# QRMeM: Unleash the Length Limitation through Question then Reflection Memory
Mechanism

Bo Wang1 , Heyan Huang1, Yixin Cao 2 , Jiahao Ying2, Wei Tang 4, Chong Feng 1
1Beijing Institute of Technology, China
2Fudan University, China
3Singapore Management University, Singapore
4 University of Science and Technology of China, China
<EMAIL_ADDRESS>
###### Abstract

While large language models (LLMs) have made notable advancements in natural
language processing, they continue to struggle with processing extensive text.
Memory mechanism offers a flexible solution for managing long contexts,
utilizing techniques such as compression, summarization, and structuring to
facilitate nuanced and efficient handling of large volumes of text. However,
existing techniques face challenges with static knowledge integration, leading
to insufficient adaptation to task-specific needs and missing multi-
segmentation relationships, which hinders the dynamic reorganization and
logical combination of relevant segments during the response process. To
address these issues, we introduce a novel strategy, Question then Reflection
Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. This
pool synergizes static textual content with structured graph guidance,
fostering a reflective trial-and-error approach for navigating and identifying
relevant segments. Our evaluation across multiple-choice questions (MCQ) and
multi-document question answering (Multi-doc QA) benchmarks showcases QRMeM’s
enhanced performance compared to existing approaches.111The code used in our
work will be released soon..

QRMeM: Unleash the Length Limitation through Question then Reflection Memory
Mechanism

Bo Wang1 , Heyan Huang1, Yixin Cao 2 , Jiahao Ying2, Wei Tang 4, Chong Feng 1
1Beijing Institute of Technology, China 2Fudan University, China 3Singapore
Management University, Singapore 4 University of Science and Technology of
China, China<EMAIL_ADDRESS>

##  1 Introduction

Large language models (LLMs) have achieved significant advancements in natural
language processing. Nevertheless, they encounter difficulties handling
extensive information, underscoring the necessity for efficient long-context
processing. Expanding the context window size is a crucial way to handle the
problem, including positional embedding and key-value cache Beltagy et al.
(2020); Zaheer et al. (2020); Chen et al. (2023b). However, the inclusion of
more information inevitably results in the accumulation of irrelevant document
content.

Figure 1: Question then Reflection Memory mechanism. This memory is composed
of structured memory and long-term memory, which collaborate during
navigation. During iterative navigation, the structured graph guides the
selection of relevant segments. QRMeM allows for the exploration of segment
combinations based on experience throughout the iterative navigation.

Retrieval-based methods aim to filter relevant parts from the original long
documents Zhang et al. (2023); Yu et al. (2023c). They achieve this through
various retrieval operations, such as dense, sparse, and reranking, between
the text and the query. Another line of approaches, known as memory mechanism
methods Liang et al. (2023), takes the source as a memory pool. These methods
offer a solution for managing long contexts and provide an opportunity to
handle extraneous documents effectively. Existing memory mechanisms employ
several strategies for indexing extensive information. The strategies include
storing most recentest acquired information Liang et al. (2023); Ebbinghaus
(2013), selecting contents based on their relevance, importance, and topics
Zhong et al. (2024); Park et al. (2023), and handling long contexts through
techniques such as compression Pan et al. (2024), summarization Chen et al.
(2023a), and structuring Hu et al. (2023a).

Despite the existing strategies, several critical issues still persist due to
limitations in the accuracy of retrieval modules or other memory construct
tools. First, the memory mechanism is built primarily on human prior knowledge
or predefined structure, which may not always align perfectly with the nuanced
needs of real-world applications. This results in the gap between the
practical scenario queries and the predefined indexing that occurs in the
memory mechanism, making it challenging to find relevant information.
Additionally, tools or LLMs could be employed to manage extensive information
in the memory through processes such as compression, structuring, or other
methods, which can lead to error propagation or information loss.
Consequently, this can result in biased answers based on memory. Moreover,
pinpointing the exact relevant information within these long contexts is
inherently challenging. This difficulty is exacerbated by the needs for multi-
hop reasoning, long-distance dependencies, and distinguishing between similar
segments, which requires tracing information across multiple steps or layers
of data, making it difficult to directly locate the segments needed for
answering questions.

Socratic inquiry involves asking questions to stimulate critical thinking and
illuminate ideas. Inspired by this, we propose a Question then Reflection
based Memory Mechanism, which guides thinking through questioning and directs
the next steps through reflection on the current state, shown in Figure 1.
This mechanism enables the utilization of structured knowledge as guidance to
mine the supporting segment. To mitigate the gap between practical scenario
queries and predefined indexing, the query-oriented structured memory is
constructed by dynamically aligning with the query’s needs. The structured
memory is formed as a graph with entities and open relations as nodes and
edges, which could be a guide to mine the supporting segment. To address
information bias during memory construction, we additionally include the
original segments as a long-term static memory. These segments are associated
with the entity to leverage structured memory for further utilization. Thus,
only the filtered segments are used to ensure information fidelity when
answering the final question. As for addressing the challenge of pinpointing
dispersed relevant segments, we propose three strategies, Entity Trial
achieves comparable results by navigating through only the information of
entities and segments in the memory. Graph Expansion Search uses the
relationships between entity nodes as guidance, thereby alleviating complex
issues during the retrieval process. QRMeM reflects on error information from
past experiences during multiple iterations, which makes the subsequent steps
more effective in finding the relevant segmentation. To summarize, our main
contributions are as follows:

  * •

A query-oriented method is proposed to bridge the gap between predefined
memory and queries while a dual-structure memory pool is built to mitigate the
influence of reorganizing memory.

  * •

Three distinct methodologies are explored utilizing the memory pool, including
QRMeM, Entity Trial, and Graph Expansion Search. These methodologies adopt
different strategies in the interaction process with memory.

  * •

Experimental results on multiple-choice questions (MCQ) and multi-document
question answering (Multi-doc QA) show that our model achieves superior
results with the various methods, validating the effectiveness of our
approach.

##  2 Methodology

Figure 2: Dual-structure Memory Construction pipeline in QRMeM, where $e$ is
the entity inside each graph. \scriptsize{1}⃝ For each segment, the sub-graph
$G_{k}$ is initialized according to the question $Q$ to be answered, then
updated by the further generated questions ${\mathbb{Q}}$ to the current
graph. \scriptsize{2}⃝ Sub-graphs are combined into global graph $G$ through
entity disambiguation and relation fusion. All the segmentation
${\mathbf{S}}_{k}$ is linked with the entity that appeared to construct the
dual-structured memory pool.

The components of QRMeM are divided into Dual-structure Memory Construction
and Reflection-based Relevant Segments Navigation.

Figure 3: Strategies for Knowledge-directed Navigation in QRMeM. Navigation
starts with a subgraph containing only the target entity from the question and
its linked segments. The subgraph is then expanded based on the relevance of
relationship edges connected to the current entity. The process will continue
until the linked segments are sufficient to answer the question.

###  2.1 Dual-structure Memory Construction

The approach involves partitioning the memory component into two distinct
sections, structured and long-term static memory. The construction is depicted
in Figure 2. The structured aspects of the memory resemble human memory
anchors, assisting in finding more relevant chunks related to the query as
one’s understanding deepens. The long-term portion serves as a reference
preventing information loss or misinterpretation.

