Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and
MBPP, but struggle with handling entire code repositories. This challenge has prompted …

Large Language Models (LLMs) have become instrumental across various applications,
with the customization of these models to specific scenarios becoming increasingly critical …

Self-critic has become a crucial mechanism for enhancing the reasoning performance of
LLMs. However, current approaches mainly involve basic prompts for intuitive instance-level …

Scientific literature understanding is crucial for extracting targeted information and garnering
insights, thereby significantly advancing scientific discovery. Despite the remarkable …

Critique ability, a meta-cognitive capability of humans, presents significant challenges for
LLMs to improve. Recent works primarily rely on supervised fine-tuning (SFT) using critiques …

Throughout its lifecycle, a large language model (LLM) generates a substantially larger
carbon footprint during inference than training. LLM inference requests vary in batch size …

With the rapid advancement of large language models (LLMs), aligning policy models with
human preferences has become increasingly critical. Direct Preference Optimization (DPO) …

Preference learning is a central component for aligning current LLMs, but this process can
be vulnerable to data poisoning attacks. To address this concern, we introduce …

With the rapid growth of Large Language Models (LLMs) across various domains, numerous
new LLMs have emerged, each possessing domain-specific expertise. This proliferation has …

Automated alignment develops alignment systems with minimal human intervention. The
key to automated alignment lies in providing learnable and accurate preference signals for …