Papers by Zishan Guo

5 papers
Exploring Multilingual Concepts of Human Values in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages? (2024.findings-emnlp)

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Challenge: Prior research has revealed that certain abstract concepts are linearly represented as directions in the representation space of LLMs, predominantly centered around English.
Approach: They extend previous research that shows certain abstract concepts are linearly represented as directions in LLMs, predominantly centered around English.
Outcome: The proposed model can be used to align LLMs with human values, and it can generate toxic, untruthful, biased, and even illegal content.
CToolEval: A Chinese Benchmark for LLM-Powered Agent Evaluation in Real-World API Interactions (2024.findings-acl)

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Challenge: a benchmark is designed to evaluate the capabilities of large language models (LLMs) as agents in decision making and operational tasks.
Approach: They propose a benchmark to evaluate LLMs in the context of Chinese societal applications . they propose he benchmark will evaluate tool invocation ability of LLM and task completion ability .
Outcome: The proposed benchmark features 398 APIs across 27 widely-used Apps across 14 domains.
CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types (2023.emnlp-main)

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Challenge: Existing datasets focus on a single type of spoken style, such as disfluencies.
Approach: They propose a Chinese Spoken-to-Written style conversion dataset with 7,237 spoken sentences extracted from transcribed conversational texts.
Outcome: The proposed dataset covers four major conversion problems corresponding to the majority of spoken styles.
RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction (2026.findings-acl)

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Challenge: Existing benchmarks focus on casual conversation or task-oriented dialogue, failing to capture “long-term project-oriented” interactions where agents must track evolving goals.
Approach: They propose a benchmark that simulates the dynamic evolution of memory in real-world projects.
Outcome: The proposed benchmarks simulate the dynamic evolution of memory in real-world projects.
ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web (2026.acl-long)

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Challenge: Existing routers that use hardcoded tools are limited by scalability and generality bottlenecks.
Approach: They propose a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems.
Outcome: The proposed pipeline can train routers with dynamic context understanding to create the plug-and-play Light Routing Agent.

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