LLaMA-LoRA Neural Prompt Engineering: A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains
Bibliographic record
Abstract
ABSTRACT The exption of Chinese natural language processing (NLP) has stimulated research in the broader NLP domain. However, existing large language models have limitations in comprehending and reasoning in Chinese. This paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource requirements. We propose LLaMA-LoRA, a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation (LoRA) of Large Language Models technique for refinement. Chain-of-Thought (CoT) are crucial for generating intermediate reasoning chains in language models, but their effectiveness can be limited by isolated language patterns. Erroneous reasoning resulting from conventional prompts negatively impacts model performance. Automatic prompts are introduced to encourage reasoning chain generation and accurate answer inference. Training the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning abilities. The LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks, surpassing benchmark performance achieved by related language models such as GPT-3.5, Chat-GLM, and OpenAssistant, delivering accurate, comprehensive, and professional answers. The availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".