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Lizhou Fan

4 accepted papers

2025

Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities

ACL 2025finding

This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from a comprehensive set of domains. We explore whether LLMs demonstrate genuine reasoning capabilities across various domain…

2025

EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

NAACL 2025long

Cloud-based Large Language Models (LLMs) such as ChatGPT have become increasingly integral to daily operations. Nevertheless, they also introduce privacy concerns: firstly, numerous studies underscore the risks to user privacy posed by jailbreaking cloud-based LLMs; secondly, the LLM service provide…

2024

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis

EMNLP 2024system demonstrations

This paper presents BattleAgent, a detailed emulation demonstration system that combines the Large Vision-Language Model (VLM) and Multi-Agent System (MAS). This novel system aims to emulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a…

2024

NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

ACL 2024long

Complex reasoning ability is one of the most important features of Large Language Models (LLMs). Numerous benchmarks have been established to assess the reasoning abilities of LLMs. However, they are inadequate in offering a rigorous evaluation and prone to the risk of overfitting, as these publicly…