Multi-modal and multi-agent systems meet rationality: A survey
Rationality is characterized by logical thinking and decision-making that align with evidence
and logical rules. This quality is essential for effective problem-solving, as it ensures that …
and logical rules. This quality is essential for effective problem-solving, as it ensures that …
On memorization of large language models in logical reasoning
Large language models (LLMs) achieve good performance on challenging reasoning
benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is …
benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is …
A survey on knowledge-enhanced multimodal learning
M Lymperaiou, G Stamou - Artificial Intelligence Review, 2024 - Springer
Multimodal learning has been a field of increasing interest, aiming to combine various
modalities in a single joint representation. Especially in the area of visiolinguistic (VL) …
modalities in a single joint representation. Especially in the area of visiolinguistic (VL) …
Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval
performance. Unfortunately, they have also displayed a higher degree of sensitivity to …
performance. Unfortunately, they have also displayed a higher degree of sensitivity to …
Causal language modeling can elicit search and reasoning capabilities on logic puzzles
Causal language modeling using the Transformer architecture has yielded remarkable
capabilities in Large Language Models (LLMs) over the last few years. However, the extent …
capabilities in Large Language Models (LLMs) over the last few years. However, the extent …
Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of Large Language Models (LLMs) has demonstrated remarkable
progress in complex reasoning tasks. However, a significant discrepancy persists between …
progress in complex reasoning tasks. However, a significant discrepancy persists between …
Enhancing adversarial robustness in Natural Language Inference using explanations
A Koulakos, M Lymperaiou, G Filandrianos… - arXiv preprint arXiv …, 2024 - arxiv.org
The surge of state-of-the-art Transformer-based models has undoubtedly pushed the limits
of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the …
of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the …
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs
The rise of large language models (LLMs) has created a need for advanced benchmarking
systems beyond traditional setups. To this end, we introduce QUENCH, a novel text-based …
systems beyond traditional setups. To this end, we introduce QUENCH, a novel text-based …
RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation
I Panagiotopoulos, G Filandrianos… - arXiv preprint arXiv …, 2024 - arxiv.org
Riddle-solving requires advanced reasoning skills, pushing LLMs to engage in abstract
thinking and creative problem-solving, often revealing limitations in their cognitive abilities …
thinking and creative problem-solving, often revealing limitations in their cognitive abilities …
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good
domain to evaluate the reasoning capability of a model which can then guide us to improve …
domain to evaluate the reasoning capability of a model which can then guide us to improve …