17 papers · 1 filter
Improving LLMs via Validator-to-Generator Alignment
Juan Diego Rodriguez, Jocelyn Zhang, Katrin Erk +1
Large language models are inconsistent: varying prompts or including unrelated information can lead to unexpected changes in model outputs. The generator-validator (G-V) gap is one…
Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning
Liyan Tang, Fangcong Yin, Greg Durrett
Large vision-language models can reason over multimodal inputs by generating textual chains of thought (CoT). A key capability exhibited in CoT reasoning is self-reflection: revisi…
Randomized YaRN Improves Length Generalization for Long-Context Reasoning
Manas Mehta, Fangcong Yin, Greg Durrett
Large language models (LLMs) are typically pretrained on short sequences and then extended to work on longer sequences with additional training. However, such LLMs still struggle t…
GENIE: A Fine-Grained Measure for Novelty
Ramya Namuduri, Manya Wadhwa, Anshun Asher Zheng +2
Large Language Models have consistently demonstrated a lack of creativity and diversity across tasks. Prior work has focused on addressing whether models are capable of generating…
Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents
Wenxuan Ding, Nicholas Tomlin, Greg Durrett
LLM agents are deployed in environments where they must interact to acquire information. In these scenarios, the agent must reason about inherent cost-uncertainty tradeoffs in how…
CREATE: Testing LLMs for Associative Creativity
Manya Wadhwa, Tiasa Singha Roy, Harvey Lederman +2
A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate…