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sag-uniroma2
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sag-uniroma2
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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).
Evaluated on:
Observed improvements:
The following results are reported in the ACL 2026 paper Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs associated with this model.
| Dataset | Setting | Meta-Llama-3.1-8B-Instruct | FrameLLaMA-3.1-8B-Instruct-FullFN17 |
|---|---|---|---|
| CONFER | Zero-shot | 0.52 | 0.55 |
| CONFER | Few-shot | 0.39 | 0.75 |
| SNLI Diagnostic | Zero-shot | 0.60 | 0.62 |
| SNLI Diagnostic | Few-shot | 0.73 |
| Evaluation | Meta-Llama-3.1-8B-Instruct | FrameLLaMA-3.1-8B-Instruct-FullFN17 |
|---|---|---|
| LU Identification F1 | 0.50 | 0.62 |
| Frame Prediction F1 | 0.66 | 0.81 |
| Roles + Span F1 | 0.11 | 0.20 |
These results are from the published ACL 2026 study and evaluate the model on frame-semantic and event-level reasoning tasks. They are not general-purpose benchmark scores such as MMLU or ARC.
For the complete experimental setup, datasets, prompting methodology, and analysis, see the paper cited below.
Single token or short response:
Outputs are concise and reflect event-level semantic reasoning.
FrameNet structures (definitions, roles, relations) are linearized into QA-style templates
Supervision includes:
Negative samples generated via similarity-based filtering
Fine-tuned using LoRA for efficiency and scalability
For training scripts, datasets, and evaluation:
👉 https://github.com/crux82/FrameLLaMA
If you use this model, please cite:
bibtex
@inproceedings{rai-etal-2026-frame, title = "Frame-Semantic Knowledge Injection for Event-Level Inference in {LLM}s", author = "Rai, Shahid Iqbal and Croce, Danilo and Basili, Roberto", editor = "Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David", booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2026", address = "San Diego, California, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2026.acl-short.55/", doi = "10.18653/v1/2026.acl-short.55", pages = "664--678", ISBN = "979-8-89176-391-3", abstract = "Large language models (LLMs) are fluent but often brittle when interpretation depends on external information (e.g., events or participant roles), as next-token prediction does not explicitly encode situation-level semantic constraints. FrameNet provides a structured account of semantics through its inventory of frames, roles, and relations. We present a scalable framework that injects frame-semantic knowledge into LLMs via LoRA, moving from fact-oriented prompting to principle-oriented supervision over the full FrameNet inventory. The supervision encodes semantic constraints through semantic types, sense-aware definitions, frame relations, and role-annotated examples. To test whether this knowledge generalizes beyond surface cues, we use Natural Language Inference (NLI) as a diagnostic task for event-level reasoning. Experiments on CONFER and SNLI show consistent gains over Meta-Llama-3.1-8B-Instruct in zero-shot and few-shot settings, especially for entailment and contradiction. Complementary semantic role labeling analyses further indicate improved sensitivity to frame, role, and span structure."}Rai, Shahid Iqbal, Croce, Danilo, and Basili, Roberto. (2026). Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 664–678.
ACL Anthology: https://aclanthology.org/2026.acl-short.55/ DOI: https://doi.org/10.18653/v1/2026.acl-short.55
| 0.77 |