Beyond Automated Citation: Reclaiming Interpretation with AI

Author: Denis Avetisyan

New research suggests the power of large language models isn’t in replacing human analysis of academic sources, but in augmenting it.

This paper explores ‘scaling in’ with GPT-5, using thick citation context analysis and fragile prompts to support richer, more nuanced textual reconstructions.

Automated citation analysis often prioritizes broad typologies over nuanced interpretive work. This study, ‘Scaling In, Not Up? Testing Thick Citation Context Analysis with GPT-5 and Fragile Prompts’, investigates whether large language models can instead assist researchers in generating richer reconstructions of citation contexts-scaling in through detailed textual analysis rather than up through automated categorization. By employing a two-stage GPT-5 pipeline and systematically varying prompt design across 90 reconstructions, the research demonstrates that while the model consistently identifies a citation’s surface function, prompt scaffolding significantly shapes the range of interpretive hypotheses it generates. Could this approach offer a viable path toward inspectable, contestable, and collaboratively-driven interpretive methods, or does the fragility of these prompts reveal inherent limitations in leveraging LLMs for complex hermeneutic tasks?

The Burden of Interpretation: Scaling Citation Analysis

Citation Context Analysis, a method for understanding how academic papers influence one another, has historically been a painstaking process. Researchers traditionally pore over individual citations, manually interpreting the surrounding text to determine the nature of the scholarly engagement – whether it’s support, critique, or simply background. This labor-intensive approach becomes increasingly untenable given the exponential growth of published research. The sheer volume of academic literature now far exceeds the capacity of manual analysis, creating a significant bottleneck in understanding the true landscape of scholarly influence and hindering efforts to synthesize knowledge across disciplines. Consequently, comprehensive assessments of research impact are often limited to small-scale studies, potentially overlooking critical connections and nuanced arguments within the broader scientific community.

The inherent limitations of manual interpretation pose a significant challenge to fully grasping the scope of scholarly influence. Analyzing citation context – the textual environment surrounding a citation – traditionally relies on human experts to discern the author’s intent, whether the citation is supportive, critical, or merely descriptive. This process, while providing valuable qualitative insights, is profoundly constrained by time and resources; a single researcher can only evaluate a fraction of the relevant literature. Consequently, interpretations are often focused on a narrow selection of cases, potentially overlooking crucial nuances or systematic patterns of influence that emerge only from broader analysis. This restricted scope hinders a comprehensive understanding of how ideas are truly disseminated, debated, and built upon within the academic community, ultimately limiting the accuracy of assessments regarding a scholar’s or a research field’s impact.

Successfully broadening the scope of citation context analysis demands a shift from singular interpretations to methods capable of generating and assessing numerous understandings of a given citation. The sheer volume of scholarly work necessitates automated approaches that can move beyond identifying simply that a citation occurred, to discerning how it was used – as agreement, disagreement, background, or methodological inspiration. This requires computational techniques not merely to process citations, but to simulate the argumentative reasoning of experts, creating a range of plausible interpretations for each instance. Crucially, these systems must also include mechanisms for evaluating the quality and validity of each interpretation, potentially leveraging statistical modeling, machine learning, or even expert feedback to prioritize the most compelling readings of scholarly influence. Only through such a multifaceted approach can citation context analysis truly scale to meet the challenges posed by the ever-expanding landscape of academic literature.

The subtleties of academic discourse often elude current citation context analysis methods. Scholarly arguments are rarely straightforward; they frequently rely on implicit assumptions, rhetorical strategies, and nuanced positioning relative to prior work. Existing techniques, frequently focused on simple co-occurrence or keyword matching, struggle to discern whether a citation signifies agreement, disagreement, extension, or merely acknowledgement. This limitation hinders a complete understanding of scholarly influence, as a citation’s true weight and meaning are lost when contextual complexities are flattened. Consequently, interpretations generated by these approaches may misrepresent the intellectual landscape, overlooking crucial distinctions and potentially leading to inaccurate assessments of research impact and evolving scientific consensus.

Automated Insight: Leveraging Language Models for Structured Interpretation

Large Language Models (LLMs), and specifically the GPT-5 architecture, present a scalable solution for automating Citation Context Analysis (CCA). Traditional CCA relies on manual review of cited sources, a process that is both time-consuming and expensive. GPT-5’s capacity for natural language understanding and generation allows for the automated extraction of relevant contextual information surrounding citations within large text corpora. This capability facilitates the processing of significantly larger datasets than is feasible with manual methods, enabling broader analyses and reducing the cost per citation analyzed. The model’s inherent ability to handle complex linguistic structures and nuanced meaning allows it to move beyond simple keyword matching, providing a more accurate and comprehensive interpretation of citation context.

