4 accepted papers
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of controllability** remains a key challenge, often leading to unfocused conversations or task failure. To address this, we…
Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost. Recent advancements extend the context window by adjusting the scaling factors of RoPE and fine-tuning. However, suboptimal initialization of these factors resu
The widespread deployment of large language models (LLMs) across various domains has made their safety a critical priority. Inspired by think-tank decision-making philosophy, we propose DiplomacyAgent, an LLM-based multi-agent system for diplomatic position analysis. With DiplomacyAgent, we are able
With the increasing capability of large language models (LLMs), LLM-as-a-judge has emerged as a new evaluation paradigm. Compared with traditional automatic and manual evaluation, LLM evaluators exhibit better interpretability and efficiency. Despite this, existing LLM evaluators suffer from limited…