LLM-Based Workflow for BT Generation

• LLM-based BT generation is an automated framework that leverages large language models to standardize and validate technical terminology across multiple languages.
• The workflow integrates retrieval, generation, verification, and optimization steps using metrics like BLEU, EMR, SMR, and IRS to ensure high semantic consistency.
• Empirical evaluations demonstrate its scalability and accuracy, achieving up to 100% semantic match and robust performance in various language paths.

An LLM-based workflow for BT (Back-Translation) generation refers to the end-to-end automation of cross-lingual terminology validation and standardization using LLMs to enforce semantic integrity and consistency across multiple languages. This paradigm is engineered to surpass the limitations of manual, expert-driven standardization in dynamic technical fields, providing scalable, quantitative, and interpretable mechanisms for terminology alignment via back-translation cycles (Weigang et al., 9 Jun 2025).

1. Conceptual Definition and Framework Overview

LLM-BT (LLM Back-Translation) is a fully automated framework for cross-lingual terminology standardization. The core procedure begins with an English source text $T$ in a source language $L_1$, which is translated via LLMs to one or more intermediate languages $L_2, L_3, \dots$, then back-translated to English ($L_1'$). Both the original text $T$ and each back-translated version $L_1'$ are compared at multiple levels—textual and term-specific—using metrics such as BLEU, TER, METEOR, BERTScore, Exact Match Rate (EMR), Semantic Match Rate (SMR), Information Retention Score (IRS), and Term Divergence Index (TDI). The approach targets highly consistent technical term preservation ($>$90%) across translation cycles and delivers “dynamic semantic embeddings” that are path-based, interpretable, and driven by translation trajectories (Weigang et al., 9 Jun 2025).

The principal aims are:

• Automatic recommendation of standardized, cross-lingual term compositions.
• Quantitative validation of term consistency under various LLMs and languages.
• Human-interpretable mapping of semantic “loops” undertaken through translation.

2. Algorithmic Pipeline: Retrieve → Generate → Verify → Optimize

The LLM-BT workflow decomposes into four principal stages, each with explicit functional or algorithmic definitions.

2.1 Retrieve

This stage extracts candidate technical terms $C = \{t_1, \dots, t_m\}$ from $T$ and, optionally, their existing translations via a term knowledge base.

Pseudocode:

def RetrieveTerms(T): prompt = "Extract a list of technical terms from the following English text:\n" + T C = LLM.generate(prompt) for t in C: translations[t] = KB.lookup(t) return C, translations

2.2 Generate

For each path $p_i$ through a sequence of languages, $T$ is translated forward and then back-translated to $T'_i$.

Pseudocode:

def GenerateBT(T, paths): results = {} for p in paths: # e.g., p = [L1, L2, L1] T_fwd = LLM.translate(model=p.model1, from=p[0], to=p[1], text=T) T_bwd = LLM.translate(model=p.model2, from=p[1], to=p[0], text=T_fwd) results[p] = (T_fwd, T_bwd) return results

2.3 Verify

Compares $T$ with each back-translation $T'_b$ using text-level and term-level quantitative metrics.

Relevant metrics and their formulas:

• BLEU: $BLEU = BP \cdot \exp({\sum_{n=1}^N w_n \log p_n})$
• TER: $TER = \frac{\#edits}{\text{length of reference}}$
• EMR: $EMR = \frac{|\{t : t \in C \cap C'_{bt}\}|}{|C|}$
• SMR: $SMR = \frac{|\{t : sim(t, t') > \tau\}|}{|C|}$
• IRS: $IRS$ as average information retention across terms.

Pseudocode:

def Verify(T, results): metrics = {} for p, (T_fwd, T_bwd) in results.items(): bleu = BLEU(T, T_bwd) ter = TER(T, T_bwd) meteor = METEOR(T, T_bwd) bert = BERTScore(T, T_bwd) C, _ = RetrieveTerms(T) C_bt, _ = RetrieveTerms(T_bwd) EMR = len(set(C) & set(C_bt)) / len(C) SMR = SemanticallyMatch(C, C_bt) / len(C) IRS = AvgRetention(C, C_bt) metrics[p] = {"bleu": bleu, "ter": ter, "meteor": meteor, "bert": bert, "EMR": EMR, "SMR": SMR, "IRS": IRS} return metrics

2.4 Optimize

Based on the computed metrics, the system decides whether to accept, re-generate, or queue term candidates for further review and optimization, including the expansion of alternate translation paths for redundancy.

Pseudocode:

def Optimize(metrics, translations): final_terms = {} for t in translations: for p in metrics: if metrics[p]['EMR'][t] > theta1 and metrics[p]['SMR'][t] > theta2: final_terms[t] = translations[p][t] elif metrics[p]['SMR'][t] > theta2: final_terms[t] = TopK(translations[p][t], k=3) else: prompt = "Translate preserving technical accuracy: " + context_of(t) new_trans = LLM.translate(..., prompt) final_terms[t] = new_trans return final_terms

Thresholds $(\theta_1, \theta_2)$ are chosen empirically.

