Large language models (LLMs) are expected to respond accurately but often exhibit
deficient reasoning or generate hallucinatory content. To address these, studies prefixed …

Large language model (LLM) performance on reasoning problems typically does not
generalize out of distribution. Previous work has claimed that this can be mitigated by …

The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps
common in everyday communication found in their pre-training data: underlying rationales …

Abstract Large Language Models (LLMs) have taken the field of AI by storm, but their
adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work …

Mainstream LLM research has primarily focused on enhancing their generative capabilities.
However, even the most advanced LLMs experience uncertainty in their outputs, often …

While Vision-Language Models (VLMs) have shown remarkable abilities in visual and
language reasoning tasks, they invariably generate flawed responses. Self-correction that …

Meeting summarization has become a critical task since digital encounters have become a
common practice. Large language models (LLMs) show great potential in summarization …

AI systems make decisions in physical environments through primitive actions or
affordances that are accessed via API calls. While deploying AI agents in the real world …

We address the challenge of online Reinforcement Learning from Human Feedback (RLHF)
with a focus on self-rewarding alignment methods. In online RLHF, obtaining feedback …

Recent studies show that LLMs, particularly open-source models, struggle to follow complex
instructions with multiple constraints. Despite the importance, methods to improve LLMs' …