Decoding Domain Speak: Enhancing Language Models with Specialized Terminology

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Author: Denis Avetisyan

A new framework, TermGPT, addresses the challenges of ambiguous and sparse data in legal and financial texts to improve large language models’ understanding of specialized vocabulary.

TermGPT constructs a sentence graph—nodes representing sentences connected by edges denoting semantic and lexical relationships—and leverages each node as an anchor for data augmentation, generating question-candidate-answer pairs to facilitate contrastive learning that refines terminology embeddings based on nuanced categorical distinctions, effectively capturing and resolving ambiguity in technical language.
TermGPT constructs a sentence graph—nodes representing sentences connected by edges denoting semantic and lexical relationships—and leverages each node as an anchor for data augmentation, generating question-candidate-answer pairs to facilitate contrastive learning that refines terminology embeddings based on nuanced categorical distinctions, effectively capturing and resolving ambiguity in technical language.

This work introduces a multi-level contrastive learning approach leveraging sentence graphs to improve terminology adaptation in high-stakes domains.

While large language models excel at text generation, their embedding spaces often struggle to discern nuanced, domain-specific terminology—a critical limitation in fields like law and finance. This paper introduces TermGPT: Multi-Level Contrastive Fine-Tuning for Terminology Adaptation in Legal and Financial Domain, a novel framework employing multi-level contrastive learning to enhance terminology understanding by leveraging sentence graphs and generating contextually-aware training samples. Experiments demonstrate that TermGPT significantly improves performance on term discrimination tasks within these high-stakes domains, outperforming existing baselines. Could this approach unlock more reliable and accurate applications of LLMs in areas demanding precise semantic interpretation?

The Limits of Statistical Comprehension

Large language models (LLMs) demonstrate remarkable capabilities in general natural language processing, but performance declines when applied to specialized domains. This limitation stems from a reliance on broad statistical patterns rather than precise semantic comprehension, leading to inaccuracies with nuanced terminology. Existing fine-tuning methodologies are often inefficient, demanding substantial labeled data and computational resources. The challenge lies in ensuring the model grasps subtle distinctions within a domain-specific lexicon.

The models demonstrate varying levels of performance, as measured by the LLM Score, across the tested datasets, highlighting dataset-specific model strengths.
The models demonstrate varying levels of performance, as measured by the LLM Score, across the tested datasets, highlighting dataset-specific model strengths.

A core difficulty is preserving semantic precision during adaptation. Simply exposing an LLM to domain-specific text is insufficient; the model may learn superficial patterns without internalizing meaning. This highlights the need for techniques that effectively transfer knowledge while maintaining semantic integrity—akin to refining an equation without altering its fundamental truth.

TermGPT: A Framework for Precise Semantic Mapping

TermGPT addresses terminology understanding through a multi-level contrastive learning approach, balancing global context with fine-grained token representations. This architecture moves beyond keyword matching, aiming for a deeper semantic grasp of terminology. The core of TermGPT lies in utilizing both sentence-level and token-level contrastive learning, discerning meaning at different granularities and improving ambiguity resolution.

Data augmentation is critical for enhancing TermGPT’s performance and reducing reliance on extensive labeled datasets. The framework leverages a Sentence Graph to generate diverse and accurate training pairs, expanding the training data and improving robustness through exposure to linguistic variations.

Scalable Implementation Through Efficient Optimization

TermGPT utilizes large-scale generative language models as foundational encoders, specifically Qwen3-8B-Instruct and LLaMA3-8B-Instruct. Training efficiency was prioritized through LoRA, a parameter-efficient fine-tuning method, and DeepSpeed-ZeRO2 for memory optimization, enabling larger batch sizes and more complex models. The AdamW optimizer was selected for its established performance in language model training.

Supervised Fine-Tuning (SFT) was integrated to align TermGPT’s outputs with desired response characteristics, training the model on a curated dataset of terminology-related questions and answers.

Empirical Validation in Specialized Domains

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

Performance distinctions emerge between different domains when evaluated on both Question Complexity Analysis (QCA) and Question Answering (QA) tasks, indicating domain-dependent task proficiency.
Performance distinctions emerge between different domains when evaluated on both Question Complexity Analysis (QCA) and Question Answering (QA) tasks, indicating domain-dependent task proficiency.

These findings highlight TermGPT’s potential to significantly enhance the performance of Large Language Models in critical applications where precise language understanding is paramount; if it feels like magic, the underlying principles are simply revealed.

The pursuit of reproducible results, central to TermGPT’s methodology, aligns with a fundamental tenet of logical rigor. As Bertrand Russell observed, “The point of contact between mathematics and life is that mathematics is true, and life is not.” TermGPT seeks to imbue large language models with a similar truthfulness, specifically regarding domain-specific terminology. The framework’s multi-level contrastive learning addresses the inherent ambiguity within legal and financial language, striving for deterministic outputs—a demonstrable understanding, rather than merely statistical correlation. This pursuit of provable accuracy, as opposed to simply ‘working’ on test datasets, is paramount in high-stakes contexts where reliability is non-negotiable.

Future Directions

The pursuit of terminology-aware large language models, as exemplified by TermGPT, reveals a fundamental tension. While empirical gains in legal and financial contexts are readily demonstrated, the core challenge remains: achieving genuine semantic understanding, rather than sophisticated pattern matching. The multi-level contrastive learning approach offers a refinement, but does not resolve the issue of distributional ambiguity inherent in natural language. A truly elegant solution will require a shift from feature-based representations to symbolic reasoning—a formalization of domain knowledge that transcends mere statistical correlation.

Current evaluations, predicated on task-specific benchmarks, provide only a limited view. The true measure of progress lies not in achieving higher scores on curated datasets, but in the model’s capacity to generalize to unforeseen circumstances and to identify logical inconsistencies. Further research must prioritize the development of adversarial testing methodologies—rigorous challenges designed to expose the limitations of these systems and to force a deeper engagement with the underlying semantics.

Ultimately, the field must confront the inherent complexity of legal and financial language. The creation of a model that can flawlessly navigate this landscape is not merely a technical problem, but a philosophical one—a quest to codify, in algorithmic form, the very principles of reasoning and interpretation. The asymptotic behavior of such a system—its scalability to ever-more complex scenarios—will be the ultimate arbiter of success.

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 14:52