Query-oriented Graph Initialization: The document $D$ is divided into K
segmentation {${\mathbf{S}}_{1}$, ${\mathbf{S}}_{2}$, …,
${\mathbf{S}}_{{\textnormal{K}}}$}, which is used as the static memory to keep
the original information. And the structured memory is constructed by the
graph $G_{k}=\\{E_{k},R_{k}\\}$, where ${\mathbf{E}}_{k}$ is the set of nodes
and ${\mathbf{R}}_{k}$ is the set of edges. The entities in the original
document, which serve as nodes in the graph, are crucial for identifying the
relevant segmentation. We first prompt LLM to extract the key entities
according to question $Q$, then combine with the schema-based
tool222https://stanfordnlp.github.io/CoreNLP to get the topic entities
${\mathbf{E}}_{k}=\\{e^{{}^{\prime}}_{k,1},e^{{}^{\prime}}_{k,2},...,e^{{}^{\prime}}_{k,N_{k}}\\}$
in ${\mathbf{S}}_{k}$, where $N_{k}$ is the number of entities in
${\mathbf{S}}_{k}$.

The requirement for a fixed schema in Relation Extraction (RE) significantly
constrains domain specificity, making it challenging to adapt to complex
scenarios. Consequently, we adopt an approach akin to Open Information
Extraction Jia et al. (2022) for relation extraction. Instead of relying on
segments from the original document, descriptive relation representations
{${r}_{k,(i,j)}\in{\mathbf{R}}_{k}$} between ${e}_{k,i}$ and ${e}_{k,j}$ are
generated through LLM. Thus, the edges in the graph $G_{k}$ are obtained.

Doubt serves as a fundamental catalyst for the acquisition of knowledge. We
advance the methodology of knowledge graph updates through a mechanism
predicated on question generation. This mechanism is formalized as
${\mathbb{Q}}_{k}=\text{LLM}(T,{\mathbf{S}}_{k},{\mathbf{E}}_{k},{\mathbf{R}}_{k})$,
where ${\mathbb{Q}}_{k}$ denotes the generated question leveraging a Large
Language Model (LLM) and the $T$ represents the summary of the entire
document. To ensure the diversity of the generated questions, a novel question
is incorporated into the question pool only if its ROUGE-L similarity score
with any existing question is less than 0.6.333Excessive number of questions
could introduce further noise, we empirically selected 0.6 as threshold.
Entities and relations are further supplemented through similar techniques
based on the questions ${\mathbb{Q}}_{k}$ during the initial stage.

Global Graph Combination: In the process of integrating sub-graphs
$\\{G_{1},G_{2},...,G_{{\textnormal{K}}}\\}$ into a comprehensive global graph
$G$, we further apply the entity disambiguation and relation merging. After
entity disambiguation, entities associated with distinct segments are further
elaborated into triples of the form
${e}_{k,j}=\\{{e}^{{}^{\prime}}_{k,j},{\mathbb{M}}_{k,j},{\mathbb{N}}\\}$,
thereby facilitating the update of paragraph indices. Here,
${\mathbb{M}}_{k,j}$ and ${\mathbb{N}}$ represent the sets of entity mentions
and segment indices, respectively. after merging entity pairs
($e_{n,i},e_{k,i^{\prime}}$) and ($e_{n,j},e_{k,j^{\prime}}$) into
consolidated entities $e_{(n,k),i}$ and $e_{(n,k),j}$, Following the initial
setup phase, we generate questions for merging relationships $Q_{s}$ according
to the entities and their original segments, which could benefit from
rethinking the relation between entity pairs. Consequently, relations
$r_{n,(i,j)}$ and $r_{k,(i^{\prime},j^{\prime})}$ are amalgamated into a
unified relation
$r_{(k,n),(i,j)}=\text{LLM}(r_{n,(i,j)},{\mathbf{S}}_{n},r_{k,(i^{\prime},j^{\prime})},{\mathbf{S}}_{k},Q_{s},Q,T)$.
Static segments will be associated with the entity nodes, thereby organizing
the connection between the structured and long-term parts in the memory pool,
as shown in Figure 2.

###  2.2 Reflection-based Relevant Segments Navigation

To accurately determine the segments relevant to question $Q$, three segment-
searching algorithms are proposed to explore the impact of different focuses.
The approaches begins by extracting the initial set of entities
$\mathbb{E}_{\text{s}}$ and the associated segments $\mathbb{R}_{\text{s}}$
from the query. Subsequently, segments corresponding to the initial entity set
$\mathbb{E}_{\text{s}}$ are extracted and designed as important segments
${\mathbb{S}}_{\text{imp}}$.

Entity Trial. To enable multiple explorations in complex scenarios and allow
the model to find more relevant segments based on previous iterations, we
employ a straightforward Entity Trial approach. This method iteratively
updates the entity set without the guidance of relations to enhance the
relevance of text segments, using a bipartite graph structure composed solely
of entities and segment nodes in the memory pool. After obtaining the initial
answer from the model, we utilize its reasoning capability to verify whether
the current entity set $\mathbb{E}$ can sufficiently answer the question. If
the model cannot answer the question, it iterates by updating
$\mathbb{E}\leftarrow LLM(\mathbb{E},R,{\mathbb{S}}_{\text{imp}},Q)$. This
process continues until the model can answer the question or reaches the
current window length limit.

Graph Expansion Search. As the number of nodes increases, the model’s ability
to filter nodes and the window length both fall short of meeting the need of
Entity Trial. Graph Expansion Search further leverages the structured part of
the memory pool, $G$, to enhance exploration from segments. $\mathbb{E}_{s}$
is first expanded by incorporating adjacent nodes within the graph. The
expansion is guided by the computation of similarity scores between the given
query $Q$ and the $\mathbb{R}_{s}$, retaining nodes $\mathbb{E}^{\prime}$ with
high similarity. The entity set $\mathbb{E}$ is updated through
$\mathbb{E}\leftarrow\mathbb{E}^{\prime}\cup\mathbb{E}$ following each
expansion iteration. The expansion process continues until either a predefined
threshold of expansion iterations is reached or no new edges with sufficiently
high similarity scores are found. The resultant expanded set of entities
$\mathbb{E}$ is then to generate the elaborated query through
${\mathbb{Q}}=LLM(\mathbb{E},\mathbb{R},Q)$. ${\mathbb{Q}}$ is utilized to
retrieve relevant segments, which are constructed into $c$ to subsequently
address $Q$.

