Domain-Specific Keyword Injection

• Domain-specific keyword injection is a technique that enhances language models by integrating curated domain-specific terms without modifying core model parameters.
• It uses dynamic retrieval, prompt-based methods, and adapter fusion to improve specialized reasoning and retrieval accuracy, as evidenced by significant performance gains.
• Empirical studies show notable improvements, such as a 44.4% increase in biomedicine QA, by aligning injected keywords with domain-specific tasks.

Domain-specific keyword injection is a technique for augmenting neural LLMs and retrieval systems with carefully selected terms or entities characteristic of a particular field, knowledge base, ontology, or database. This strategy has emerged as a central paradigm for equipping general-purpose models with the capacity to perform specialized reasoning, retrieval, and generation in medical, legal, financial, and scientific domains. By explicitly integrating domain tags, schema tokens, or keyphrases—often at inference or as a prompt—the model’s output is biased toward domain-relevant vocabulary and semantically anchored concepts, improving both coverage and accuracy on domain-specific downstream tasks.

1. Theoretical Foundations and Formal Characterization

Domain-specific keyword injection operates by integrating a curated set of domain terms or embeddings, denoted as $K = \{k_1, \ldots, k_m\}$, into the input, either as explicit tokens or as continuous prompt vectors, without modifying the backbone parameters $\theta$ of the underlying LLM. More formally, for a model $M(\cdot\,; \theta)$, the output with keyword injection is:

$$\mathbf{y} = M\left([\mathbf{p}_{\mathrm{dom}}(K), \mathbf{x}];\,\theta\right)$$

where $\mathbf{p}_{\mathrm{dom}}(K)$ is a literal template (tokenized keywords) or a learned “soft prompt.” This stands in contrast to static embedding (permanent parameter updates, $\Delta\theta$), modular adapters (side modules with extra parameters $\phi$), or knowledge graph augmentation, all of which require weight or architecture changes (Song et al., 15 Feb 2025). Keyword injection thus provides a non-invasive, input-level mechanism for modulating model behavior.

Parameterized approaches to domain-specific keyword selection involve an alignment heuristic or similarity function $A(t, k_j) = \mathrm{sim}(\phi(t), \psi(k_j))$ between a text input $t$ and candidate knowledge tuple or keyword $k_j$, where $\phi, \psi$ are textual and knowledge-space encoders. Selection probabilities can be defined as a softmax over $A(t,k_j)$, with injection proceeding via concatenation, mid-layer adapter fusion, or prompt-based templates (Fu et al., 2023).

2. Taxonomy of Methods: Dynamic, Prompt-Based, Adapter, and Graph-Driven Approaches

Dynamic and Prompt-Based Injection

Dynamic keyword injection retrieves relevant terms from a knowledge base or index (via BM25, TF-IDF, or learned retrievers) as a function of the input, constructs a prompt or prepended segment, and feeds this into the LLM for downstream generation or retrieval (Song et al., 15 Feb 2025, Wei et al., 31 May 2025). In contrast, prompt optimization (soft-prompting) learns a continuous matrix $P \in \mathbb{R}^{\ell \times d}$ that encodes the effect of domain keywords, with $P^*$ found by minimizing task loss over a domain-specific dataset. Both approaches keep the model weights $\theta$ unchanged, ensuring rapid adaptability and minimal compute overhead at inference.

Adapter-Based and Fusion Techniques

Adapter-based methods insert small, parameter-efficient modules at each transformer layer. These modules are pre-trained to store and recall domain facts (e.g., entity–attribute KB tuples) via a masked-fact denoising objective, then integrated at inference by learned gating mechanisms (Emelin et al., 2022). This supports rapid knowledge updates, as only the adapters are retrained when the underlying KB changes.

Knowledge Graph, Graph-Walk, and Structure-Aware Mechanisms

Graph-based planning, as exemplified in legal clause generation (Joshi et al., 2023), constructs a directed, weighted graph $G = (V, E)$ with nodes representing topics and keywords, and edges weighted by topic-to-keyword or keyword-to-keyword co-occurrence statistics. Keyword plans are generated by greedy or beam search walks over this graph, supporting both generic-to-specific content ordering and user-controlled keyword constraints.

Structure-aware domain knowledge injection, such as StructTuning (Liu et al., 2024), explicitly extracts a domain taxonomy from raw corpora, reconstructs a knowledge hierarchy via LLM prompting, and conditions both pre-training and supervised fine-tuning on explicit path strings from the taxonomy, thereby strongly anchoring textual segments in knowledge-point space.

3. Pipeline Components: Extraction, Weighting, Alignment, and Integration

A typical domain-specific keyword injection pipeline consists of:

1. Keyword/Entity Extraction:
   • Automatically via high-PMI n-gram mining, diff-IDF, or LLM-based summarization (Miao et al., 2020, Liu et al., 2024)
   • Alignment via similarity heuristics between input and knowledge base entries ($A(t, k_j)$), or with BM25 or embedding-based retrieval (Wei et al., 31 May 2025, Fu et al., 2023)
2. Selection and Weighting:
   • Terms are filtered by frequency, domain-specificity (e.g., diff-IDF), and relevance thresholds.
   • Graph-based approaches normalize edge weights by occurrence position and topic frequency to ensure generic-to-specific ordering (Joshi et al., 2023).
3. Integration:
   • Prompts: Constructed as “[Keywords:] $k_1; k_2; \ldots ; k_m$ [Question:]” or as structured JSON for tasks like Text-to-SQL (Chen et al., 18 Sep 2025).
   • Adapters: Fused via weighting functions or gating in the transformer backbone (Emelin et al., 2022).
   • Attention: Keyword-aware attention layers restrict self-attention to cross-sentence or cross-token pairs involving identified domain keywords (Miao et al., 2020).
4. Augmentation and Supervision:
   • Data augmentation with KG-derived or corpus-matched replacements (KnowledgeDA (Ding et al., 2022)).
   • Confidence-weighted selection for training instances near a target model confidence threshold.
   • Negative sampling strategies driven by keyword overlap and coverage for robust training (Miao et al., 2020).

