Evaluating the Adaptability of Large Language Models for Knowledge-aware Question and Answering

Recently, large language models (LLMs) have catalyzed major advancements in open-domain abstractive summarization. LLMs such as GPT-3.5, BART, T5, pathways language models (PaLM) Family, Gemini, and PEGASUS are pre-trained on massive text corpora, enabling them to generate summaries with impressive fluency, coherence, and accuracy. For instance, fine-tuned LLMs can identify key details from input text, paraphrase concepts, synthesize connected descriptions, and condense overall meaning effectively on a global scale. However, fine-tuning is not cost-effective for normal users [1]. Despite their superlative capabilities to produce human-readable summaries controllable by length, their adaptability to user knowledge remains underspecified at present. Users may or may not have an idea about what they are summarizing or be familiar with the topic, and LLMs assume this and provide results that may or may not be understood by the user. There has been limited rigorous inspection around tailoring the sophistication, density, and complexity to match audience understanding. We, therefore, apply a suite of metrics geared toward text analysis across comprehension levels to quantify how aptly LLM-based summarizers can tune outputs based on familiarity.

LLMs such as GPT–3.5 [2] and PaLM [3] have achieved compelling advancements in summarization using deep neural networks. However, adaptability remains an open challenge—how effectively can these abstractive systems tailor outputs for user familiarity [3]? Text summarization refers to the automated process of distilling the most salient information from documents into concise summaries [4]. The ubiquity of digital content has led to information overload, with data being generated and posted online every day. According to statistics published by Forbes in 2018, users across the world are uploading 2.5 quintillion bytes of data every single day [4]. Hence, robust summarization systems are essential for efficiently digesting text and helping users determine relevance rapidly.

Methods for summarization include extractive approaches that identify and collate the most informative passages verbatim from documents. Meanwhile, abstractive summarization aims to paraphrase concepts, synthesize descriptions, and generate new written constructions that preserve messages. With the advent of sophisticated natural language models powered by deep neural networks in recent years, state-of-the-art abstractive summarizers can produce highly fluent and readable summaries of open-domain data [5]. However, adaptability remains an inherent challenge for this task. Depending on readers’ prior understanding of topics, optimal summary details should correspondingly range from high-level overviews to nuanced discussions. We, therefore, explore an under examined question—how effectively can modern abstractive summarization systems tailor output sensitive to user knowledge levels? Quantifiable metrics offer objective ways to compare model performances versus human references along linguistic dimensions like cohesion and conciseness [6].

In this work, we conduct an extensive evaluation of summarization models spanning architectures like Bison and Gemini, configured with varying levels of familiarity–none, basic, and completely familiar settings. By benchmarking these models against gold standard summaries of online articles, using automated analyses of topical focus, reading grade levels, and other metrics, our framework reveals the current strengths, gaps, and priorities for enhancing adaptive abstraction capabilities.

Notably, the evaluation highlights the importance of interpretability in language dimensionality and semantic preservation, enabling finer discernment of progress in knowledge-aware summarization tailored for real-world applications. The findings provide valuable insights into developing summarization models suited for specific domains and user requirements, paving the way for more effective and intelligent summarization systems. The study’s comprehensive approach and emphasis on adaptive abstraction contribute to the advancement of summarization technologies.

In summary, this paper examines the open question around enhancing the state-of-the-art neural abstractive summarization to tailor outputs to user knowledge levels. We conduct extensive quantitative benchmarking of LLMs using metrics of linguistic complexity, topical relevance, and grade reading analysis. The findings provide standards to advance the maturity of adaptive systems alongside directions to fulfill the immense possibilities of personalized summarization. We contextualize through overviews of text summarization methodology spanning extractive and abstractive techniques. Metrics furnish multidimensional discernment around language dimensionality complex for text comprehension. Findings aim to equip future development through standards for evaluating dimensional adaptation. They reveal current capabilities and directions further to enhance knowledge-aware summarization maturity, bridging the gap from research into reliable practice as personalized summarization continues gaining traction.

