Description
FrameLLaMA-3.1-8B-Instruct-FullFN17 is a frame-aware language model designed to improve event-level semantic reasoning in Large Language Models (LLMs). The model injects structured knowledge from FrameNet 1.7 into Llama-3.1-8B-Instruct using parameter-efficient LoRA fine-tuning.
Unlike standard instruction tuning, this model leverages principle-oriented supervision, where frame definitions, participant roles, semantic types, lexical senses, and frame-to-frame relations are converted into structured question–answer tasks. This enables the model to learn reusable semantic constraints rather than isolated facts.
The model is optimized for tasks where meaning depends on event structure, participant roles, and lexical disambiguation, such as Natural Language Inference (NLI) and Semantic Role Labeling (SRL).
Model Details
- Base Model: Llama-3.1-8B-Instruct
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Training Data: FrameNet 1.7 (full inventory, 1,200+ frames)
- Supervision Type: Principle-oriented QA-style prompts
- Tasks: NLI, SRL (evaluation), semantic reasoning
- Model Type: Instruction-tuned causal language model
Key Features
- ✅ Full FrameNet Coverage: Trained on 1,200+ frames.
- ✅ Principle-Oriented Learning: Encodes role constraints, semantic types, and frame relations
- ✅ Event-Level Reasoning: Improves understanding of causality, entailment, and contradiction
- ✅ Frame-Aware Inference: Better handling of lexical ambiguity and role compatibility
- ✅ Parameter-Efficient Training: Uses LoRA for scalable adaptation
- ✅ Generalization Beyond SRL: Transfers to NLI and semantic inference tasks
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Evaluated on:
- SNLI (diagnostic subset) for event-level inference
- CONLL-style FrameNet SRL dataset (via OpenSesame preprocessing)
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Observed improvements:
- Strong gains in entailment and contradiction detection
- Improved frame identification and role-span alignment in SRL
- Reduced reliance on surface-level lexical cues
Use Cases
- Natural Language Inference (NLI): Event-based reasoning and entailment detection
- Semantic Role Labeling (SRL): Frame and role prediction
- Event Understanding: Modeling causality and participant structure
- Linguistically-Informed AI: Applications requiring structured semantic interpretation
- Research on LLM Interpretability: Studying structured knowledge injection
Training Details
- FrameNet structures (definitions, roles, relations) are linearized into QA-style templates
- Supervision includes:
- Frame definitions
- Role constraints
- Semantic types
- Lexical unit disambiguation
- Frame-to-frame relations
- Negative samples generated via similarity-based filtering
- Fine-tuned using LoRA for efficiency and scalability
GitHub
For training scripts, datasets, and evaluation:
👉 https://github.com/crux82/FrameLLaMA
Citation
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