Papers by Jakub Szymanik
Teaching Small Language Models to Learn Logic through Meta-Learning (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models are increasingly evaluated on reasoning tasks, yet their logical abilities remain contested. |
| Approach: | They propose to apply few-shot meta-learning to large language models' reasoning domain to enable them to acquire abstract inference patterns that generalize to novel structures. |
| Outcome: | The proposed model outperforms GPT-4o and o3-mini on a syllogistic reasoning task. |
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish (2022.lrec-1)
Copied to clipboard
| Challenge: | a tool for automatic marking up of quantifiers is proposed for Polish . it is trained on a recently annotated corpus of Polish quantificational expressions . |
| Approach: | They propose to use a BERT based neural model to mark up quantifiers in text . they analyse a manually annotated corpus of Polish quantificational expressions and compare it to a human annotation model. |
| Outcome: | The proposed model can be used to build semantically annotated quantifier corpora for other languages. |
Testing the limits of logical reasoning in neural and hybrid models (2024.findings-naacl)
Copied to clipboard
| Challenge: | despite the successes of deep learning models, we still need to know more about how and what they learn. |
| Approach: | They create tests to analyze logical reasoning patterns in neural and hybrid models . they find that models can generalize logical thinking only to a limited degree . |
| Outcome: | The proposed models can capture elementary aspects of meaning but only to limited extent . authors say they need to understand how and what they learn . |
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers (P18-2)
Copied to clipboard
| Challenge: | cloze deletion test is a test that requires the learner to understand the context and vocabulary in order to identify the correct word. |
| Approach: | They collect data from human participants and test various models in a local and a global context condition to examine the role of linguistic context in predicting quantifiers. |
| Outcome: | The proposed models outperform humans in a local and global context and are only slightly better in the latter. |
Language Models Use Monotonicity to Assess NPI Licensing (2021.findings-acl)
Copied to clipboard
| Challenge: | Neural language models (LMs) have become powerful approximators of human language . fewer studies have been done on what kind of formal semantic features are encoded by LMs . |
| Approach: | They propose a series of experiments that investigate the semantic knowledge of language models . they use diagnostic classifiers, linguistic acceptability tasks and a ranking method to investigate the models' inner workings. |
| Outcome: | The proposed method can be applied to LMs trained on filtered corpora and gain stronger insights into their generalizations. |