ALF: A Fine-Grained French Analogical Dataset for Evaluating Lexical Knowledge of Large Language Models
Bibliographic record
Abstract
The undeniable revolution brought forth by Large Language Models (LLMs) stems from the amazing fluency of the texts they generate, mastering language with seemingly human-like finesse. This fluency raises a key scientific question: How much lexical knowledge do LLMs actually capture in order to produce such fluent language? To address this, we present ALF, a freely available, analogical dataset endowed with rich lexicographic information grounded in Meaning-Text Theory for the French language. It comprises 2600 fine-grained lexical analogies with which we evaluate the lexical ability of five off-the-shelf LLMs, namely ChatGPT-4o mini, Llama3.0-8B, Llama3.1-8B, Qwen2.5-14B, and Mistral7B. Their performance spans from 45% for Mistral, through about 55% for the ChatGPT and Llama models, and up to nearly 60% for Qwen2.5-14B, thus qualifying ALF as a challenging dataset. Experimenting with larger models (OpenAI o1, Llama3.0/3.1-70B, and Qwen2.5-32B) yields rather limited returns considering the drastic increase in computational cost. We further identify certain types of analogies and prompting methods that reveal performance disparities.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".