QRMeM. To trace the experience across multiple iterations, QRMeM utilizes a
reflection-based navigation strategy that dynamically traverses the graph $G$,
which is shown in Figure 3 Initially, QRMeM sets up an additional segmentation
set, ${\mathbb{S}}_{\text{add}}$, as $\emptyset$ for further navigation. For
each iteration, the real relevant segments ${\mathbb{S}}_{\text{mix}}$ is
formed by combination of ${\mathbb{S}}_{\text{imp}}$ and
${\mathbb{S}}_{\text{add}}$. Then the LLM is employed to evaluate whether
${\mathbb{S}}_{\text{mix}}$ can answer the question $Q$ by the feedback of the
question $Q$ and the reason $R$ for the failure is identified if not. While
the ${\mathbb{S}}_{\text{mix}}$ is insufficient for the current query, the
first-order adjacent nodes $\mathbb{E}_{\text{adj}}$ of the current entities
and all connecting edges $\mathbb{R}$ are collected. Drawing upon the lessons
learned from the previous iteration, the query, reason, and current entities
$[Q;R;\mathbb{E}_{\text{s}}]$ are used to select the next expanded node.
ContrieverIzacard et al. (2022) is used for calculating the similarity score
between the $[Q;R;\mathbb{E}_{\text{s}}]$ and each edge in $\mathbb{R}$. After
the most relevant adjust node $e$ is selected and merged with the new entity
$e$ into the entity set $\mathbb{E}$, the segments corresponding to the new
entity $e$ are collected. All segments not present in
${\mathbb{S}}_{\text{mix}}$ are added to ${\mathbb{S}}_{\text{add}}$ with the
similarity score. Allowing for thorough updates to
${\mathbb{S}}_{\text{add}}$, a crucial step is to ensure that the context
window remains sufficient after updates. ${\mathbb{S}}_{\text{add}}$ is
processed and filtered according to the similarity score while adhering to the
window constraints. This iterative segment combination, window checking,
question evaluation, and sub-graph expansion process continues until the
question $Q$ is satisfactorily answered. The detail of the algorithm is shown
in Algorithm 1.

##  3 Experiment

###  3.1 Baselines

To evaluate the effectiveness of the proposed method, we compare the QRMeM
with several methods. Retrieval Methods. We use dense retrievers Contriever
Izacard et al. (2022) and BM25 for all datasets. Vanilla, we use the model
with 4096 context windows, including Llama-70b Touvron et al. (2023) and
gpt-3.5-turbo. Summary, we summarize the long context to fit the context
window size. DECOMFu et al. (2021) simplifies complex questions into sub-
questions, answers them sequentially. LLMLingua Pan et al. (2024), compresses
prompts to speed up inference and enhance response efficiency in large
language models. Long context model, including Vicuna-7b-32k, Vicuna-13b-16k,
Longchat-7b-32k, Longchat-13b-16k,Llama-70b-32k. MemWalker Chen et al.
(2023a), which utilize a tree of summary nodes for long context understanding.

###  3.2 Experiment Setting

The official LLaMa-2-Chat and gpt-3.5-turbo are used for the majority of our
experiments. The two models both have a maximum 4,096 token context length. We
utilize the model in a zero-shot prompting fashion without further fine-tuning
or using few-shot examples for our tasks. For both memory tree construction
and generating action and reasoning for navigation, we use top-p sampling. The
temperature is initially set to a default value, but if the output cannot be
parsed, we increase the temperature and retry up to four times. The segment
size is set to 600 for all datasets.

| Qualtiy | Hotpot | 2Wiki | Musique
---|---|---|---|---
Method | Acc | EM | F1 | EM | F1 | EM | F1
Long Context Model
Vicuna-7b-16k | 0.4175 | 0.2750 | 0.2316 | 0.2750 | 0.1779 | 0.0700 | 0.0928
Vicuna-13b-16k | 0.5191 | 0.2800 | 0.3074 | 0.3400 | 0.2674 | 0.1150 | 0.1315
Longchat-7b-32k | 0.2938 | 0.3750 | 0.2932 | 0.3750 | 0.2012 | 0.1400 | 0.1256
Longchat-13b-16k | 0.3250 | 0.3000 | 0.2465 | 0.2850 | 0.1945 | 0.1100 | 0.1335
Llama-2-70b-32k | 0.5765 | 0.4000 | 0.3372 | 0.4200 | 0.3725 | 0.1750 | 0.1605
GPT-3.5-16K | 0.7162 | 0.3900 | 0.4415 | 0.4050 | 0.4278 | 0.1550 | 0.1888
Llama-70B
BM25 (top 3) | 0.6055 | 0.4800 | 0.4418 | 0.4000 | 0.3285 | 0.1750 | 0.1697
Contriever (top 3) | 0.6098 | 0.3900 | 0.3552 | 0.4500 | 0.3632 | 0.1300 | 0.1640
Vanilla (keep left) | 0.6131 | 0.3750 | 0.2966 | 0.4050 | 0.2393 | 0.1000 | 0.0898
Vanilla (keep right) | 0.6251 | 0.2950 | 0.2655 | 0.3400 | 0.2185 | 0.1200 | 0.1202
MemWalker† | 0.6302 | 0.4350 | 0.2046 | 0.3850 | 0.3462 | 0.1600 | 0.1628
LLMLingua | 0.4636 | 0.2750 | 0.2662 | 0.2750 | 0.2439 | 0.0600 | 0.0334
DECOM | 0.4515 | 0.4250 | 0.3938 | 0.3350 | 0.3136 | 0.1300 | 0.1277
QRMeM (TE) | 0.6351 | - | - | - | - | - | -
QRMeM (GES) | 0.6441 | 0.4550 | 0.4032 | 0.4650 | 0.3044 | 0.1800 | 0.1933
QRMeM (KN) | 0.6531 | 0.4650 | 0.4211 | 0.4700 | 0.3314 | 0.2000 | 0.1996
GPT-3.5-turbo
BM25 (top 3) | 0.6359 | 0.3850 | 0.4511 | 0.3200 | 0.3471 | 0.0850 | 0.1476
Contriever (top 3) | 0.6275 | 0.3650 | 0.3950 | 0.4100 | 0.4523 | 0.1350 | 0.1948
Vanilla (keep left) | 0.6448 | 0.3650 | 0.4229 | 0.3350 | 0.3615 | 0.0650 | 0.1020
Vanilla (keep right) | 0.6687 | 0.2550 | 0.3098 | 0.2550 | 0.2691 | 0.0750 | 0.1213
MemWalker† | 0.6622 | 0.3350 | 0.4113 | 0.4050 | 0.4432 | 0.0950 | 0.1323
LLMLingua | 0.5360 | 0.3200 | 0.3393 | 0.2600 | 0.2817 | 0.1000 | 0.0811
DECOM | 0.5495 | 0.3850 | 0.3933 | 0.3750 | 0.3939 | 0.1000 | 0.1086
QRMeM (TE) | 0.6936 | - | - | - | - | - | -
QRMeM (GES) | 0.6801 | 0.4150 | 0.4731 | 0.4250 | 0.4432 | 0.1850 | 0.1931
QRMeM(KN) | 0.7207 | 0.4200 | 0.4632 | 0.4250 | 0.4698 | 0.1900 | 0.2094
Table 1: Mian results on Quality and Multi-doc QA datasets, †denotes that the
results are reproduced based on the original paper. TE denotes the Entity
Trial, GES denotes the Graph Expansion Search, and KN denotes the QRMeM. We
bold the highest scores and underline the second-highest scores for each
block.

###  3.3 Dataset & Metrics

The evaluation datasets predominantly comprise sections on multiple-choice
questions (MCQ) and multi-hop question-answering (Multi-Doc QA). QuALITY Pang
et al. (2022) is employed for the MCQ component, which comprises extended
narratives derived from Project Gutenberg, coupled with questions annotated by
expert human annotators. For our experimental analysis, we selected a subset
consisting of 222 examples, and accuracy is used for evaluation. To evaluate
the capability of short-context models in comprehending extended texts, we
opted against using the original multi-hop QA dataset. Instead, we employed
Multi-Doc QA derived from LongBench Bai et al. (2023), with an average length
exceeding 8,000 tokens, ensuring suitability for evaluating the understanding
of extensive textual information. The datasets incorporate the Wikipedia
passages encompassed version of HotpotQA Yang et al. (2018), 2WikiMultihopQA
Ho et al. (2020) and MuSiQue Trivedi et al. (2022). Consistent with previous
works Yang et al. (2018); Bai et al. (2023), exact match (EM) and
${\textnormal{F}}1$ is selected for evaluation.

