LLMiner: Autonomous Domain Knowledge Mining

• LLMiner is a class of LLM-driven frameworks that autonomously extracts domain-specific structured knowledge from various unstructured sources using methods like chain-of-thought reasoning and combinatorial search.
• It employs advanced techniques such as prompt engineering, Monte Carlo Tree Search, and evaluative feedback to optimize relevance, novelty, and extraction accuracy.
• LLMiner applications span quantitative finance, biomedical informatics, chatbot QA enhancement, and unsupervised machine translation, outperforming traditional methodologies.

LLMiner is a designation for a class of LLM-driven frameworks that autonomously mine, extract, or synthesize domain-specific structured knowledge from unstructured sources, typically with minimal or no human annotation. LLMiner architectures leverage advanced prompt engineering, chain-of-thought reasoning, combinatorial search, and feedback mechanisms to advance the state of automated knowledge extraction and formulaic search in contexts spanning finance, biomedical informatics, domain chatbot bootstrapping, and unsupervised machine translation. In recent literature, LLMiner variants have been instantiated in alpha factor mining for quantitative finance, knowledge QA pair harvesting for conversational systems, in-context example mining for machine translation, and large-scale biomedical profile extraction.

1. System Architectures and General Workflow

LLMiner frameworks are characterized by multi-component designs integrating LLM agents for generative reasoning and knowledge distillation, a controlling module for search or mining orchestration, and evaluative modules for feedback and curation.

• Alpha Factor Mining: In quantitative finance, LLMiner instantiates as an LLM-powered Monte Carlo Tree Search (MCTS) engine (Shi et al., 16 May 2025). The system contains an LLM agent for symbolic formula generation, an MCTS controller for exploration/exploitation, a backtester providing multidimensional quantitative metrics (IC, IR, interpretability, stability), and a frequent subtree avoidance module enforcing formulaic novelty. Another variant, AlphaAgent (Tang et al., 24 Feb 2025), uses a tri-agent loop with explicit regularizations for originality, alignment, and complexity rooted in abstract syntax tree (AST) metrics.
• Domain QA Extraction: For chatbot bootstrapping, LLMiner extracts question–answer pairs via chain-of-thought reasoning, building a mixed instructional corpus for fine-tuning (Zhang et al., 2023). The pipeline parses a raw text corpus into candidate sentences, chains through significance analysis, question and answer synthesis using a pre-trained reasoning-capable LLM, aggregating mined data for downstream chat agent tuning.
• Unsupervised Parallel Data Mining: In unsupervised MT, LLMiner operates as a self-miner of synthetic in-context translation pairs from monolingual data through a sequence of bilingual lexicon induction, synthetic sentence mining, and filtering based on sentence similarity and BM25 scores (Mekki et al., 14 Oct 2024).
• Biomedical Information Extraction: In "IHC-LLMiner" (Kim et al., 1 Apr 2025), the framework encompasses abstract relevance classification, extraction of structured biomarker profiles from free-text, normalization to UMLS concepts, and large-scale aggregation across corpora.

2. Principal Algorithms and Regularization Mechanisms

LLMiner implementations rely on advanced generative search, regularization criteria, and evaluative feedback to optimize output relevance, novelty, and utility.

Search and Mining Procedures

• Generative Tree Search: For alpha factor mining, MCTS is used to navigate the space of symbolic formulas with UCT selection:

$$\text{UCT}(s, a) = Q(s,a) + c \sqrt{\frac{\ln N(s)}{N(s,a)}}$$

Backtesting metrics are used as reward signals to score search tree expansions (Shi et al., 16 May 2025).

• Chain-of-Thought Analysis: Knowledge mining via LLMiner produces (analysis, question, answer) triplets by sequential LLM generations, governed by maximum likelihood training with GPT-4-seeded examples:

$$\mathcal{L}_{\text{miner}}(\theta) = -\sum_{i=1}^{600} \left[ \log P_\theta(a^i|p^i,s^i) + \log P_\theta(q^i|p^i,s^i,a^i) + \log P_\theta(r^i|p^i,s^i,a^i,q^i) \right]$$

(Zhang et al., 2023).

• Unsupervised Parallel Selection: In translation mining, the framework builds a pool of candidate parallel sentences through word-level mining, leverages LLM for synthetic translation generation, and filters candidates using cosine similarity and BM25 scoring (TopK+BM25) (Mekki et al., 14 Oct 2024).

Regularization and Filtering

• Originality Enforcement: In factor mining, similarity against a factor zoo via AST graph similarity is penalized:

$$S(f) = \max_{\phi \in Z} s(f, \phi) / \max_{\text{node count}}$$

(Tang et al., 24 Feb 2025).

• Hypothesis Alignment: Semantic match between market hypothesis and factor description/expression is quantified using LLM-driven consistency scores:

$C(h,d,f) = c_1(h,d) + c_2(d,f)$

(Tang et al., 24 Feb 2025).

• Complexity Control: AST node counts (SL) and parameter counts (PC) are bounded hard and soft; compound regularization aggregates these with originality and alignment for optimization.