The system utilizes a two-stage prompting pipeline to facilitate structured interpretation of citations. The initial stage involves surface-level classification, where the LLM categorizes the citation based on readily identifiable features – such as the cited source type, the presence of specific keywords, or the overall sentiment expressed. This classification serves as a preliminary filter and provides context for the second stage: interpretative reconstruction. In this phase, the LLM leverages the initial classification, along with the full text of the citation, to generate a more nuanced and detailed interpretation of the citation’s meaning and relevance, focusing on the relationship between the citing and cited documents.

Effective prompt engineering is fundamental to LLM-driven structured interpretation due to the models’ sensitivity to input phrasing. Specifically, prompts must explicitly define the desired output format – including the types of relationships to extract, the expected entities, and the structure of the resulting hypothesis – to avoid ambiguous or irrelevant responses. Iterative refinement of prompts, incorporating few-shot examples demonstrating the desired behavior, significantly improves the coherence and factual accuracy of generated interpretations. Furthermore, incorporating constraints within the prompt – such as specifying the maximum length of a hypothesis or requiring justification based on the source text – enhances the reliability and usability of the LLM’s output for downstream tasks.

The system generates multiple interpretations per citation by leveraging the LLM’s capacity for diverse hypothesis generation within the defined prompting parameters. Rather than a single, definitive interpretation, the model outputs several distinct, text-supported explanations for the relationship between the citing and cited documents. Each interpretation is directly derived from the content of both texts, ensuring traceability and allowing for evaluation of interpretative confidence and potential biases. This multi-interpretation approach facilitates a more nuanced understanding of the citation context and enables downstream applications requiring a range of possible relationships, such as knowledge graph construction or comprehensive literature reviews.

A Rigorous Test: Footnote 6 as a Benchmark for Interpretation

Footnote 6 from Chubin and Moitra’s 1975 work presents a significant challenge for Large Language Model (LLM) interpretation due to its ambiguous phrasing and reliance on nuanced contextual understanding within the field of science studies. The footnote details a specific instance of citation practice, but lacks explicit justification for the connection made between the cited work and the authors’ argument. This necessitates inferential reasoning from the LLM to formulate a plausible hypothesis regarding the authors’ intent, moving beyond simple information retrieval and demanding a level of interpretative ability typically associated with human scholarly analysis. The complexity arises from the subtle nature of academic argumentation and the potential for multiple valid interpretations of citation choices, making definitive evaluation of the LLM’s response difficult.

The interpretation of Footnote 6 offered by Gilbert in Chubin and Moitra (1975) functions as a crucial comparative standard for assessing the validity of hypotheses generated by the Large Language Model. By directly comparing the LLM’s proposed interpretations against Gilbert’s established reading, researchers can quantitatively and qualitatively evaluate the model’s ability to accurately process and understand historical context. This benchmark allows for a focused analysis of the LLM’s strengths and weaknesses in nuanced interpretative tasks, identifying areas where the model aligns with, or diverges from, existing scholarly consensus. The methodology involves a detailed side-by-side examination of the evidence used to support both Gilbert’s interpretation and the LLM’s generated hypotheses.

Expectation Checks constitute a critical component of the evaluation process, ensuring alignment between the Large Language Model’s (LLM) generated hypotheses and established scholarly consensus. This involves a systematic comparison of the LLM’s predictions regarding Footnote 6 with the original text of Chubin and Moitra (1975) and relevant secondary sources. Specifically, the LLM’s interpretations are assessed for factual accuracy, logical consistency with the cited material, and compatibility with existing interpretations within the field. Discrepancies identified during these checks flag potential errors in the LLM’s reasoning or areas where its interpretative framework diverges from established academic understanding, prompting further investigation and refinement of the evaluation criteria.

Cue Analysis systematically examines specific textual features within Footnote 6 to identify elements that could support interpretations diverging from the established Gilbert interpretation. This involves isolating linguistic markers – such as hedging, modal verbs, and rhetorical questions – as well as identifying shifts in argumentative structure or topic. The goal is not to disprove existing interpretations, but rather to map a broader range of plausible readings inherent in the source text, thereby demonstrating the potential for nuanced understanding beyond a single dominant perspective and enriching the interpretative landscape with alternative hypotheses.

Beyond Singular Truth: Quantifying and Analyzing Interpretative Diversity

The study demonstrates a compelling example of Interpretative Plurality through the extensive hypothesis generation of a Large Language Model. Rather than converging on a single interpretation, the LLM produced a total of 450 distinct hypotheses, illustrating the inherent multiplicity of meaning within scholarly texts. This expansive output suggests that complex communication often allows for, and even benefits from, diverse readings, challenging the notion of a singular, definitive understanding. The sheer volume of generated hypotheses provides a robust foundation for analyzing the range of possible interpretations and identifying patterns in how different prompts elicit varied responses, ultimately revealing the richness and flexibility embedded within academic discourse.