###  3.4 Main Result

We evaluate the proposed model QRMeM under the long context question-answer
setting. Table 1 shows the comparison results of QRMeM with different
navigation methods on Quality and three Multi-doc QA datasets against all
baseline methods. Entity Trial is only tested on Quality due to the excessive
number of entities present on Multi-Doc QA. We can see that:

1) Our study presents significant advancements over retrieval methods on the
Quality dataset, on both the LLaMA 70B and GPT-3.5-turbo models. Detailed
results related to retrieval methods are referred to in Appendix B. Notably,
our approach, when compared to models utilizing longer context windows,
achieves results comparable to those of GPT-3.5-16K, thereby outperforming
other models designed for long-text comprehension. This highlights the
effectiveness of our method in selecting relevant segments, promoting a
comprehensive understanding of complex documents.

2) Furthermore, our methods demonstrated exceptional performance on Multi-Doc
QA datasets. Specifically, on the 2Wiki and Musique datasets, our approach
achieved remarkable results and yields comparable results to existing
retrieval models on the Hotpot dataset. Even though BM25 obtained a
satisfactory result on Hotpot, it still struggles with complex tasks like
Quality and Musique. Across all three datasets, our method outperformed long-
document comprehension models. This indicates our approach’s adaptability to
multi-hop tasks that require the understanding of entity relationships,
capturing these relationships more effectively than long-document models. The
lesser improvement on fewer inference steps Hotpot questions and more
significant advancements on the complex Musique and Quality tasks illustrate
our method’s effectiveness in organizing original paragraphs for complex
tasks. The reason GPT-3.5-turbo outperforms GPT-3.5-turbo might be due to
differences in their document understanding capabilities. GPT-3.5-turbo tends
to focus more on the overall context, while GPT-3.5-turbo pays closer
attention to details. This is also why GPT-3.5-16k shows better performance on
QuALITY datasets.

3) Three strategies proposed in our study outperformed retrieval-enhanced and
direct truncation strategies. Entity Trial’s performance improvement
demonstrates the effectiveness of iterative exploration. By using entities as
anchors and leveraging the reflective iterative exploration of segments, it
shows the impact of reflective iteration in acquiring relevant segments. Graph
Expansion Search, although lacking an iterative navigation process, still
utilizes structured memory to guide the LLM. This indicates that our query-
based structured memory provides better guidance and relevance compared to
predefined indexing. Among all the methods, QRMeM achieves the best
performance. It combines the strengths of both Entity Trial and Graph
Expansion Search, using the graph to guide the model and leveraging past
errors to inform future steps. This approach allows the model to utilize
specific structured knowledge while retaining the ability to iteratively
combine different segments.

###  3.5 Ablation Study

Method | Overall | Easy | Difficult
---|---|---|---
QRMeM | 0.7207 | 0.7000 | 0.7450
-w/o Graph Update | 0.6846 | 0.7166 | 0.6470
-w/o Open Entity | 0.6621 | 0.7083 | 0.6078
-w/o Reflection | 0.6081 | 0.6833 | 0.6764
-w/o Navigation | 0.6351 | 0.6750 | 0.5882

Table 2: Ablation study on Quality dataset through GPT-3.5-turbo. Quality is
annotated with difficult levels, and we report the results on different
levels.

In this section, we study the effectiveness of each proposed module by
removing them from QRMeM on Quality Datasets. Results in Table 2 show that
removing any component decreases performance, which observe their individual
contributions to performance. Graph Update denotes the additional question
generation and graph update during initialization. Open Entity refers to the
entity recognition method through LLM. When these two components are removed,
we observed improved performance on the easy level but a significant decline
in performance on the difficult level. This phenomenon indicates that query-
oriented structured memory is crucial for addressing complex problems.
Reflection involves adding reasoning as a reflection when calculating
similarity with relations. Without leveraging past experiences and only
relying on the query to traverse the graph, there is a significant loss in
performance. This demonstrates that our reflection mechanism provides more
precise guidance. Navigation refers to the navigation stage. Without
Navigation, we simply use the start entity to locate the segments and then
answer the query. This approach loses the iterative navigation process,
resulting in an overall decline in performance. Our technique of reorganizing
paragraphs helps to mitigate this issue and compensates for this deficiency.

###  3.6 Performance over Different Max Trials

Figure 4: Accuracy on Quality datasets with different max trials number.

In this study, we introduce a constraint on the number of updates allowed for
entity set $\mathbb{E}$ during the search process, which is shown as Figure 4.
Once the maximum number of updates is reached, the search is terminated, then
the answer is generated based on the current state of $\mathbb{E}$. Our
results indicate a significant enhancement in performance transitioning from
the first to the second update, underscoring the effectiveness of the
navigation strategy. Upon undergoing a series of iterative trial and error
processes, it has been observed that the performance of the model reaches a
plateau, indicating a deceleration in the rate of improvement. The model could
still gain profit from the navigation. Experimental observations indicate that
trail number could be a trade-off between efficiency and effectiveness.

###  3.7 The impact of irrelevant information

| Head | Middle | Tail
---|---|---|---
Datasets | EM | F1 | EM | F1 | EM | F1
Vicuna-7b-16k
Hotpot | 0.3150 | 0.2283 | 0.2650 | 0.1889 | 0.3250 | 0.2257
2Wiki | 0.3850 | 0.2387 | 0.2700 | 0.1749 | 0.3150 | 0.2043
Musique | 0.0750 | 0.0896 | 0.0500 | 0.0733 | 0.1950 | 0.2060
Vicuna-13b-16k
Hotpot | 0.3750 | 0.3444 | 0.3050 | 0.2643 | 0.3650 | 0.3282
2Wiki | 0.3400 | 0.2584 | 0.3550 | 0.2864 | 0.3900 | 0.3052
Musique | 0.0900 | 0.0783 | 0.0700 | 0.0680 | 0.1000 | 0.0725

Table 3: Result on Multi-Doc QA datasets, the supporting segments are
positioned at the beginning, middle, and tail of the document.

To further investigate the influence of irrelevant documents, we conducted
additional tests on cross-document multi-hop questions. In this section, we
retained the original wiki support text from the Long Bench dataset without
chunking the long text, enabling us to more clearly identify the impact of
irrelevant documents. Following Liu et al. (2023), we investigated the impact
of varying the placement of supporting segments within a document, and the
result is shown as 3 Specifically, we positioned the corresponding support
segments at the beginning, middle, and tail of the overall document. Our
findings are consistent with those reported by Liu et al. (2023), indicating
that the placement of information can significantly affect the model’s
performance on the Multi-Doc QA task. Therefore, in our subsequent
experiments, we placed the multi-hop support text at both the beginning and
end of the document, ensuring that the supporting segments remain in these
positions to avoid any positional impact.

(a) Hotpot (b) Musique

Figure 5: The performance over different distances between the supporting
segments.

