62\%$. This highlights the proficiency of PEI in retaining
acquired knowledge through the integration of explicit and implicit knowledge,
surpassing other multi-hop QA models. However, it is noteworthy that PEI also
demonstrates a significant likelihood (36.55%) of correctly answering the
parent multi-hop question even when only one of the sub-questions is answered
accurately 333The calculation process:
$(7.1+22.1)/(49.2+7.1+22.1+1.5)=36.55\%$. To further illustrate the sub-
question dependent success rate of various multi-hop QA models, Figure 3 shows
that these models exhibit a high probability (exceeding $20\%$) of correctly
answering parent multi-hop questions even when only one sub-question is
answered correctly. This indicates that the utilization of potentially unsound
reasoning shortcuts in predicting answers is a prevalent and challenging
phenomenon in multi-hop QA tasks.

$q$ | $q_{sub1}$ | $q_{sub2}$ | DFGN | DecompRC | HGN | PCL | PEI(Ours)
---|---|---|---|---|---|---|---
c | c | c | 23.0 | 31.3 | 39.5 | 43.6 | 49.2
c | c | w | 9.7 | 7.2 | 5.1 | 6.8 | 7.1
c | w | c | 17.9 | 19.1 | 19.6 | 21.3 | 22.1
c | w | w | 7.5 | 5.5 | 3.8 | 2.1 | 1.5
w | c | c | 4.9 | 3.0 | 2.8 | 1.7 | 1.2
w | c | w | 17.0 | 18.6 | 16.7 | 16.3 | 13.4
w | w | c | 3.5 | 3.4 | 2.6 | 1.1 | 1.0
w | w | w | 16.5 | 11.9 | 9.9 | 7.1 | 4.5
Table 3: Categorical EM statistics (%) of sub-question evaluation for five
multi-hop QA models. $c/w$ denotes that the question is answered
correctly/wrongly. $sub1$ denotes the first sub-question and $sub2$ is the
second sub-question of corresponding question $q$. For example, the first four
rows show the percentage of multi-hop questions that can be correctly
answered. Figure 3: The success rate (%) of five multi-hop QA models. $sub1$
denotes the first sub-question and $sub2$ is the second sub-question of
corresponding question $q$.

### 4.5. Ablation Studies

To evaluate the contributions of distinct components within our PEI model, we
conducted a series of ablation studies using the development set of HotpotQA
as the experimental platform.

Model | Ans F1 | Sup F1 | Joint F1
---|---|---|---
ELECTRA | 78.12 | 88.20 | 73.50
\- Type Prompter | 81.14 $\uparrow_{3.02}$ | 89.37 $\uparrow_{1.17}$ | 75.68 $\uparrow_{2.18}$
\- Pre-trained | 78.82 $\uparrow_{0.70}$ | 88.82 $\uparrow_{0.62}$ | 74.54 $\uparrow_{1.04}$
\- Implicit knowledge | 81.22 $\uparrow_{3.10}$ | 90.25 $\uparrow_{2.05}$ | 75.80 $\uparrow_{2.30}$
PEI |  85.68 $\uparrow_{7.56}$ |  92.11 $\uparrow_{3.91}$ |  79.02 $\uparrow_{5.52}$
Table 4: Ablation Study of PEI on the development set of HotpotQA. Ans F1
stands for answer F1; Sup F1 is supporting F1.

Effect of Implicit Knowledge. To verify the hypothesis that implicit knowledge
enhances reasoning for multi-hop QA, we conduct an ablation study between the
performance of the ELECTRA model with and without implicit knowledge. In Table
4, the experimental results show that the ELECTRA model with implicit
knowledge improves $3.10/2.05/2.30$ in Ans F1/Sup F1/Joint F1 compared to the
model without the implicit knowledge. These findings support the notion that
implicit knowledge enhances the reasoning ability of the model thereby proving
the hypothesis underlying our proposed PEI model, inspired by human reading
comprehension.

Effect of Type Prompts. To verify the effects of the type prompts and perform
type-specific reasoning on multihop QA, we conduct a comparative analysis
between the performance of the ELECTRA language model with and without the
type prompter. As illustrated in Table 4, the language model combined with the
type prompter achieves a substantial improvement of $3.02/1.17/2.18$ in
Ans/Sup/Joint F1 compared to the model without the type prompter component.
These findings demonstrate that integrating question type information through
the type prompter module effectively enhances the overall performance of the
model and enables type-specific reasoning for multi-hop QA. Furthermore, these
results validate the alignment of our model design with the cognitive
processes observed in human reasoning. Because type information could be
consider as one form of implicit knowledge.