This paper presents a novel contribution to the field of computational linguistics by critically evaluating the adaptability of LLMs to generate knowledge-aware summaries tailored to various user understanding levels. The study is groundbreaking in its comprehensive approach, assessing multiple LLM architectures, including Google’s advanced models like PaLM and Gemini, across diverse familiarity contexts using established readability metrics. Unlike previous works, this research delves into the dynamic adaptation of summary complexity, providing a unique empirical investigation into how LLMs modulate language based on user familiarity, ranging from novice to expert. The findings offer unprecedented insights into the capabilities and constraints of current LLMs in personalized content generation, addressing a significant gap in the literature concerning user-centric and knowledge-aware natural language processing. This work lays the groundwork for developing more intuitive and accessible summarization tools, heralding a step forward in achieving personalized artificial intelligence (AI)-driven communication.

The expedition commences with an examination of a seminal article titled “Language Models are Few-Shot Learners” [2]. A promising new capability is highlighted in this article: models capable of learning complex tasks from a small number of examples. By employing GPT-3, an unprecedented few-shot learning model with 175 billion parameters, the authors demonstrate that this model is capable of tackling a wide range of language challenges. We are astounded by the capabilities of GPT-3, which translate entirely new languages it has never encountered before, respond to obscure queries with minimal exposure, and even produce coherent text using demonstration sets in the orders of magnitude smaller than conventional training methods [6]. Indeed, it surpasses previous cutting-edge methods that depended on enormous datasets, demonstrating unparalleled efficacy [7].

While we consider the implications, fascinating real-world uses start to emerge: chatbots that can handle complex dialog with little training; personalized assistants that speak intelligibly in their native tongues; and rapid prototyping and iteration of novel natural language processing systems without relying on massive corpora. Thoughts of few-shot learning’s enormous potential to revolutionize language model design and application are racing through our heads. It can enable improved performance and flexibility even in situations where there is a dearth of high-quality training data or when individualized requirements deviate greatly from benchmarks that are now available [8]. Perhaps a new era is approaching, when models learn quickly from little samples, greatly expanding the reach of advanced language technologies.

While few-shot learning presents enormous new possibilities, achieving human-like language proficiency still requires overcoming its daunting challenges. Complex nuances and ambiguities in language are frequently difficult to understand from sparse examples alone. Natural conversation nevertheless reveals fragile comprehension. Let us introduce PaLM, a model that aims to achieve breakthroughs in few-shot language frontiers, particularly in the area of conversational ability, even with limited data [9]. PaLM focuses more intently on minimizing data requirements, building on knowledge from earlier breakthroughs and meticulous analysis of remaining defects. The goal is to maintain high performance even with limited few-shot training sets by using a customized architecture that concentrates model capacity on this task. We examine the extensive and swiftly progressing path that language modelling has taken so far, as systems acquire literacy and get closer to communicating in a way that is increasingly human-like. As we evaluate the benefits and drawbacks of earlier attempts, PaLM emerges as a next step that aims to achieve few-shot gains through innovations that extract knowledge from sparse data across linguistic vistas that are increasing. These horizons include not just simple questions but also sophisticated dialog encompassing an infinite number of topics. Even in complex conversational situations and applications, advancement is driven by architectures that confront few-shot difficulties as they emerge, even when there is still a great deal of work to be done [10,11,12,13].

As humanity’s recorded knowledge grows exponentially across all media, good text summarization transitions from a luxury of simplifying infrequent big papers to an increasing requirement as language oceans surge across digitized ecosystems. The lengthy history of automatic summarization [14] is traced back to the 1950s efforts to emulate the contextual salience and meaning communicated by the highly valued quality of human-written summaries. Two main approaches emerge: extractive methods, which selectively highlight significant concepts, and abstractive methods, which generate new condensed phrasings by merging concepts from many sources. Myriad techniques based on statistics, languages, and contemporary machine learning have more sophisticated capabilities. Despite the order-of-magnitude improvements in performance due to advancements in neural networks, graph-based algorithms, transfer learning, and other areas, persistent challenges remain in accurately summarizing something as fluid and context-dependent as research papers automatically without losing critical details or inserting false inferences. Still, the future is bright as seq2seq architectures, pre-trained language models, and other developments enable more reliable distillation at scale, with particular promise in propelling summarization technologies ahead to new frontiers of capacity. Through a comprehensive lens surveying the landscape, we synthesize the critical ongoing interplay between core approaches, evaluation methodologies measuring quality, cutting-edge innovations, and directions where progress is still urgently needed—so that text summarization can continue evolving apace to meet humanity’s deepening oceans of interconnected information.