4. Empirical Efficacy and Task-Specific Results

Domain-specific keyword injection yields substantial improvements in specialized benchmark performances. Empirical results include:

Study/Model	Task	Baseline	Keyword Injection	Δ (Absolute)
MedQA (biomedicine)	QA (multiple)	43.7%	88.1%	+44.4
PubMedQA (biomedicine)	QA	74.3%	83.5%	+9.2
DeKeySQL (NL2SQL BIRD)	SQL Gen (Dev EX)	62.3%	69.1%	+6.8
Legal clause gen (BLEU)	Text Gen	33.4	48.98	+15.6
Finance-RAG MAP@10	Retrieval	32.8	36.2	+3.4
Paired BERT acc (QA)	Semantic Matching	93.9%	95.1%	+1.2

In text-to-SQL (Ma et al., 2024, Chen et al., 18 Sep 2025), keyword injection via schema + value serialization and explicit decomposition of user intent leads to increased column/table accuracy and synonym robustness. In legal, advertising, and biomedical domains, the injection of domain-constrained or graph-selected terms has been shown to raise retrieval, generation, and classification benchmarks by multiple points over both BERT-style and seq2seq-only baselines (Song et al., 15 Feb 2025, Joshi et al., 2023, Zhou et al., 2019).

Ablation experiments confirm that naive or random keyword injection can dilute performance, but careful alignment and pruning (e.g., ConceptualKB) recovers task-relevant improvements (Fu et al., 2023).

5. Application Domains and Practical Implementation

Domain-specific keyword injection plays a pivotal role in:

• Biomedical QA and document classification: Dynamic retrieval or prompt-based injection yields 30–45% absolute gains on benchmarks, with best performance at $m\sim$10–20 keywords per input (Song et al., 15 Feb 2025).
• Legal clause and contract generation: Graph-planning followed by keyword-aware conditioning ensures the inclusion of stipulated legal jargon and structure (Joshi et al., 2023).
• Retrieval-augmented generation (RAG) for private corpora: BM25-based signal distillation (BMEmbed) aligns embedding spaces for specialized terminology (Wei et al., 31 May 2025).
• Task-oriented dialogue: Light-weight modular adapters memorize and fuse KB facts, supporting dynamic updates at fine granularity (Emelin et al., 2022).
• Advertising: Reinforcement learning–guided domain bias optimizes keyword novelty and domain-consistency under commercial constraints (Zhou et al., 2019).
• Text-to-SQL pipelines: Schema-linked keyword extraction and explicit decomposition improve SQL generation and downstream execution match (Chen et al., 18 Sep 2025).

Implementation best practices include precise selection of domain term sets (BM25, embedding similarity, or knowledge graph proximity), template prompt design, and threshold tuning for pruning and regularization (Song et al., 15 Feb 2025, Fu et al., 2023).

6. Challenges, Pitfalls, and Future Directions

Several empirical and theoretical challenges remain:

1. Noise and Alignment Sensitivity: Merely injecting random knowledge tuples (unaligned) yields comparable results to aligned injection in some settings. Pruning to high-quality, abstract concepts (ConceptualKB) restores alignment sensitivity (Fu et al., 2023).
2. Knowledge Consistency: Injected keywords may introduce conflicts or hallucinations; robust filtering and context-coherence checking are critical (Song et al., 15 Feb 2025).
3. Cross-Domain Transfer and Scalability: Current pipelines are mainly domain-centric; composing keyword sets for cross-cutting domains or automating discovery is an open problem.
4. Prompt Design and Robustness: Generalizing prompt templates across architectures and tasks is largely manual and remains susceptible to minor input changes.
5. Dynamic Adaptation: Real-time domains (e.g., finance) benefit more from dynamic retrieval than prompt-tuning, but at increased infrastructure cost (Song et al., 15 Feb 2025).
6. Augmentation and Evaluation: Systematic augmentation (e.g., PA-RAG(Bhushan et al., 12 Feb 2025), KnowledgeDA (Ding et al., 2022)) and hard negative sampling are necessary but risk overfitting or catastrophic forgetting if not judiciously balanced.

Opportunities for future work include meta-learning prompt and keyword templates, contrastive fine-tuning for better text–knowledge alignment, and scalable nearest neighbor search for injection over massive knowledge bases.

7. Summary Table: Method Families and Key Characteristics

Method	Injection Site	Training Required	Dynamic/Static	Example Papers
Prompt/Token Prepend	Input	No / Minimal	Both	(Song et al., 15 Feb 2025)
Soft Prompt	Embedding Matrix	Yes (short phase)	Static	(Song et al., 15 Feb 2025)
Adapter Fusion	Mid Layer	Yes	Static	(Emelin et al., 2022)
Graph-Based Planner	Input/Prompt	No	Dynamic	(Joshi et al., 2023)
Structure-Aware Embedding	Input/Path-Cond.	Yes	Static	(Liu et al., 2024)
KG-Augmented Sample Gen.	Data/Augmentation	Yes	Static/Dyn	(Ding et al., 2022, Fu et al., 2023)

Empirical performance and infrastructure fit dictate the choice of method. Each confers distinct advantages with respect to scalability, ease of update, and downstream robustness, underlining domain-specific keyword injection as a cornerstone of modern domain adaptation in language technologies.