Irrelevant documents utilized to increase the distance are retained,
represented as [$S_{1};S_{non};S_{2}$], where $S_{1}$ and $S_{2}$ are the
supporting segments, and $S_{non}$ represent sequences of non-relevant
segments. The outcomes are depicted in Figure 5. The results indicate that
although the model exhibits heightened attention to the positions at the
beginning and end of the text, its performance is still impacted by the
distance between documents. Effectiveness consistently declines as distance
increases on both the Hotpot and Musique datasets.

In summary, tasks of increasing complexity necessitate the aggregation of
relevant documents, where the effects can even surpass the influence of the
locations of the supporting documents. Long context understanding requires
high capability in capturing long-distance dependencies that demand complex
reasoning. The case study in Appendix C illustrates our ability to find
relevant documents in complex situations. QRMeM constructs structured
knowledge for each segment, mitigating the impact of long-distance
dependencies. By utilizing structured knowledge to reorganize the segments,
our approach facilitates the close integration of text with dependency
relationships, which leads to superior effectiveness on Multi-Doc tasks
compared to the long context windows model.

##  4 Related Work

###  4.1 Memory Mechanism

Except for the LLMs with extended context window size(Press et al., 2022; Guo
et al., 2022; Ainslie et al., 2023) and vanilla retrieve augment generation
methodsShi et al. (2023); Zhang et al. (2023); Yu et al. (2023b); Chevalier et
al. (2023), numerous attempts have been made to enhance the memory
capabilities of neural modelsChen et al. (2023a). Memory mechanisms could be
predefined representations, including raw natural languages Zhong et al.
(2024); Hu et al. (2023a), structured information Chen et al. (2023a) such as
tuples, databases, etc., and paramterized representations Yu et al. (2023a);
Tang et al. (2024). To leverage the constructed memory, the method stores all
the information of the agent-environment interaction history based on long-
context strategies, maintains the most recently acquired memoriesLiang et al.
(2023); Packer et al. (2023); Ebbinghaus (2013), and selects memory contents
based on their relevance, importance, and topicsZhong et al. (2024); Park et
al. (2023). However, these memory-based algorithms do not allow the
preparation of specific memory structures for different queries, and further
memory integration is also ignored. We use the Knowledge-directed Navigation
method to reorganize the context adjustably.

###  4.2 KG augmented Language Modeling

Knowledge Graphs (KGs) offer dynamic, structured knowledge representation,
enhancing Large Language Models (LLMs) through explicit structured knowledge,
as demonstrated by early studies such as (Zhang et al., 2019; Peters et al.,
2019; Yamada et al., 2020; Wang et al., 2021b, a; Hu et al., 2023b). Methods
include generating SPARQL query backbones (Li et al., 2023a), sampling entity-
related triples (Baek et al., 2023), decomposing (Li et al., 2023b), and
employing a retriever-reader-verifier(Wang et al., 2023). Other approaches
like (Jiang et al., 2023) enable LLMs to navigate KGs through greedy search,
indicating a trend towards interactive KG exploration and reasoning (Sun et
al., 2023), graph-driven context retrieval (Banerjee et al., 2024), and the
formulation of examinations to assess LLM comprehension (Wan et al., 2024).
Previous methods primarily incorporate graphs into the response in various
ways or introduce external knowledge graphs. Our approach directly utilizes
the original documents to construct graphs and employs the original paragraph
information during the answer generation process. This strategy effectively
circumvents the propagation of errors that may arise during the extraction
process.

##  5 Conclusion

In this study, we propose the Question-then-Reflection Memory mechanism, which
enables the utilization of structured knowledge to guide the mining of
supporting segments. QRMeM aligns with the query’s needs by using structured
memory for guidance, and original segments are included as long-term static
memory to avoid information bias. We introduce Entity Trial, Graph Expansion
Search, and QRMeM as methodologies to navigate through structured memory and
find relevant segments. QRMeM reflects on errors from past experiences during
multiple iterations, allowing the model to learn from its mistakes and improve
performance. Experimental results on MCQ and Multi-doc QA demonstrate that our
model achieves superior results with various methods, validating the
effectiveness of our approach.

##  6 Limitations

In this paper, we propose a Question then Reflection Memory Mechanism method
for iteratively select the relevant segments. The limitations of the proposed
method are as follows: 1) The computational cost of initializing and updating
the knowledge in processing the long context, may restrict its system
applicability in scenarios where there is a need to quickly build additional
knowledge bases. 2) Given the constraints imposed by the size of the model
window, reliance on entity resolution is utilized to bridge distinct
paragraphs during the extraction of pertinent knowledge in constructing the
structure knowledge. The segmentation of text into paragraphs potentially
impacts the efficacy of connections within the graph’s structure.

In future work, we plan to mitigate the method’s computational cost by
developing more efficient pipelines. We aim to limit the updates of the graph
to a very small local range to reduce the cost of updating the graph structure
or explore the incorporation of external knowledge sources to mitigate this
limitation and enhance the model’s performance.

##  7 Ethics Statement

This work was conducted in rigorous compliance with the ACL Ethics Policy. All
datasets and large language models (LLMs) used for evaluation are publicly
available. Furthermore, our work strives to explore a Question and Reflection
Memory Me for long-context understanding. We do not foresee any form of
negative ethical impact induced by our work.

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##  Appendix A Navigation strategy

To better describe our navigation strategy, the detailed algorithm of
navigation is shown as Algorithm 1

Algorithm 1 Reflection-Based Navigation Strategy

0: Query $Q$, Initial Entity Set $\mathbb{E}_{\text{s}}$, Initial Relation Set
$\mathbb{R}_{\text{s}}$, Segment Set
${\mathbb{S}}_{\text{add}}\leftarrow\emptyset$, Context Windows Size $L$

0: Answer to the question $Q$

1: ${\mathbb{S}}_{\text{imp}}\leftarrow$ Extract segments corresponding to
$\mathbb{E}_{\text{s}}$

2:
${\mathbb{S}}_{\text{mix}}\leftarrow{\mathbb{S}}_{\text{imp}}\cup{\mathbb{S}}_{\text{add}}$

3: while not $\text{answered}(Q)$ do

4: if LLM can answer $Q$ with ${\mathbb{S}}_{\text{mix}}$ then

5: return Answer

6: else

7: $R\leftarrow\text{LLM}({\mathbb{S}}_{\text{mix}},Q)$

8: $\mathbb{E}_{\text{adj}}\leftarrow$ First-order adjacent nodes of
$\mathbb{E}_{\text{s}}$

9: $\mathbb{R}\leftarrow$ Connecting edges of $\mathbb{E}_{\text{adj}}$

10: $S\leftarrow\text{Similarity}([Q;R;\mathbb{E}_{\text{s}}],\mathbb{R})$

11: $e\leftarrow\text{Max}_{S}(\mathbb{R})$

12: $\mathbb{E}\leftarrow\mathbb{E}\cup\\{e\\}$

13: ${\mathbb{S}}_{\text{new}}\leftarrow$ Extract segments corresponding to
$e$

14:
${\mathbb{S}}_{\text{add}}\leftarrow{\mathbb{S}}_{\text{add}}\cup({\mathbb{S}}_{\text{new}}\setminus{\mathbb{S}}_{\text{mix}})$

15: end if

16:
${\mathbb{S}}_{\text{mix}}\leftarrow{\mathbb{S}}_{\text{imp}}\cup{\mathbb{S}}_{\text{add}}$

17: if LEN(${\mathbb{S}}_{\text{mix}}$) $>$ $L$ then

18: ${\mathbb{S}}_{\text{add}}\leftarrow$ Filter ${\mathbb{S}}_{\text{add}}$
according to $S$

19:
${\mathbb{S}}_{\text{mix}}\leftarrow{\mathbb{S}}_{\text{imp}}\cup{\mathbb{S}}_{\text{add}}$

20: end if

21: end while

##  Appendix B Retrieval Result

We progressively incorporated the content retrieved into the original document
in the retrieval experiments. "Max" indicates including retrieved content in
the prompt until the length limit is reached. For the multi-doc QA dataset, we
reported Exact Match (EM), and for the quality dataset, we reported Accuracy.
The results are shown in the table 4. Since performance does not improve with
the inclusion of more data, we selected the top 3 with the higher average
values for comparison in our paper.