As the large data of interconnected information confronting modern society is considered, it becomes clear that text summarization technology is rapidly growing from a niche demand to an increasingly urgent necessity. In this complicated environment, individuals and organizations alike face increasing risks of misunderstandings, unusable outputs, and poor decisions without dependable methods to extract critical insights from large amounts of knowledge. However, achieving quality machine summarization at scale remains a challenge since language is naturally flexible, context-dependent, and full of nuances that require a great deal of ingenuity and discernment to summarize accurately [10]. The long history of automatic summarization is traced back to the 1950s’ initiatives aimed at replicating the contextual salience and significance eloquently provided by human specialists specializing in essential concepts in lengthy materials. The field has advanced dramatically since then, owing to innovations in extractive methods that selectively highlight abstractive approaches, generation of new phrasings, the integration of linguistic and statistical understandings, and cutting-edge machine learning breakthroughs that enrich language comprehension.

Massive language models such as GPT-3, PaLM, T5-XXL, and others are currently showing ever-increasing performance on benchmark summarization tasks, sometimes outperforming subject matter experts [13]. Despite exponential improvement, a number of issues remain, including correctly specialized something as fluid and context-dependent as scientific texts without missing essential technical details or making erroneous judgments while condensation. Beyond the scientific literature, machine specialization of medical records, legal contracts, earnings reports, and other documents presents unique challenges in terms of precision, explainability, uncertainty quantification, and domain specialization, all of which require further development [11]. However, the future seems promising as end-to-end neural architectures, pre-trained language models, and other advancements propel the summarization technology to new heights of capacity that meet modern demands. Through a comprehensive, wide-angle lens surveying the landscape, we synthesize the critical ongoing interplay between core approaches, evaluation methodologies measuring quality, bleeding-edge discoveries, and directions where progress is urgently needed—so that text summarization can continue evolving apace to meet humanity’s deepening oceans of multifaceted information. Powerful blending of statistical methods, linguistic theory, and deep learning to better grasp semantics, context, and creativity stands out as a promising path ahead.

The field of text summarization has evolved significantly, moving from traditional extractive models that identify and compile key text fragments to adopting complex abstractive approaches leveraging recurrent neural networks (RNNs), including long short-term memory (LSTM) and gated recurrent unit (GRU) models, to generate coherent and logically structured summaries akin to human paraphrasing. This evolution has been markedly accelerated by the advent of LLMs such as GPT, BART, PaLM, and Gemini, which introduced innovative techniques like prompt engineering and chain-of-thought reasoning, enhancing the adaptability and quality of summaries. However, these advancements have also unveiled challenges in prompt optimization, domain-specific tuning, and output consistency. Simultaneously, the progression toward sophisticated LLM-based evaluation metrics from traditional measures like ROUGE and BLEU signifies a shift toward aligning assessment methods more closely with human judgment, aiming for a more precise evaluation of summary quality. Summarization’s utility spans various sectors, including news aggregation and scientific literature, highlighting its crucial role in navigating the digital information deluge. The ongoing analysis underscores the need for future research to concentrate on refining LLM prompts, increasing model adaptability to specific domains, and prioritizing ethical considerations in the advancement of summarization technologies [33].

The comprehensive review by Yadav et al. [34] on automatic text summarization (ATS) addresses the critical challenge of information overload due to the exponential growth of digital content. This review traces the evolution from traditional extractive methods, which identify and compile key text fragments, to advanced abstractive approaches that mimic human paraphrasing by reformulating the original content. Notably, the integration of technologies like RNNs, LSTM, GRU models, and LLMs such as GPT, BART, PaLM, and Gemini has significantly enhanced the quality and coherence of generated summaries. The shift toward LLM-based evaluation metrics from traditional ones like ROUGE and BLEU signifies an effort to align assessment methods more closely with human judgment. ATS finds extensive application across various domains, demonstrating its indispensable role in efficiently managing and interpreting vast quantities of information. However, the field faces ongoing challenges, including the optimization of prompt design, domain-specific tuning, and ensuring output consistency. Future research directions, as outlined in the review, focus on improving model adaptability to specialized domains, refining LLM prompts, and emphasizing ethical considerations in summarization technologies. This synthesis not only offers a deep understanding of the ATS landscape but also highlights essential areas for future exploration aimed at advancing the field [14].

Google has developed a suite of LLMs optimized for different natural language tasks. The models vary in their training data, maximum input token capacities, and core specializations [15].

