Enterprise AI Research Analysis
Named Entity Recognition of Historical Text via Large Language Models
Large Language Models (LLMs) show promising results in Named Entity Recognition (NER) for historical documents. This study explores zero-shot and few-shot prompting strategies to address challenges like scarce annotated data and linguistic variability in historical texts. While LLMs may not fully match supervised models, they offer a viable, efficient, and training-free alternative for low-resource historical corpora.
Executive Impact Summary
LLMs provide a powerful, adaptable tool for historical data analysis, overcoming traditional data scarcity issues. Their ability to perform NER with minimal training data makes them ideal for specialized historical archives, significantly reducing manual annotation costs and accelerating research.
0% F1
Average Fuzzy F1 Score
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Average Strict F1 Score
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Strict F1 Improvement over Baseline
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Fuzzy F1 Improvement via Majority Voting
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Methodology Overview
Performance Comparison
Key Findings
Challenges & Future Work
Approach to Historical NER with LLMs
This study leveraged Large Language Models (LLMs) for Named Entity Recognition (NER) on historical texts, employing both zero-shot and few-shot prompting. The few-shot approach provided the LLM with annotated examples to adapt its predictions without extensive training, utilizing strategies to retrieve the most relevant examples.
Enterprise Process Flow
Example Retrieval
→
Prompt Generation
→
Response Processing
Example Retrieval: Identifies similar texts from training/development sets using either Lexical Overlap (TF-IDF based token similarity) or Embedding Similarity (semantic similarity via `distiluse-base-multilingual-cased-v2` model). Random selection was also used for baseline comparison.
Prompt Generation: The retrieved examples and the target text are used to construct a prompt, adhering to a predefined template for the LLM API.
Response Processing: The LLM's output, a Python list of entity tuples, is converted into the IOB annotation format for evaluation, discarding tuples not found in the input text.
Additionally, Majority Voting was applied across three runs of experiments to enhance robustness and filter spurious predictions, assigning the final tag based on the most frequent vote for each token.
LLM Performance vs. State-of-the-Art
The study compared LLM-based prompting against established supervised State-of-the-Art (SOTA) methods on the HIPE-2022 dataset. While LLMs demonstrated reasonable performance, a performance gap typically exists compared to models fine-tuned directly on extensive annotated corpora.
| Dataset | SOTA Strict F1 | LLM Strict F1 | Strict F1 Delta | SOTA Fuzzy F1 | LLM Fuzzy F1 | Fuzzy F1 Delta |
|---|---|---|---|---|---|---|
| ajmc (de) | 0.934 | 0.728 | -0.206 | 0.952 | 0.769 | -0.183 |
| ajmc (en) | 0.877 | 0.657 | -0.220 | 0.933 | 0.754 | -0.179 |
| hipe2020 (de) | 0.794 | 0.579 | -0.215 | 0.876 | 0.690 | -0.186 |
| sonar (de) | 0.529 | 0.580 | +0.051 | 0.695 | 0.717 | +0.022 |
| topres19th (en) | 0.787 | 0.709 | -0.078 | 0.838 | 0.752 | -0.086 |
While LLM-based methods generally exhibit lower F1 scores compared to SOTA, they offer significant advantages in terms of cost-effectiveness and applicability in low-resource, multilingual contexts where annotated data is scarce. Notably, the LLM approach slightly outperformed SOTA on the Sonar dataset, demonstrating its potential in specific scenarios.
Effectiveness of Few-Shot Prompting
Few-shot prompting significantly improved NER performance over the zero-shot baseline across all datasets. Crucially, even a single in-context example proved effective, highlighting the LLM's strong in-context learning capabilities. Counterintuitively, providing more examples often led to decreased performance, possibly due to exceeding optimal context windows.
33.9%
Average Strict F1 Improvement over Zero-Shot Baseline with Few-Shot Learning
The choice of example selection strategy (lexical overlap, embedding similarity, or random) had less impact than the mere presence of an example, suggesting LLMs generalize well from minimal demonstrations regardless of their specific source.
Impact of Majority Voting
Applying majority voting over multiple runs generally led to improved performance, especially under fuzzy evaluation settings. While gains were modest and not always statistically significant under strict evaluation, it demonstrates a robust way to reduce prediction variance.
LLMs: A Viable Alternative for Historical NER
LLMs offer a cost-effective, language-agnostic, and training-free alternative for historical NER, particularly where annotated data is scarce or expensive to produce. While not yet matching fully supervised SOTA, they provide a strong baseline and a flexible tool for researchers in digital humanities, expanding access to historical corpora.
Current Limitations
The study acknowledges several limitations: small HIPE-2022 test sets introduced high variance, experiments were restricted to a single LLM (DeepSeek-V3-0324), and longer prompts in few-shot settings sometimes exceeded the model's optimal context window, diminishing performance. Results might differ across other LLM architectures or different training data.
Future Research Directions
Future work should focus on prompt optimization, including compression techniques and methods for constructing concise, informative prompts. Developing more sophisticated retrieval strategies that leverage semantic and historical metadata could enhance in-context examples. Expanding evaluation across broader historical corpora and additional languages would strengthen the generalizability of these findings and the role of LLMs in multilingual historical research.
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