Table 4: Results about the Retrieval Methods Method | Hotpot | 2wiki | Musique | Quality
---|---|---|---|---
BM25(top 1) | 0.3800 | 0.2450 | 0.1150 | 0.5288
BM25(top 2) | 0.4450 | 0.3600 | 0.1500 | 0.5705
BM25(top 3) | 0.4800 | 0.4000 | 0.1750 | 0.6055
BM25(Max) | 0.4550 | 0.4050 | 0.1750 | 0.6208
Contriever(top 1) | 0.3350 | 0.2800 | 0.1000 | 0.5292
Contriever(top 1) | 0.3850 | 0.3950 | 0.1600 | 0.5959
Contriever(top 3) | 0.3900 | 0.4600 | 0.1300 | 0.6098
Contriever(max) | 0.4300 | 0.4450 | 0.1550 | 0.6385
RKDN(ours) | 0.4650 | 0.4700 | 0.2000 | 0.6531

##  Appendix C Case Study

As illustrated in Table 5, when crucial entities are absent in the query, we
can employ an iterative navigation process to search for and identify the
relevant entities gradually. Although irrelevant segments may be included, the
noise introduced is less than that of retrieval methods. The first two
segments rank lower in retrieval, indicating that for complex reasoning tasks,
it is essential to construct memory based on the query and reorganize the text
to bridge the gap with the query. This validates the effectiveness of QRMeM.

Segments |  Rank |  Contain
---|---|---
Valencia CF had a successful season, finishing in the top four of La Liga and thus qualifying for the UEFA Champions League, thanks to the extension of the competition to include more teams from the top leagues. Valencia also won the Copa del Rey, ending a long trophy drought and marking a successful end to Italian coach Claudio Ranieri’s first spell at the club. |  7 |
José Daniel Valencia (born 3 October 1955) is an Argentine former professional footballer who played as an attacking midfielder. He is perhaps most famous for having been part of the 1978 World Cup winning squad.Valencia started his club career at Gimnasia y Esgrima de Jujuy but was soon transferred to Talleres de Córdoba, the club at which he would play most of his career. At Talleres, Valencia suffered the disappointment of finishing runner-up in Nacional 1977, finishing third in Metropolitano 1980, and losing the semi-finals on four occasions. |  5 |
Higinio Ortúzar Santamaría (10 January 1915 – 8 November 1982) was a Chilean footballer who made his entire career in Spain. The first Chilean in Spanish football, he made his debut for Erandio Club in 1935, and next he played for Barakaldo CF, Athletic Bilbao, Valencia CF, Real Valladolid, and Real Sociedad. He was loaned to Racing de Santander in 1936 for 4,500 pesetas, but he couldn’t play due to the Spanish coup of July. |  1 |
José Raúl Aveiro Lamas (born 18 July 1936) is a Paraguayan former professional footballer who played as a striker. Born in Asunción, Aveiro played for Sportivo Luqueño, Valencia, Valencia Mestalla, Elche, Ontinyent, and Constància. He was also a member of the Paraguay national team between 1957 and 1959. |  2 |
Claudio Javier López (Spanish pronunciation:, born 17 July 1974) is an Argentine former footballer, who played as a forward. Nicknamed Piojo (louse), he is best known for his spells with Valencia in Spain and Lazio in Italy. López also had a notable impact in the Argentina national team, participating in two World Cups. |  3 |
Question: Which retired Argentine footballer who played as a forward was a
main player for Valencia CF?
Golden Answer: Claudio Javier López
Retriever Answer: Mario Alberto Kempes Chiodi
Compressed Answer: Mario Alberto Kempes Chiodi
QRMeM Answer: Claudio Javier López
Table 5: Case studies of solving Mulri-Doc QA task. For the question, we show
the rank of each segment. “" denotes the segment is contained in the final set
of QRMeM, “" denotes the segment is not contained in the final set of QRMeM.

##  Appendix D Prompt in QRMeM

The prompt used in this paper is shown as follows, the blue text denotes the
part to be filled with the different documents.

The prompt used for asking the LLM weather could answer the question Read the
text in triple quotes and answer a question: """ [Concatenated Segments]
""" Question: [Qeustion and possible Choice] If the answer CANNOT be inferred
from the text above, reply with action -1. If the answer CAN be inferred from
the text above, reply with action -2, and provide your reasoning and the final
answer. You are ONLY allowed to reply with action -2 or -1.
####################### Reasoning: … Action: -2/-1, … #######################

The prompt used for Entity Recognition # Task Definition This project aims to
extract key entities from the given text to help answer related questions.
Please follow the following steps when performing this task: 1\. Understand
the content: Before starting entity extraction, carefully read and understand
the text’s content. Pay attention to the story plot, themes, and context. 2\.
Identify key entities: Focus on important entities in the text, including but
not limited to characters, locations, times, organizations, etc. These
entities are usually crucial for answering questions. 3\. Contextual
relevance: When annotating entities, consider their roles and importance in
the text. Note how entities are interconnected with events, plots, or other
entities in the text. 4\. Avoid subjective interpretation: Only annotate based
on textual content and avoid adding personal interpretations or assumptions.
5\. Review and verify: After completing the initial annotation, carefully
review to ensure no omissions or errors have occurred. The background
information is [Document Summary]
# Requirement Extract the key point. Named entities from the document below
are inside the triple quote and only appear in the document. """ [Segment
Context]
"""

The prompt used for Relation Extraction # Task Definition This task aims to
associate the extracted entities according to the text to help answer specific
questions. 1\. Describe the relationship in one clear paragraph; 2\. Avoid
false inferences: extract relationships based only on the clear information in
the text, and avoid false inferences based on assumptions or external
knowledge. 3\. Analyze the relationships between entities: Study the mutual
relationships identified by the research institute. Pay attention to how they
interact and describe the relationships between entities in a concise text.
4\. Record the context of the relationship: When extracting the relationship,
record the specific context of the relationship, which is important for
understanding the whole picture of the relationship. # Main text """ [Segment
Content] """ # Extracted entities """ [Segment Content with marked entities]
""" Extract relations from the document according to the given entities, and
give me the entity pair and the description for each relation.

The prompt used for Summary # Task Definition Summary the context into
shortened text, and retain as much relevant information as possible about the
original characters, person, events, location, etc., in the summary. The
context is """ [Segment Context]
""" # Requirement Summary the segments into a short paragraph.


# Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -
Transformer Ensemble Models Tackling Deception and Persuasion

Sudeep Mangalvedhekar , Kshitij Deshpande11footnotemark: 1 , Yash
Patwardhan11footnotemark: 1 ,
Vedant Deshpande11footnotemark: 1 Equal contribution Ravindra
Murumkar11footnotemark: 1
Pune Institute of Computer Technology, Pune
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation
(ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task
2-A of the shared task which focus on persuasion technique detection and
disinformation detection respectively. Detection of persuasion techniques and
disinformation has become imperative to avoid distortion of authentic
information. The tasks use multigenre snippets of tweets and news articles for
the given binary classification problem. We experiment with several
transformer-based models that were pre-trained on the Arabic language. We
fine-tune these state-of-the-art models on the provided dataset. Ensembling is
employed to enhance the performance of the systems. We achieved a micro
F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task
2-A (7th rank on the leaderboard) respectively.

##  1 Introduction

In today’s digital age, numerous platforms aid people in reaching out to the
world. However, some individuals resort to disinformation and persuasion
techniques to influence people, keeping in mind a certain biased agenda, which
can have negative societal effects. Disinformation Wardle and Derakhshan
(2017) is an intentional effort to disseminate malicious, manipulative, and
misleading information for espionage. The propagation of incorrect information
can be deleterious to an individual, an organization, or a nation. Hence,
disinformation detection has become imperative to catch false reports and
avoid social upheaval. Persuasion is the act of changing someone’s
convictions, views, or conduct through interaction or exchange. Persuasion
techniques can be employed to propagate propaganda Alam et al. (2022) and
influence the behavioral patterns of the targeted audience. Persuasion can be
done via textual mediums such as news articles and tweets. Social media can
act as a key instrument to proliferate persuasive content as well as
disinformation among the masses.

With advancements in science and technology, specifically in the domain of
machine learning, machines are now capable of detecting persuasion techniques
as well as disinformation from the given data. However, the detection
techniques have certain limitations. The tactics used to spread disinformation
constantly evolve, and the sheer volume is immense. Understanding context and
intent is another challenge when it comes to detecting persuasion and
disinformation. Detecting and countering these instruments of influence across
multiple languages and cultural contexts can be daunting.

This paper demonstrates our work on Task 1 - Persuasion Technique Detection
and Task 2 - Disinformation Detection Hasanain et al. (2023). We intend to
examine whether the given multigenre textual snippets contain persuasive
content in Task 1 and classify whether the given tweet Mubarak et al. (2023)
is disinformation or not in Task 2. Our approach highlights the use of various
transformer-based models for binary classification on the given Arabic data.
Ensemble-based techniques have also been employed to yield better results.

Figure 1: System architecture used for both Task 1 and Task 2

##  2 Related Work

In the pre-internet era, traditional media analysis, fact-checking, and
investigative journalism were employed to detect disinformation and persuasion
techniques. With the emergence of the internet, keyword-based approaches and
sentimental analysis techniques found their groove in detecting fake news and
persuading content. An analysis of linguistic features Conroy et al. (2015),
lexical patterns Feng et al. (2012), and rhetorical structures Rubin et al.
(2015) was used for this purpose. Further advancements in text analysis Pérez-
Rosas et al. (2017) proved fruitful for this task.

Although mitigating disinformation and persuasion techniques had been a
difficult undertaking, machine learning techniques Manzoor et al. (2019);
Khanam et al. (2021) showed promise to address this issue. Supervised machine
learning methods Reis et al. (2019) such as Support Vector Machine(SVM),
XGBoost, and Naive Bayes have been used for this purpose. Iyer and Sycara
(2019) stated that the detection of persuasive tactics in a text can be
automated using unsupervised learning. Network analysis methods Shu et al.
(2019) such as centrality measures can be used to identify coordinated
behavior. This technique can help to highlight the propagation of
disinformation or persuasive content over social networks.

Subsequently, deep learning techniques Kumar et al. (2020) also contributed to
fine-tuning the results. The utilization of word embeddings and convolutional
neural networks (CNNs) for recognizing persuasion at an early stage also
fueled the prevention of social engineering attacks Tsinganos et al. (2022).
Multiple state-of-the-art systems such as LSTMs Kumar et al. (2020) are also
used to detect fake news. Further research revealed that transfer learning
approaches like BERT produced more promising results than other cutting-edge
NLP techniques Qasim et al. (2022). Ensembling techniques Ahmad et al. (2020)
were utilized to further enhance the results by integrating various approaches
into a single one. Hybrid architectures like combining BERT with a recurrent
neural network (RNN) Kula et al. (2021) or a combination of parallel CNNs with
BERT Kaliyar et al. (2021) achieved a significant score. Recent developments
suggest that AI approaches such as explainable AI (XAI) Chien et al. (2022)
are being experimented with for the task of disinformation and persuasion
technique detection.

In this paper, we present our approach, which encompasses the utilization of
transformer-based models for classification. Variations of BERT are used to
develop an ensemble-based system for the given classification tasks.

##  3 Data

The dataset provided for Task 1 - Persuasion Technique Detection comprises
multigenre text snippets, which are either tweets or news paragraphs. The
training data has 2427 samples of such snippets, the development data has 259
samples, and the testing data has 503 samples. The training data contains
features such as the id, text, label, and type. Each snippet in the training
dataset is labeled as either ’true’ or ’false’ based on the presence of
persuasion techniques in the given sample. This task falls under the category
of binary classification.

The dataset provided for Task 2 - Disinformation Detection comprises tweets.
The training data has 14147 (14126 non-null) samples of such tweets, the
development data has 2111 samples, and the testing data has 3729 samples. The
training data contains features such as the id, text, and label. Each tweet in
the training dataset is labeled as either ’disinfo’ or ’no-disinfo’ based on
the content in every sample. This task falls under the category of binary
classification.

The provided dataset is preprocessed using regular expressions to remove
irrelevant strings such as "@USER", "LINK" and "RT" to reduce the noise.

##  4 System

This shared task discusses the problems of Disinformation and Persuasion
detection. These problems come under the umbrella of classification problems
for which Transformer-based Models have been widely used and have achieved
impressive performance. Thus, we have utilized several transformer-based
models and ensembling methods in our research as shown in figure 1. The models
are trained for 10 epochs with a learning rate of 1e-5, a batch size of 32,
and the AdamW optimizer. The methodologies have been briefly discussed in the
section below.

###  4.1 BERT

Antoun et al. (2020) discusses how BERT models which are pre-trained on a
large corpus of a specific language like Arabic, perform well on language
understanding tasks. They propose several such models that help provide state-
of-the-art results for the Arabic language and thus have been utilized for our
research.

The pre-training dataset used for the models comprises 70 million sentences
which is about 24GB in size. The data consists of news that spans multiple
topics and thus represents a variety that is useful for numerous downstream
tasks. The Masked Language Modeling and Next Sentence Prediction Tasks have
been used as the pre-training objectives which help the models develop a good
contextual understanding of the input sequence. AraBERT was evaluated on three
NLP tasks namely, Question Answering, Sentiment Analysis, and Named-Entity
Recognition to prove its effectiveness across various tasks and domains.