To move beyond simply observing the diversity of interpretations, researchers employed Linear Probability Models to rigorously assess how specific prompt alterations influenced the generation of hypotheses. This statistical approach quantified the average change in the probability of a particular code – representing a specific interpretative theme – occurring in a hypothesis, given a one-unit change in a prompt variable. The analysis revealed statistically significant Average Marginal Effects (AMEs) for several codes, indicating that certain prompt settings demonstrably increased or decreased the likelihood of those themes appearing in the generated interpretations. This wasn’t merely descriptive; it provided concrete, quantifiable evidence of the relationship between prompting strategies and the resulting range of scholarly insights, establishing a foundation for understanding and potentially controlling interpretative diversity within large language models.

A systematic inductive coding process unveiled consistent thematic structures within the LLM-generated hypotheses, demonstrating how varied prompts elicit predictable argumentative patterns. Researchers identified recurring codes representing distinct interpretative approaches, and subsequent analysis of code co-occurrence revealed strong associations between specific prompt settings and the emergence of related themes. This suggests that subtle changes in prompting strategies can reliably steer the LLM towards particular lines of reasoning, highlighting the model’s sensitivity to nuanced input and its capacity to consistently generate interpretations aligned with those cues. The observed patterns provide a quantifiable framework for understanding how LLMs construct arguments, revealing an underlying logic responsive to directed inquiry.

The application of Large Language Models to scholarly texts offers a pathway towards a more nuanced comprehension of academic discourse, moving beyond singular interpretations to embrace a multiplicity of perspectives. This research demonstrates that through a process of hypothesis generation and rigorous statistical analysis – specifically, the identification of statistically significant Average Marginal Effects (AMEs) where confidence intervals do not include zero – LLMs can reveal latent argumentative structures within texts. This isn’t simply pattern recognition; it’s textual grounding, where interpretations are directly derived from the content itself, rather than imposed upon it. The ability to systematically quantify and analyze these diverse interpretations offers a powerful new tool for understanding the complexities of scholarly communication and identifying subtle shifts in academic thought, ultimately providing a richer, more comprehensive view than traditional analytical methods allow.

The study champions a deliberate shift in approach to citation analysis, advocating for ‘scaling in’ rather than ‘scaling up’. This prioritizes depth of interpretative reconstruction over sheer volume-a principle echoing Tim Berners-Lee’s sentiment: “The Web is more a social creation than a technical one.” The research demonstrates how careful prompt engineering, despite the fragility of such prompts, can unlock nuanced understandings of scholarly connections. It isn’t about automating interpretation entirely, but about augmenting human researchers’ capacity to explore a richer landscape of contextual meaning, a landscape where the quality of insight, not quantity, reigns supreme.

What Lies Ahead?

The pursuit of automated citation analysis continues to resemble a quest for a perfect map – a reduction of lived intellectual history into neatly delineated territories. This work suggests a different tack: not to build a more complete map, but to provide better tools for the cartographer. The limitations of Large Language Models are not bugs to be fixed, but fundamental constraints that, paradoxically, may force a return to interpretive rigor. The ‘fragile prompts’ employed here were not failures of engineering, but rather stark reminders that meaning resides not in the algorithm, but in the sustained, critical engagement of a human intellect.

Future work must resist the siren song of ‘scale.’ The temptation to simply amass more data, or build larger models, obscures the more pressing need for methodological transparency and a clearer articulation of interpretive principles. The true challenge lies in developing techniques that do not merely generate reconstructions of citation context, but allow researchers to evaluate their plausibility and assess their significance. Intuition, after all, remains the best compiler.

Perhaps the most fruitful avenue for exploration involves a systematic investigation of the biases inherent in both the models themselves and the prompts used to elicit responses. Code should be as self-evident as gravity, and any attempt to automate interpretive work demands an equally relentless scrutiny of its underlying assumptions. The goal is not to replace the scholar, but to augment their capacity for nuanced, critical thinking-a task that demands clarity, not complexity.

Original article: https://arxiv.org/pdf/2602.22359.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Burden of Interpretation: Scaling Citation Analysis

Automated Insight: Leveraging Language Models for Structured Interpretation

A Rigorous Test: Footnote 6 as a Benchmark for Interpretation

Beyond Singular Truth: Quantifying and Analyzing Interpretative Diversity

What Lies Ahead?

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