Decoding Domain Speak: Enhancing Language Models with Specialized Terminology

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Author: Denis Avetisyan

A new framework, TermGPT, aims to bridge the gap between general language understanding and the nuanced vocabulary of fields like law and finance.

TermGPT constructs a sentence graph—nodes representing sentences connected by edges denoting semantic and lexical ambiguities—and leverages this structure for data augmentation, generating question-candidate-answer pairs to refine terminology embeddings through contrastive learning at multiple levels, thereby cultivating a system where nuanced distinctions in meaning emerge not from design, but from the relationships within the evolving graph.
TermGPT constructs a sentence graph—nodes representing sentences connected by edges denoting semantic and lexical ambiguities—and leverages this structure for data augmentation, generating question-candidate-answer pairs to refine terminology embeddings through contrastive learning at multiple levels, thereby cultivating a system where nuanced distinctions in meaning emerge not from design, but from the relationships within the evolving graph.

TermGPT leverages multi-level contrastive learning to improve terminology adaptation in large language models for high-stakes domains.

While large language models excel at text generation, their inherent limitations in discerning nuanced domain-specific terminology pose challenges for high-stakes applications. This paper introduces TermGPT: Multi-Level Contrastive Fine-Tuning for Terminology Adaptation in Legal and Financial Domain, a novel framework designed to address this issue through multi-level contrastive learning and sentence graph construction. Our approach enhances LLMs’ understanding of subtle semantic distinctions critical in legal and financial contexts, yielding improved performance in term discrimination tasks. Will this methodology pave the way for more reliable and accurate AI-driven solutions in these complex, regulated industries?

The Erosion of Meaning in Specialized Domains

Large language models (LLMs) demonstrate broad language proficiency, yet frequently falter when processing specialized terminology, resulting in inaccuracies and a loss of nuance. Current fine-tuning methods, while adaptable, often prove inefficient for focused domains, requiring substantial labeled data and computational resources. A core challenge lies in preserving semantic precision—LLMs must learn new vocabulary without distorting existing conceptual relationships.

Comparative analysis reveals variations in LLM Score performance across different models and datasets.
Comparative analysis reveals variations in LLM Score performance across different models and datasets.

Monitoring is the art of fearing consciously.

TermGPT: A System Built on Controlled Decay

TermGPT addresses limitations in terminology extraction via a multi-level contrastive learning approach. This framework enhances understanding of specialized language by balancing global sentence context with fine-grained token representations, moving beyond simple word embeddings. The architecture discerns terminology even with varied phrasing through sentence-level and token-level contrastive learning.

To improve robustness and reduce data requirements, TermGPT incorporates data augmentation powered by the Sentence Graph, generating diverse training pairs by identifying semantically related sentences and terms. This strategy improves generalization and enables effective learning from limited data.

Implementation: The Illusion of Efficiency

TermGPT utilizes large-scale generative language models – Qwen3-8B-Instruct and LLaMA3-8B-Instruct – as foundational encoders, prioritizing models demonstrating strong instruction following and general language understanding. Training efficiency was prioritized with Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning and DeepSpeed-ZeRO2 for optimized memory usage. The AdamW optimizer was selected for its robust convergence properties.

Supervised Fine-Tuning (SFT) aligned model outputs with desired response characteristics, training the model on a curated dataset of technical terms and explanations, reinforcing accurate and informative content.

Evaluation: Measuring the Inevitable Drift

TermGPT was evaluated on the JecQA dataset (legal question answering) and a specialized Financial Regulations Dataset, demonstrating substantial performance gains compared to baseline LLMs in both domains. Quantitative analysis reveals an average improvement of 6.14% in terminology Question-Answering (QA) tasks and 2.60% in terminology Question-Choice Answering (QCA) tasks. Utilizing the Qwen3 backbone, TermGPT achieved a 15.98% performance gain on QCA and a 43.52% improvement on QA.

Performance metrics demonstrate domain-specific differences in both QCA and QA tasks.
Performance metrics demonstrate domain-specific differences in both QCA and QA tasks.

These findings highlight TermGPT’s potential to improve reliability in critical applications requiring precise language understanding. Perfect understanding remains impossible, but TermGPT represents a step towards more nuanced and context-aware systems.

The pursuit of nuanced understanding within large language models, as demonstrated by TermGPT’s multi-level contrastive learning, echoes a fundamental truth about complex systems. It isn’t about imposing order, but about cultivating an environment where meaning emerges from the interplay of context and contrast. This resonates with Marvin Minsky’s observation: “Questions must be very simple and answers may be very complex.” TermGPT doesn’t seek to control terminology; rather, it encourages the model to discern subtle differences—a promise made to the past regarding consistent definitions—allowing a more robust, self-correcting understanding of specialized domains like finance and law. The system, left to its own devices within a carefully constructed framework, begins fixing itself, adapting to the inherent ambiguity of language.

What Lies Ahead?

The pursuit of “terminology-aware” models, as exemplified by TermGPT, reveals a fundamental tension. It assumes terminology is a fixed point, an anchor in the shifting semantic landscape. This is, predictably, an illusion. Terms evolve, bifurcate, and accrue contextual baggage. The framework addresses sparsity and ambiguity, but it cannot legislate against future linguistic drift. The system is built on the premise of catching meaning—a fundamentally reactive posture.

Future work will inevitably confront the inherent instability of these models. Contrastive learning, while effective, simply delays the inevitable divergence between the model’s internal representation and the lived reality of legal or financial discourse. A guarantee of continued performance is, of course, a contract with probability. The more interesting question isn’t how to prevent drift, but how to build systems that gracefully accommodate—perhaps even learn from—controlled semantic chaos.

Stability, it should be remembered, is merely an illusion that caches well. The focus should shift from fine-tuning for specific terms to developing architectures that facilitate continuous adaptation, recognizing that the true measure of a language model isn’t its current accuracy, but its resilience in the face of inevitable change. The ecosystem, not the tool, is the operative unit.

Original article: https://arxiv.org/pdf/2511.09854.pdf

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

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2025-11-14 15:22