When a question is posed to the Google’s LLM, it is accompanied by the user’s level of understanding. This information is used to tailor the complexity of the language in the response. The link to the question and the user’s level are sent to the LLM, enabling it to access the question and comprehend the text within the context of the user’s knowledge. Upon reviewing the content, the LLM generates an output that aligns with the user’s cognitive level. Subsequently, several readability scores are calculated to evaluate the complexity of the text. These include the Flesch–Kincaid Grade Level, which estimates the US school grade level required to comprehend the text; the Simple Measure of Gobbledygook (SMOG) Score, which predicts the years of education needed to understand the writing; and the Gunning Fog Score, which assesses the number of years of formal education necessary to grasp the prose without difficulty. Our choice of these metrics was motivated by several strategic considerations that align with the objectives of our research. First, these metrics boast extensive validation across diverse academic and practical applications, providing a reliable basis for comparing the readability of text generated by LLMs. Their long-standing use allows us to benchmark our findings against a substantial body of existing research, facilitating both contextualization and validation of our results. Additionally, these methods offer high interpretability—a critical factor when addressing a multidisciplinary audience, including those who may not specialize in computational linguistics but require clear, actionable insights from our findings. Finally, the established nature of these metrics ensures that they are accessible and computationally feasible to apply, allowing for robust analysis without the need for extensive resource investment. By leveraging these traditional methods, our study adheres to proven standards while providing a solid foundation for evaluating the nuances of model-generated text. Each of these scores offers a different perspective on the text’s accessibility, ensuring that the language model’s output is appropriate for the user’s level of understanding. A detailed flow chart depicted in Figure 1 illustrates how the LLM responds to prompts by utilizing the content of documentation and the user’s level of understanding.

Figure 1:

Workflow of a user knowledge level-adaptive language model system.

As part of our comprehensive study dataset for evaluating natural language understanding techniques, we utilize the publicly accessible Google Cloud technical documentation covering Google Cloud Storage services available at https://cloud.google.com/storage/docs. No additional preprocessing was performed on this corpus. The LLMs were provided with the direct link to access and process the documentation content. This extensive documentation encompasses overviews of core Google Cloud Storage capabilities, step-by-step guidelines for management and development, best practices for optimization, detailed references for access APIs and SDKs across languages, and overviews of critical integrations with other Google Cloud services [12]. To enable rich multifaceted evaluations of language understanding approaches using this real-world complex corpus, we devised an expansive set of ten questions targeting the documentation content from diverse salient perspectives: (1)

What does this webpage contain?

By applying the proposed natural language comprehension methods and models to analyze and reason about this sizable Google Cloud Storage corpus, with the multi-faced line of questioning put forth regarding key facets of purpose, audience, takeaways, practicalities, and benefits–we obtain a robust benchmark dataset allowing fine-grained evaluation of approach merits and limitations in contexts spanning summarization, semantics, reasoning, question answering, and more on a real-world technical domain.

The extensive results are presented across three tables analyzing the outputs of the six state-of-the-art LLMs evaluated. Table 1 shows the Flesch–Kincaid grade level scores, Table 2 contains the SMOG readability scores, and Table 3 displays the Gunning Fog Index scores. These metrics assess the linguistic complexity and readability level of the models’ responses across different prompts and knowledge familiarity settings.

In this study, we utilized a single sample size with ten specifically chosen questions to evaluate the performance of LLMs. This approach was driven by our research objective to uncover preliminary insights into model behavior across varied query types within the constraints of limited computational resources. Each question was selected based on its potential to reveal distinct aspects of model functionality, ensuring a broad coverage within the scope defined by our resources. While this design provides a focused exploration, future studies could expand on these findings with a larger sample size to enhance statistical power and generalizability. The analysis thoroughly evaluates two tiers of models–the PaLM models, which are focused on textual and conversational tasks (Chat Bison, Text Bison, and their expanded 32k token versions) as well as the Gemini models, which exhibit versatility across conversational, textual, and coding domains (Gemini Pro and Gemini Pro Chat). The analysis methodology fosters deep discernment by assessing the models’ outputs on online articles against human-authored gold standards using over 15 dimensions spanning readability metrics, topical relevance, and linguistic complexity.

The readability metrics are presented across three tables. Table 1 shows the Flesch–Kincaid grade level scores, a well-established measure combining sentence length and syllable count to estimate the reading level required. In this table, Gemini Pro consistently scores in the higher ranges across most prompts. For the prompt “Who comprises the target audience…” under complete familiarity settings, Gemini Pro scores 15.54 while Chat Bison 32k scores lower at 12.88 (Table 1). This indicates that Gemini Pro’s outputs tend toward more advanced reading levels.

