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Found 9 papers, 5 papers with code
FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in Large Language Models
Fuzzy reasoning is vital due to the frequent use of imprecise information in daily contexts.
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions.
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
The student learners are trained by the main model and, in turn, provide feedback to help the main model capture more generalizable and imitable correlations.
Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
Generalized quantifiers (e. g., few, most) are used to indicate the proportions predicates are satisfied (for example, some apples are red).
SPE: Symmetrical Prompt Enhancement for Fact Probing
In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.
Adversarial Scrubbing of Demographic Information for Text Classification
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.
Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages
In this work, we design a knowledge-base and prediction model embedded system for spelling correction in low-resource languages.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
Towards Minimal Supervision BERT-based Grammar Error Correction
Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings.