3. Multipath and Serial Back-Translation Strategies

The LLM-BT workflow supports both serial and parallel back-translation paths for comprehensive verification. For serial paths, sequences such as $$EN \rightarrow ZH_{cn} \rightarrow ZH_{tw} \rightarrow EN'_{tw}$$ trace deep semantic loops, while parallel paths such as $$EN \rightarrow ZH_{cn}/PT_{br}/JA \rightarrow EN'_{cn/pt/ja}$$ allow independent robustness checks across multiple language axes.

Aggregate term-level metrics across all paths:

$$TA_{avg} = \frac{1}{|\mathcal{P}|} \sum_{p\in\mathcal{P}} TA_p$$

where $TA_p$ is the per-path term accuracy.

4. Back-Translation as Dynamic Semantic Embedding

Traditional embedding methods map text $T$ to a static, opaque vector $v \in \mathbb{R}^d$. LLM-BT instead defines an explicit, interpretable path in semantic space:

$$E_e \xrightarrow{\text{forward}} E_i = f_{L_1 \rightarrow L_2}(E_e) \xrightarrow{\text{backward}} E'_e = g_{L_2 \rightarrow L_1}(E_i)$$

By traversing multiple intermediate languages, the semantic trajectory of a term becomes an explicit, human-readable record of transformation and restoration, with each step subject to inspection. For $n$-hop serial paths, the model generalizes to:

$$E_e \rightarrow f_{L_1 \rightarrow L_2}(E_e) \rightarrow g_{L_2 \rightarrow L_3}(f_{L_1 \rightarrow L_2}(E_e)) \rightarrow \cdots \rightarrow E_e^{(n)}$$

This dynamic perspective allows back-translation loops to serve as active “semantic loop embeddings” whose interpretable outputs facilitate both human and algorithmic inspection of term stability and drift.

5. Empirical Evaluation: Metrics, Language Pairs, and Model Variants

Empirical assessment demonstrates high robustness and consistency of the LLM-BT workflow across technical domains. In the artificial intelligence and medical domains, test cases achieve:

• BLEU scores up to $0.92$
• Term-level exact match rates (EMR) exceeding $88.9\%$, reaching $100\%$ in some Portuguese and Japanese paths
• Semantic match rates (SMR) up to $100\%$
• Information retention scores (IRS) at $1.00$
• Consistently high Grok model accuracy ($90.9$–$100\%$) across evaluated translations

The following table summarizes key results for several representative translation paths:

Path	BLEU-4	EMR	SMR	IRS	Accuracy (Grok)
EN→ZHcn→EN	0.80	77.8%	88.9%	0.85	90.9%
EN→ZHtw→EN	0.87	88.9%	94.4%	0.96	100%
EN→JA→EN	0.85	88.3%	94.4%	0.98	100%
EN→PT→EN	0.92	100%	100%	1.00	100%

Case studies confirm that the workflow achieves both high surface-level fidelity (BLEU, BERTScore F1) and deeper semantic invariance (EMR, SMR, TDI) (Weigang et al., 9 Jun 2025).

6. Implementation Considerations and Practical Guidelines

For practitioners, the LLM-BT workflow can be replicated with standard LLM APIs (e.g., GPT-4, DeepSeek, Grok), employing zero-shot term extraction prompts and direct translation calls. API parameters are set to maximize determinism (temperature $=0.0$), maximize token throughput, and comply with rate limits.

Key prompt templates include:

• Term extraction: “Extract the key technical terms from the following abstract: {text}.”
• Forward translation: “Translate the following English scientific abstract into Simplified Chinese. Preserve all technical terminology exactly.”
• Back-translation: “Translate the following Simplified Chinese text back into English. Use formal academic style.”

Quantitative metrics are implemented via NLTK or equivalent backends. Workflow parallelization (e.g., batch size $=5$ per API call) accelerates processing.

The optimize step includes mechanisms for iterative re-prompting, enforcing stricter accuracy thresholds or tri-lingual chains for especially ambiguous terms.

7. Significance, Limitations, and Extensions

The LLM-BT workflow establishes a scalable and interpretable framework for cross-lingual terminology standardization, leveraging LLMs’ capabilities for high-consistency, high-throughput verification cycles. It provides interpretable dynamic embeddings in the form of translation trajectories rather than opaque vectors.

Constraints include reliance on model and path diversity—performance may vary with language pair and model alignment, and hallucination or path divergence requires redundant multipath strategies. The integration of human review for low-confidence outputs supports optimal semantic and cultural adaptation.

In summary, LLM-based BT generation delivers a reproducible, quantitatively validated, and human-readable pipeline for technical terminology alignment, and can be extended to additional applications where cross-lingual semantic invariance is mandatory (Weigang et al., 9 Jun 2025).