Effect of Pre-training on Single-hop. Initially, we trained an ELECTRA-based
QA model on the single-hop QA dataset SQuAD (Rajpurkar et al., 2016), and
subsequently retrained it on the HotpotQA dataset. Although conservation
learning (Deng et al., 2022) is not employed in our model, we evaluate the
performance of our model with and without pre-training in order to verify the
effect of the pre-training on single-hop. As shown in Table 4, the language
model combined with the pre-training improves $0.70/0.62/1.04$ in Ans F1/Sup
F1/Joint F1 compared to the model without the pre-training. This indicates
that pre-training in the single-hop QA task enable the model to acquire
valuable information, enhancing its overall performance. However, it is
noteworthy that this improvement is relatively limited without conservation
learning.

Effect of Foundation PLMs. To assess the effects of foundation PLMs, we
compare PEI with PCL and HGN based on the same data and backbone. As shown in
Table 5, PEI consistently outperforms PCL and HGN across all metrics. This
indicates the effectiveness and robustness of PEI across PLMs. Moreover, PEI
with ALBERT achieves an improvement of $0.62/0.23$ in Ans/Sup F1 compared to
PEI with RoBERTa. ALBERT outperforms RoBERTa on GLUE benchmark using a single-
model setup 444https://github.com/google-research/albert. These results
confirm that adopting a more competent foundation PLM can improve the
performance of our model.

Model | Ans F1 | Sup F1 | Joint F1
---|---|---|---
HGN (RoBERTa) | 82.22 | 88.58 | 74.37
HGN (ELECTRA) | 82.24 | 88.63 | 74.51
HGN (ALBERT) | 83.46 | 89.20 | 75.79
PCL (RoBERTa) | 84.33 | 90.75 | 77.12
PCL (ELECTRA) | 84.42 | 91.15 | 77.76
PCL (ALBERT) | 85.47 | 91.28 | 78.76
PEI (RoBERTa) | 85.61 | 92.02 | 78.95
PEI (ELECTRA) | 85.68 | 92.11 | 79.02
PEI (ALBERT) | 86.23 | 92.25 | 79.11

Table 5: Results with different PLMs on the development set of HotpotQA.
RoBERTa, ELECTRA and ALBERT denote that we use RoBERTa-large, ELECTRA-large
and ALBERT-xxlargev2 as the PLM respectively

## 5\. Conclusions and Future Work

In this paper, we introduce a novel framework that mimics human cognitive
reading processes, employing prompts to bridge explicit and implicit
knowledge. Our framework leverages prompts to elicit implicit knowledge from
PLMs within the input context. Additionally, we integrate question type
information to enhance model performance. Experimental results show that PEI
performs comparably to the state-of-the-art on HotpotQA. Furthermore ablation
studies confirm the effectiveness and robustness of our model in emulating
human reading processes. In the future, we aim to extend and apply human
reading cognition theories to diverse reasoning tasks, with the hope of
enabling stronger, complex reasoning capabilities.

## 6\. Limitations

Human reading and cognition theory for reasoning. While our experimental
results demonstrate that our model outperforms all but one baseline model for
multi-hop QA, we have solely validated the efficacy of these cognition
theories within this specific domain. Future research opportunities include
extending these principles to diverse reasoning tasks, such as mathematical
reasoning. Additionally, exploring alternative theories of human cognition and
their potential applications in reasoning tasks would be valuable.

Interpretability of implicit knowledge. Although leveraging implicit knowledge
benefits the model’s reasoning process, the soft prompting we elicit are
challenging to explain. Currently, it is a lack of insights into the specific
knowledge acquired and how it contributes to the model’s reasoning processes
and decision-making capabilities.

Experiments with larger-scale models. While we currently conduct our
experiments with BART and ELECTRA, the availability of large-scale language
models like GPT-3 presents an opportunity to enhance the model’s capabilities
by incorporating more powerful models with richer prior knowledge.
Nevertheless, it remains imperative to strike a balance between computational
cost and model performance.

## 7\. Acknowledgements

This work is supported by the Alan Turning Institute/DSO grant: Improving
multimodality misinformation detection with affective analysis. Yunfei Long,
Guangming Huang and Cunjin Luo acknowledge the financial support of the School
of Computer science and Electrical Engineering, University of Essex.

## 8\. Bibliographical References

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