Various variants of the AraBERT model have been provided with slight tweaks in
their pre-training phases and parameters used. AraBERT v1 or v0.1 are the
original models, while v2 or v0.2 are the newer versions with better
vocabulary and pre-processing. AraBERTv0.2-Twitter-base consists of 136M
parameters, it is pre-trained with 60M multi-dialect tweets besides the
dataset used for the other v0.2 models. AraBERTv2-base is pre-trained on 420M
examples that have a sequence length of 128 and on 207M examples that have a
sequence length of 512.

To pre-train MARBERT Abdul-Mageed et al. (2021), 1B Arabic tweets were
selected at random from a sizable internal dataset of roughly 6B tweets.
Unlike AraBERT, the MARBERT model is trained on Twitter data, which involves
both MSA and diverse dialects. It is trained using 163M parameters. This model
is trained with a batch size of 256 and a maximum sequence length of 128. It
is fine-tuned on several downstream tasks such as social meaning and sentiment
analysis.

###  4.2 ELECTRA

Although, Masked Language Modeling pre-training for BERT-based models has
given impressive results, the "Efficiently Learning an Encoder that Classifies
Token Replacements Accurately" (ELECTRA) approach has yielded better results
whilst being more efficient in terms of model size and compute needed for pre-
training. AraELECTRA is the discriminator model (araelectra-base-
discriminator) and the generator is a BERT model (araelectra-base-generator).

The data used for pre-training consists of mostly news articles and the size
of the dataset is 77GB which consists of 8.8 billion words. The model is pre-
trained for 2 million steps with a batch size of 256.

AraELECTRA is a BERT-based model with 12 encoding layers consisting of 12
attention heads. Its hidden size is 768 and has a maximum input sequence
length of 512. The total parameters in AraELECTRA are 136 million. The
generator Model (araelectra-base-generator) used in the ELECTRA approach for
pre-training is a BERT model of a considerably smaller size with 60 million
total parameters. AraELECTRA is evaluated on three NLP tasks namely, Question
Answering, Sentiment Analysis, and Named-Entity Recognition.

##  5 Ensembling

Ensembling is a technique that combines the results of various models to
generate the eventual intended result of the system. Statistical as well as
non-statistical methods are used for this purpose. Ensembling is useful as it
helps generate results that are better than the results given by the
individual models.

Amongst several methods leveraged for ensembling, we observed that the "hard
voting" ensemble technique proved to be the most efficient and accurate. In
hard voting, the majority vote or the "mode" of all the predictions is
selected as the final prediction. It helps improve the robustness of the
system and minimizes the variance in the results.

##  6 Results

We discuss the results of our experiments for tasks 1-A and 2-A in this
section. Table 2 and Table 4 contain our results for the models and the
ensembled score for the respective tasks. The micro F1 score serves as the
official score metric for both tasks 1-A and 2-A.

Model |  | Micro F1
---
Score
Araelectra-base-discriminator | 0.872
AraBERTv0.2-Twitter-base | 0.842
MARBERTv2 (Post-evaluation) | 0.876
AraBERTv1-base | 0.823
AraBERTv2-base | 0.849
Ensemble - Hard Voting | 0.865
Ensemble - Hard Voting | 0.869
(Post-evaluation) |
Table 1: Results for Task 1-A on Development dataset Model |  | Micro F1
---
Score
Araelectra-base-discriminator | 0.750
AraBERTv0.2-Twitter-base | 0.746
MARBERTv2 (Post-evaluation) | 0.732
AraBERTv1-base | 0.702
AraBERTv2-base | 0.728
Ensemble - Hard Voting | 0.742
Ensemble - Hard Voting | 0.751
(Post-evaluation) |
Table 2: Results for Task 1-A on Test dataset

###  6.1 Task 1-A

Araelectra-base-discriminator performs best with a micro F1 score of 0.872 on
the development dataset and 0.750 on the test dataset as seen in Table 1 and
Table 2 respectively. This performance is indicative of the advantages of
utilizing the ELECTRA pre-training approach, where the Replaced Token
Detection (RTD) is the objective for pre-training. It achieves a marginally
better micro F1 score than the hard voting-based ensembled result of the four
models. Despite this, we use the ensemble-based system as our final approach
because it generates low-variance results and provides stable predictions. Our
system achieved a micro F1 score of 0.742 on the test dataset.

In the post-evaluation phase (after submission of the official scores), out of
the various models we experiment with for the given task, MARBERTv2
outperforms Araelectra-base-discriminator and emerges as the best model with a
micro F1 score of 0.876 on development dataset. This can be attributed to the
large size of the tweet-based training corpus. It boosts the ensemble scores
to the 0.869 on development dataset and the 0.751 on test dataset.

Model |  | Micro F1
---
Score
Araelectra-base-generator | 0.893
AraBERTv0.2-Twitter-base | 0.907
MARBERTv2 (Post-evaluation) | 0.909
AraBERTv1-base | 0.882
AraBERTv2-base | 0.897
Ensemble - Hard Voting | 0.909
Ensemble - Hard Voting | 0.914
(Post-evaluation) |
Table 3: Results for Task 2-A on Development dataset Model |  | Micro F1
---
Score
Araelectra-base-generator | 0.882
AraBERTv0.2-Twitter-base | 0.900
MARBERTv2 (Post-evaluation) | 0.903
AraBERTv1-base | 0.882
AraBERTv2-base | 0.894
Ensemble - Hard Voting | 0.901
Ensemble - Hard Voting | 0.905
(Post-evaluation) |
Table 4: Results for Task 2-A on Test dataset

###  6.2 Task 2-A

AraBERTv0.2-Twitter-base achieves the best results with a micro F1 score of
0.907 on the development dataset and 0.900 on the test dataset as seen in
Table 3 and Table 4 respectively among the four models. This is suggestive of
the benefits of the model being pre-trained on a dataset consisting of tweets.
The hard voting-based ensemble provides the best results as mentioned in Table
4. In addition to achieving the best performance, ensembling also generates
results with greater generalizability and stable predictions and is therefore
chosen as the final approach for the system. Our system achieved a micro F1
score of 0.901 and a macro of F1 score 0.861 on the test dataset.

In the post-evaluation phase (after submission of the official scores), out of
the various models we experiment with for the given task, MARBERTv2
outperforms AraBERTv0.2-Twitter-base and emerges as the best model with a
micro F1 score of 0.909 on development dataset and 0.903 on test dataset. This
can be attributed to the large size of the tweet-based training corpus. It
boosts the ensemble scores to 0.914 on the development dataset and 0.905 on
the test dataset.

##  7 Conclusion

In this paper, we compared the performance of several transformer-based models
on the tasks of Persuasion technique detection and Disinformation detection.
For the final submission, amongst the individual models, it is observed that
the Araelectra-base-discriminator achieved the best performance for Task 1-A.
This model was able to achieve a micro F1 score of 0.742. Likewise,
AraBERTv0.2-Twitter-base achieved the best results for Task 2-A and the final
system yielded a micro F1 score of 0.901. Hard voting-based ensembling is used
for our final systems to improve performance whilst also generating stable
predictions. In the future, with the availability of better computational
resources, we can enhance the system’s performance by training it for longer
and by using larger models. Moreover, we can experiment with other suitable
ensembling techniques to gauge their effectiveness.

##  8 Limitations

Language Models used here are compute-intensive and thus may not always be
suitable for application in real-world and real-time systems that have
constraints on computational resources. The pre-training datasets may have
certain biases in them, even though they might be rich in information. They
may thus not represent the real-world picture accurately.

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