3. Representative Applications

LLMiner architectures have demonstrated efficacy in diverse domains:

• Quantitative Finance: Automated mining of alpha factors for equities, with outperforming IR and AR versus genetic programming and RL baselines. LLMiner’s subtree avoidance mechanism ensures diversity in formulaic mining (Shi et al., 16 May 2025).
• Biomedical Data Mining: Extraction and normalization of tumour immunohistochemical profiles from PubMed abstracts, producing structured data for oncology knowledge bases. Gemma-2 fine-tuned via LoRA achieves classification accuracy of 91.5% and extraction correctness of 63.3%, with UMLS normalization enabling integration and cross-abstract aggregation (Kim et al., 1 Apr 2025).
• Chatbot Tuning: Bootstrapping of domain-specialist QA corpora for conversational agents via autonomous chain-of-thought QA mining, surpassing direct instruction-tuning on raw passage data in average domain-specific answer ratings (GPT-4 judged) (Zhang et al., 2023).
• Unsupervised Machine Translation: Mining of high-quality in-context translation pairs from monolingual corpora, yielding near-supervised translation BLEU scores and surpassing other UMT techniques by $\approx$7 BLEU on strong multilingual LLMs (Mekki et al., 14 Oct 2024).

4. Quantitative Evaluation and Benchmarking

LLMiner systems are evaluated using rigorous domain metrics and benchmarked against traditional and contemporary baselines.

• Finance: Metrics include Information Coefficient (IC), RankIC, Information Ratio (IR), and Annualized Return (AR). LLMiner with MCTS and FSA yields IC = 0.055, AR = 0.111, IR = 1.18, outperforming RL and GP approaches (Shi et al., 16 May 2025). AlphaAgent’s regularization maintains IC stability over years, mitigating alpha decay (Tang et al., 24 Feb 2025).
• Biomedical Extraction: Model selection based on accuracy, F1, and extraction correctness as judged by domain experts. Gemma-2 LoRA beats GPT4-O in both accuracy (Δ+9.5%) and speed (5.9× faster), with normalized UMLS outputs matching curated ranges in majority cases (Kim et al., 1 Apr 2025).
• Chatbot QA Mining: Evaluation by GPT-4 judge on 5-point Likert scales over four specialist domains; LLMiner QA augmentation consistently lifts benchmark scores by 2–6 points (Zhang et al., 2023).
• Translation Mining: BLEU (spBLEU), chrF++ metrics, ablation on example count ($k$), filter thresholds, and linguistic feature correlations (word overlap, genetic distance) are reported. Performance of mined examples within 1 BLEU of supervised extraction (Mekki et al., 14 Oct 2024).

5. Scalability, Automation, and Human Intervention

A hallmark of LLMiner frameworks is pipeline scalability and minimal dependence on manual labeling.

• Domain QA Extraction: Only 600 seed examples (GPT-4-generated) required; subsequent mining is fully autonomous (Zhang et al., 2023).
• Biomedical Mining: 1,000 manually annotated abstracts suffice for highly accurate classification and profile extraction over 107,759 abstracts (Kim et al., 1 Apr 2025).
• Alpha Factor Mining: Few-shot prompt templates enable LLMiner operation without specialized fine-tuning; agent loops can be re-run on schedule or drift for adaptive factor set refinement (Tang et al., 24 Feb 2025, Shi et al., 16 May 2025).

This suggests feasibility for periodic re-mining or corpus refresh to maintain up-to-date domain knowledge without labor-intensive annotation.

6. Limitations, Extensions, and Future Research

Commonly observed limitations among LLMiner implementations include sensitivity to pre-training coverage (performance drop in low-resource domains), prompt and template ordering, and bottlenecks in full-text detail extraction (abstract-only in biomedical contexts).

Potential extensions highlighted across works:

• Advanced similarity/dense retriever methods for filtering (e.g., translation mining) (Mekki et al., 14 Oct 2024).
• Integration of morphological/structural signals in filtering (Mekki et al., 14 Oct 2024).
• On-chain factor mining in decentralized finance (Tang et al., 24 Feb 2025).
• Cross-domain adaptation to other symbolic knowledge discovery tasks (scientific formula mining, control law synthesis) (Shi et al., 16 May 2025).
• Human-in-the-loop verification or user-constrained prompt systems for production deployment. A plausible implication is the broad applicability of LLMiner as a unifying paradigm for automating knowledge extraction in highly specialized, multi-domain contexts.

7. Comparative Analysis and Impact

In head-to-head benchmarks, LLMiner variants systematically outperform traditional genetic programming, vanilla RL, and transformer baselines in effective knowledge mining, predictive accuracy, efficiency, and interpretability (Shi et al., 16 May 2025, Tang et al., 24 Feb 2025, Zhang et al., 2023, Mekki et al., 14 Oct 2024, Kim et al., 1 Apr 2025). The combination of LLM creativity, structured exploration (e.g., MCTS), regularized novelty enforcement, and rigorous feedback mechanisms underpins their competitive edge. These frameworks are poised to accelerate knowledge base construction, financial factor innovation, and domain-specialist chatbot training at previously unattainable scale and automation.