Interlinear glossed text (IGT) creation remains a major bottleneck in linguistic documentation and fieldwork, particularly for low-resource morphologically rich languages. We present a hybrid automatic glossing pipeline that combines neural sequence labeling with large language model (LLM) post-correction, evaluated on Jungar Tuvan, a low-resource Turkic language. Through systematic ablation studies, we show that retrieval-augmented prompting provides substantial gains over random example selection. We further find that morpheme dictionaries paradoxically hurt performance compared to providing no dictionary at all in most cases, and that performance scales approximately logarithmically with the number of few-shot examples. Most significantly, our two-stage pipeline combining a BiLSTM-CRF model with LLM post-correction yields substantial gains for most models, achieving meaningful reductions in annotation workload. Drawing on these findings, we establish concrete design principles for integrating structured prediction models with LLM reasoning in morphologically complex fieldwork contexts. These principles demonstrate that hybrid architectures offer a promising direction for computationally light solutions to automatic linguistic annotation in endangered language documentation.
Figures & tables
Metric
#
Total sentences
895
Narratives
40
Unique grammatical morphemes
240
Unique content morphemes
1258
Average words per sentence
8.38 (± 5.71)
Average morphemes per word
1.69 (± 0.30)
Table 1: Corpus statistics for the Tuvan fieldwork dataset.
Figure 1: Hybrid pipeline combining BiLSTM-CRF structured prediction with LLM post-correction using retrieval-augmented prompting.
Model
Random
RAG
deepseek-v3.2-exp
0.118
0.506
qwen3-max
0.062
0.381
gpt-4o-mini
0.103
0.396
gemma-3-27b-it
0.068
0.344
Table 2: Retrieval-augmented prompting (RAG) vs. random example selection across four LLMs (3-shot, no glossary). All models show meaningful improvement with RAG.
Figure 2: Experiment 2: n-shot scaling curves for RAG LLM generation. Performance scales approximately logarithmically with example count, plateauing around n=10–15 for most models. The BiLSTM baseline (0.474) is provided in the text for reference.
Figure 3: Experiment 3: glossary ablation results. Partial glossaries (Top-100, Grammatical) hurt performance compared to no glossary, while complete glossaries show modest gains. The negative effect suggests models are usually distracted by morphological information. The BiLSTM baseline (0.474) is provided in the text for reference.
Figure 4: Experiment 4: hybrid pipeline improvement over RAG LLM generation. Solid lines show hybrid accuracy (BiLSTM + LLM correction), dashed lines show pure N-Shot baseline from Experiment 2, and shaded areas indicate improvement. The hybrid approach consistently improves performance across all four models, particularly in low-shot scenarios (n=1–5).
Frequency
Count
BiLSTM
Hybrid
Infrequent (1 to 5)
69
0.029
0.426
Common (6 to 20)
58
0.448
0.724
Frequent (over 20)
86
0.860
0.859
Table 3: Accuracy by training frequency for lexical morphemes. Hybrid improvements concentrate in infrequent morphemes (+0.397), with no test morphemes completely unseen in training.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Glossary
deepseek
qwen3
gpt-4o
gemma-3
None
0.510
0.436
0.420
0.325
Top-100
0.469
0.332
0.353
0.430
Grammatical
0.426
0.303
0.364
0.335
Entire
0.490
0.422
0.369
0.455
Appendix
Table 4: Glossary ablation study across four LLMs (3-shot RAG). Partial glossaries consistently degrade performance, while complete dictionaries fail to help most models. Model names abbreviated: deepseek = deepseek-v3.2-exp , qwen3 = qwen3-max , gpt-4o = gpt-4o-mini , gemma-3 = gemma-3-27b-it .
N
deepseek
qwen3
gpt-4o
gemma-3
1
0.350
0.196
0.159
0.118
3
0.505
0.439
0.383
0.336
5
0.588
0.519
0.423
0.471
10
0.658
0.529
0.430
0.600
15
0.646
0.611
0.486
0.540
20
0.626
0.552
0.475
0.534
Appendix
Table 5: N-shot scaling for RAG LLM generation (RAG, no glossary). Performance improves logarithmically, with diminishing returns beyond n=10–15. Model names abbreviated as in Table 4 .