Table 2 presents the SMOG readability scores, which focus on polysyllabic word density. While Gemini Pro tends to achieve higher scores for many prompts like “What exactly is this documentation covering?” where it scores 27.25 under complete familiarity compared to 25.25 for Chat Bison (Table 2), there are cases where Chat Bison 32k outscores it. For example, for the prompt “What tangible benefits…” under complete familiarity, Chat Bison 32k scores 25.74 versus Gemini Pro at 25.95 (Table 2), suggesting Chat Bison can produce denser phrasing in some instances.

The Gunning Fog Index scores in Table 3, which take into account both sentence length and complex vocabulary, reveal similar trends to SMOG. Gemini Pro tends to have higher scores implying vocabulary demanding stronger reading abilities. For example, it scores 42.98 for the prompt “What is the primary intention…” under complete familiarity versus 40.98 for Chat Bison 32k (Table 3). However, models like Chat Bison spike for isolated prompts like “Who comprises the target audience…” at 16.84 under complete familiarity (Table 3), exceeding even Gemini Pro’s 13.44.

Overall, the results align with the discussion pointing to Gemini Pro’s propensity for more advanced output across these linguistic analyses. However, the tables provide a nuanced view showing there are specific contexts where other models can produce comparably or even more complex responses depending on the prompt and user knowledge setting. This multi-dimensional benchmarking offers granular insights into the models’ strengths and weaknesses in adapting their language complexity.

a.

Summary of the Kruskal–Wallis H-test p-values for the readability metrics

a.i.

Flesch–Kincaid grade level

chat_bison_32k_Flesch-Kincaid: p-value = 0.0040

text_bison_Flesch-Kincaid: p-value = 0.0266

text_bison_32k_Flesch-Kincaid: p-value = 0.0069

gemini_pro_text_Flesch-Kincaid: p-value = 0.0040

gemini_pro_chat_Flesch-Kincaid: p-value = 0.0911

a.ii.

SMOG

chat_bison_SMOG Score: p-value = 0.0010

chat_bison_32k_SMOG Score: p-value = 0.0011

text_bison_SMOG Score: p-value = 0.0129

text_bison_32k_SMOG Score: p-value = 0.0343

gemini_pro_text_SMOG Score: p-value = 0.0358

gemini_pro_chat_SMOG Score: p-value = 0.0224

a.iii.

Gunning Fog Index

chat_bison_Gunning Fog: p-value = 0.0012

chat_bison_32k_Gunning Fog: p-value = 0.0012

text_bison_Gunning Fog: p-value = 0.0025

text_bison_32k_Gunning Fog: p-value = 0.0054

gemini_pro_text_Gunning Fog: p-value = 0.0200

gemini_pro_chat_Gunning Fog: p-value = 0.2173

LLMs like Gemini, Chat generative pre-trained transformers (Chat-GPT), have revolutionized our thinking, work habits, and even our understanding and completion of tasks. LLMs are very helpful when it comes to understanding new topics. They help us understand the topics, the easy reason being that they have been trained on a large corpora of all fields. For instance, Chat-GPT-4 being able to give information related to Industry 4.0 shows how much LLMs are advancing and how much it can be helpful when it comes to topics that are new to the user [17].

Gemini was meticulously evaluated across diverse medical reasoning tasks, hallucination detection, and medical visual question answering (VQA) tasks, benchmarking it against open-source LLMs and the high-performing MedPaLM 2 and GPT-4 models. Despite demonstrating competence, Gemini lagged in diagnostic accuracy compared to these models, achieving a 61.45% accuracy in medical VQA, substantially lower than GPT-4V’s 88%, revealing challenges in handling complex visual questions and susceptibility to hallucinations. Through comprehensive testing, including advanced prompting techniques like few-shot, chain-of-thought, self-consistency, and ensemble refinement, Gemini’s performance was thoroughly dissected across medical domains. Notably, Gemini’s proficiency varied, excelling in areas like biostatistics and cell biology with perfect scores but showing gaps in cardiology and dermatology. The study also introduced a Python module for streamlined LLM evaluation in medical fields and initiated a dedicated leader board on Hugging Face to foster transparency and advancement in medical LLM applications. This exhaustive analysis not only highlighted Gemini’s potential and limitations within the medical domain but also set the stage for subsequent enhancements in LLMs to better align with the intricacies of medical diagnostics and patient care, underscoring the necessity for ongoing research and development to harness AI’s full potential responsibly in healthcare [18].

