Confidence estimation aiming to evaluate output trustability is crucial for the application of
large language models (LLM), especially the black-box ones. Existing confidence estimation …

Large Language Models exhibit robust problem-solving capabilities for diverse tasks.
However, most LLM-based agents are designed as specific task solvers with sophisticated …

Although most current large multimodal models (LMMs) can already understand photos of
natural scenes and portraits, their understanding of abstract images, eg, charts, maps, or …

Large Language Models (LLMs) are often described as being instances of foundation
models-that is, models that transfer strongly across various tasks and conditions in few-show …

Self-consistency (SC), leveraging multiple samples from LLMs, shows significant gains on
various reasoning tasks but struggles with free-form generation due to the difficulty of …

Reinforcement learning (RL) has become the de facto standard practice for sequential
decision-making problems by improving future acting policies with feedback. However, RL …

Text-to-SQL is an important task that helps people obtain information from databases by
automatically generating SQL queries. Considering the brilliant performance, approaches …

Large Language Models (LLMs) are revolutionizing the field of code development by
leveraging their deep understanding of code patterns, syntax, and semantics to assist …

This work studies mitigating fact-conflicting hallucinations for large language model (LLM) at
inference time. Particularly, we propose a self-endorsement framework that leverages the …

Requiring a Large Language Model to generate intermediary reasoning steps has been
shown to be an effective way of boosting performance. In fact, it has been found that …