SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention (2025.acl-long)
Copied to clipboard
| Challenge: | Content analysis is labor-intensive and time-consuming process that requires multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. |
| Approach: | They propose a multi-agent framework that effectively Simulates Content Analysis via Large language model (LLM) ag Ents. |
| Outcome: | The proposed framework achieves human-approximated performance across various content analysis tasks. |
Similar Papers
From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
Copied to clipboard
Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
| Approach: | They propose a framework that reframes language modeling as next-state prediction under interaction. |
| Outcome: | The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics . |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
Copied to clipboard
Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
Social Intelligence in the Age of LLMs (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are a powerful tool for integrating human-like communication and context-aware interactions into artificial systems. |
| Approach: | They propose to introduce and overview different aspects of artificial social intelligence and their relationship with LLMs by introducing scientific methods for evaluating social intelligence in LLM. |
| Outcome: | This tutorial will introduce scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. |
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)
Copied to clipboard
| Challenge: | Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications. |
| Approach: | They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs. |
| Outcome: | The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci. |
Can Intelligent Agents Revolutionize Scale Generation? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing measurement scales require extensive manual labor and require extensive validation and validation. |
| Approach: | They propose a multi-agent framework that automates scale development by leveraging collaborative AI agents. |
| Outcome: | The proposed framework automates scale development while maintaining rigorous quality standards. |
MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing systems rely heavily on literature retrieval and synthesis, resulting research lacking insight and creativity in social science. |
| Approach: | They propose a method that leverages highly realistic social simulations to the creativity of LLMs-generated research. |
| Outcome: | The proposed model shows a 6.81% improvement in quality over foundation LLMs and 17.19% gain in Insight over strong baselines. |
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments. |
| Approach: | They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. |
| Outcome: | The proposed framework can support simulations of 30,000 agents faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance increases linearly with the increase of LLM computational resources. |
Investigating and Extending Homans’ Social Exchange Theory with Large Language Model based Agents (2025.acl-long)
Copied to clipboard
| Challenge: | Social exchange theory (SET) is widely recognized as a basic framework for understanding human interactions and interactions. |
| Approach: | They propose to use large language models to study Homans’ social exchange theory (SET) by constructing a virtual society composed of three LLM agents and having them engage in a social exchange game to observe their behaviors. |
| Outcome: | The proposed model extends Homans’ SET with LLM-based agents and demonstrates consistency between the agent and human behavior. |
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
Copied to clipboard
| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |