cs-552-2026-catma/general_knowledge_model

General Knowledge Model

This model is the General Knowledge individual-model submission for the CS-552 Modern NLP course project. It is a merged post-trained checkpoint based on `Qwen/Qwen3-1.7B`, developed by Tuan Dang Nguyen for closed-book multiple-choice general knowledge evaluation.

The uploaded checkpoint corresponds to the final Stage 5 merge-aware DPO model:

sft_dpo_stage5_error_contrastive_mergeaware_v1_r16_lr8e8_beta003_eval100_500_merged

Task And Output Format

The model receives a multiple-choice question and should answer with exactly one option letter inside a LaTeX boxed expression:

\boxed{C}

The evaluation pipeline extracts the letter inside \boxed{...}. Any surrounding reasoning is ignored for scoring, but the intended behavior is a concise boxed final answer.

Training Summary

The training campaign used LoRA-based post-training on top of Qwen/Qwen3-1.7B.

Main stages:

• —Supervised fine-tuning on mixed general-knowledge multiple-choice data.
• —Hard-source and CI-style refinements, including MMLU-Pro and variable option-count examples.
• —Plus Quartz v1 SFT, which first reached the best hidden-CI score.
• —Conservative Stage 2 SFT refinement from the Plus Quartz anchor.
• —Stage 5 merge-aware DPO using the Stage 2 model's own wrong boxed answers plus protection pairs.

The final Stage 5 model is selected because it is the strongest merged local checkpoint. The strongest hidden-CI score was first reached by the Plus Quartz SFT anchor, and the later Stage 2/DPO submissions tied that hidden score.

Evaluation

Local evaluation used the course ten-example public General Knowledge validation snapshot in both prompt modes plus a 290-example diagnostic set built from public multiple-choice sources.

Interpretation: DPO improved the retained merged local diagnostic result and made checkpoint selection more robust, but it did not improve beyond the best hidden-CI SFT score of 0.4900.

Usage Notes

This checkpoint is a fully merged model, not a standalone LoRA adapter. It can be loaded with standard transformers text-generation tooling.

For best compatibility with the course evaluator:

• —Ask closed-book multiple-choice questions.
• —Include clear answer options.
• —Require the model to finish with \boxed{LETTER}.
• —Score only the extracted boxed letter.

Example prompt:

Answer the following multiple-choice question. Return only the final answer in the form \boxed{LETTER}.

Question: Which planet is known as the Red Planet?
A) Venus
B) Mars
C) Jupiter
D) Mercury

Expected style:

\boxed{B}

Limitations

This model is specialized for English closed-book multiple-choice general knowledge. It is not a general chat assistant and should not be used as a reliable factual oracle outside the benchmark setting. Local diagnostics were useful for model selection but did not perfectly predict hidden-CI changes; hidden-CI accuracy remained tied at 0.4900 for the final refinements.