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20202026
most citedTowards Zero-Label Language Learning

46 citations · 117 across the 54 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

When Metrics Disagree: Automatic Similarity vs. LLM-as-a-Judge for Clinical Dialogue Evaluation

Bian Sun, Zhenjian Wang, Orvill de la Torre +1

As Large Language Models (LLMs) are increasingly integrated into healthcare to address complex inquiries, ensuring their reliability remains a critical challenge. Recent studies ha…

cs.CL2025

Cognitive Alignment in Personality Reasoning: Leveraging Prototype Theory for MBTI Inference

Haoyuan Li, Yuanbo Tong, Yuchen Li +3

Personality recognition from text is typically cast as hard-label classification, which obscures the graded, prototype-like nature of human personality judgments. We present ProtoM…

cs.CL2025

Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning

Bowen Jin, TJ Collins, Donghan Yu +10

Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialize…

cs.CL2025

Small Language Models for Emergency Departments Decision Support: A Benchmark Study

Zirui Wang, Jiajun Wu, Braden Teitge +2

Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and hig…

cs.CL2025

MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use

Zijian Wu, Xiangyan Liu, Xinyuan Zhang +12

MCP standardizes how LLMs interact with external systems, forming the foundation for general agents. However, existing MCP benchmarks remain narrow in scope: they focus on read-hea…

cs.CL2025

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description

Shaoming Duan, Zirui Wang, Chuanyi Liu +5

Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch betwee…