Abstract Large Language Models (LLMs) have recently gained significant attention due to
their remarkable capabilities in performing diverse tasks across various domains. However …

Although model editing has shown promise in revising knowledge in Large Language
Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this …

Position embedding is a core component of current Large Language Models (LLMs). Rotary
position embedding (RoPE), a technique that encodes the position information with a …

Extending context window sizes allows large language models (LLMs) to process longer
sequences and handle more complex tasks. Rotary Positional Embedding (RoPE) has …

Handling long-context inputs is crucial for large language models (LLMs) in tasks such as
extended conversations, document summarization, and many-shot in-context learning …

Capturing the unique knowledge demands for each dialogue context plays a crucial role in
commonsense knowledge-grounded response generation. However, current CoT-based …

With the rapid development of large language models (LLM), the evaluation of LLM
becomes increasingly important. Measuring text generation tasks such as summarization …

Position embedding is a core component of current Large Language Models (LLMs). Rotary
position embedding (RoPE), a technique that encodes the position information with a …

Numerous recent works target to extend effective context length for language models and
various methods, tasks and benchmarks exist to measure model's effective memorization …

Large Language Models (LLMs) have shown impressive proficiency across a range of
natural language processing tasks yet remain vulnerable to adversarial prompts, known as …