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Chandler Smith

3 accepted papers

2025

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

NeurIPS 2025poster

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing eval…

Cited by 0SourceScholar
2024

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices

NeurIPS 2024spotlight

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundati…

Cited by 19SourcePDFScholar
2024

Evaluating Language Model Character Traits

EMNLP 2024finding

Language models (LMs) can exhibit human-like behaviour, but it is unclear how to describe this behaviour without undue anthropomorphism. We formalise a behaviourist view of LM character traits: qualities such as truthfulness, sycophancy, and coherent beliefs and intentions, which may manifest as con…