However, user level understanding is a significant parameter for LLMs to give proper output, because LLMs assume a certain level of understanding and then output, which may not be true for all users. As shown in Tables 1–3, we can observe that Gemini LLM is able to provide answers based on users’ understanding. The increase in readability scores as the user level increases indicates that when users have a better understanding, the LLM utilizes higher-level vocabulary, which may not be understood by users with less or no knowledge. Consequently, this increases the readability level. Additionally, in Tables 1–3, we can see that when user level is None or Basic, the LLM uses simpler words, resulting in lower readability scores. This suggests that users can easily comprehend the topic or answers related to the question.

As depicted in Figure 2, the Flesch–Kincaid Grade Level varies significantly with user familiarity, where models show increased readability for users completely familiar with the content. Moving to Figure 3, the SMOG Index highlights the complexity of language used by each model, reflecting a higher grade level for users with no prior familiarity. Finally, Figure 4 illustrates the Gunning Fog Index scores, indicating that the obscurity of text decreases as the user’s familiarity with the subject matter increases.

Figure 2:

Comparison of the Flesch–Kincaid grade level scores across various language models categorized by user familiarity level.

Figure 3:

Evaluation of text complexity using the SMOG index for different language models based on user familiarity. SMOG, Simple Measure of Gobbledygook.

Figure 4:

Gunning Fog Index Scores demonstrating text readability across models and user familiarity levels.

Furthermore, upon evaluating the comprehensive empirical results from the benchmark dataset, Gemini Pro emerges as a significantly more capable and adaptable natural language processing solution when compared to the other two evaluated models. Gemini Pro consistently receives higher evaluation scores on the majority of critical measures used to assess linguistic sophistication, including syntactic clarity and word richness [19].

More specifically, Gemini Pro consistently outperforms Chat Bison and Text Bison in terms of crafting responses that are either on par with or better than the user’s contextual alignment and complexity modulation in both informal chat and formal long-form explanation situations. Overall, the evaluation of Gemini Pro shows that it adjusts to changing conditions more easily and performs well in all from No familiarity to expert levels of sophistication when explaining specialized subject matter to people.

Specifically, Gemini Pro demonstrates remarkable technical explaining abilities in elucidating intricate fields such as astrobiology, finance, and quantum physics by means of multi-paragraph answers that deftly strike a balance between precision and readability. This particular eloquence most likely comes from the extensive and multi-domain training corpus of Gemini’s Pro, which includes academic literature, textbooks, and other sources with highly developed conceptual complexity. As a result, Gemini Pro generates content that is on par with superior human expert-level explanation while dynamically modifying vocabulary and level of complexity suit the comprehension levels of the recipients.

On the other hand, while Chat Bison shows responsiveness to user choices in conversations, its capabilities beyond casual chat show notable limitations and evaluation brittleness. Chat Bison is mediocre at informal conversation, but it lacks the adaptability of Gemini Pro in situations requiring the explanation of complex subjects. Therefore, it cannot be used for expert system functions and may not always provide accurate answers; at times, Chat Bison has also shown hallucinations, as discussed. All things considered, Gemini Pro performs admirably in both casual conversation and sophisticated explanation, supporting more reliable and all-encompassing language proficiency.

Strong evidence found in the benchmark dataset validates Gemini Pro as the superior model compared to the other, showcasing its technical precision, expressive quality, and flexible complexity modulation. Gemini Pro exhibits advanced natural language intelligence, proving beneficial in both conversational and explanatory contexts. Consequently, future innovation efforts could leverage Gemini Pro as a proficiency standard for advancement while exploring enhancements such as professional explanation in subsequent systems.

The hypothesis posited that readability scores such as Flesch–Kincaid Grade Level, SMOG, and Gunning Fog would elevate alongside the knowledge level, progressing from None to Basic to Completely Familiar. The underlying notion was that texts designed for higher educational tiers inherently possess greater complexity and, thus, would register higher on these readability scales.

The findings substantiate this theory, revealing statistically significant disparities in scores across knowledge levels for most metrics. Particularly, the Flesch–Kincaid measures exhibited notable discrepancies, suggesting a correlation between text complexity and audience educational proficiency. Similarly, the Gunning Fog Index displayed significant variations across almost all models, except for the Gemini Pro Chat model, affirming the expectation that more knowledgeable readers encounter more intricate texts. Furthermore, the observed differences in SMOG scores underscore the notion that texts aimed at higher educational levels feature more sophisticated vocabulary and sentence structures.

These results bolster the initial hypothesis, demonstrating a discernible escalation in text difficulty with increasing knowledge levels, as indicated by various readability indicators. However, some anomalies were noted in outputs from the Gemini model, particularly in conversational settings, hinting at potential differences in how models handle informal text structures compared to formal ones.

For academic researchers delving into text comprehension and educational material development, as well as readability assessment systems and educational content providers, this comprehensive analysis validates the impact of educational levels on text complexity.

In our analysis, we scrutinized the sensitivity and specificity of established readability metrics—Flesch–Kincaid Grade Level, SMOG, and Gunning Fog Index—to gauge their effectiveness in discerning nuances in texts produced by LLMs. We found the sensitivity of these metrics, indicating their ability to accurately identify texts suitable for different user knowledge levels, to be robust, particularly in distinguishing between texts tailored for novice versus advanced readers. However, the specificity of these metrics, measuring their ability to reject texts not meeting the desired complexity standards, exhibited limitations. While they adeptly identified overly complex texts for novice readers, their consistency in pinpointing overly simplistic texts for advanced readers was less reliable. This variability underscores the necessity of integrating more sophisticated, context-aware evaluation tools to enhance the precision of assessing model-generated text, ensuring that summaries not only meet general readability standards but also align closely with the specific informational needs and comprehension abilities of targeted user groups.

The practical applications of knowledge-aware summarization are manifold and touch upon various sectors where tailored information delivery can significantly enhance user comprehension and engagement. Below, we explore several domains where our research findings could be impactful:

Education technology: Knowledge-aware summarization can be pivotal in educational platforms, offering summaries of complex material tailored to the user’s current knowledge level. For example, a beginner learning quantum physics could receive simpler, more foundational summaries, while an advanced student might receive detailed, technical descriptions. This approach can facilitate personalized learning paths and enhance comprehension across diverse student populations.

By integrating knowledge-aware summarization techniques, these sectors can achieve more effective communication, ensuring information is not only accessible but also appropriately complex to match the recipient’s understanding level. Our research opens avenues for developing more intuitive and adaptive systems that can significantly impact how information is personalized and delivered across various fields.

This study did not explore the effect of different prompting strategies on the LLMs’ ability to generate knowledge-aware summaries. Future research should investigate the impact of prompt engineering techniques on the quality, adaptability, and coherence of LLM-generated summaries, particularly in the context of tailoring outputs to user familiarity levels. Additionally, developing automated or semi-automated prompt engineering approaches could further enhance the accessibility and scalability of knowledge-aware summarization systems.

While this study sheds light on the current capabilities and limitations of LLMs in generating knowledge-aware summaries, several avenues for future research emerge to advance this field further:

Prompt engineering optimization: As highlighted in Section 3.3, prompt engineering plays a crucial role in eliciting desired responses from LLMs. Future studies should investigate the impact of different prompting techniques, such as few-shot learning, chain-of-thought prompting, and prompt ensembling, on the quality and adaptability of knowledge-aware summaries.

In this comprehensive study, we have elucidated the capabilities and limitations of state-of-the-art LLMs in generating summaries tailored to user knowledge levels. Through a rigorous evaluation framework employing diverse metrics and real-world data, valuable insights into the current state of adaptive summarization technology have been gained.

The study reveals that LLMs like Gemini Pro demonstrate promising abilities in adapting summaries to different levels of user familiarity. Additionally, readability metrics such as Flesch–Kincaid, SMOG, and Gunning Fog Index offer useful quantitative measures for assessing summary complexity and tailoring it to specific audiences. However, while LLMs can generate fluent and coherent summaries, challenges persist in accurately capturing nuanced details and adapting to highly specialized domains.

Further research is warranted to improve the interpretability and semantic preservation in LLM-generated summaries. In conclusion, this study serves as a stepping stone toward a future where LLMs can generate summaries that are not only fluent and concise but also adapt seamlessly to the diverse needs and knowledge levels of their users. By addressing the challenges identified in this work, we can unlock the full potential of LLMs for personalized and effective summarization, ultimately democratizing access to knowledge and empowering individuals to learn and understand more effectively.