4 citations · 17 across the 9 of their papers we have counts for
9 papers
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Peter Jansen, Oyvind Tafjord, Marissa Radensky +6
Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they…
SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
Yuling Gu, Oyvind Tafjord, Hyunwoo Kim +4
Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) - the ability to attribute mental states to oneself and others. Yet most evaluations stop at expli…
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Peter Jansen, Marc-Alexandre Côté, Tushar Khot +5
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reason…
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Nathaniel Weir, Kate Sanders, Orion Weller +8
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on br…
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra +3
Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM ac…
BaRDa: A Belief and Reasoning Dataset that Separates Factual Accuracy and Reasoning Ability
Peter Clark, Bhavana Dalvi Mishra, Oyvind Tafjord
While there are numerous benchmarks comparing the performance of modern language models (LMs), end-task evaluations often conflate notions of *factual accuracy* ("truth") and *reas…
Digital Socrates: Evaluating LLMs through Explanation Critiques
Yuling Gu, Oyvind Tafjord, Peter Clark
While LLMs can provide reasoned explanations along with their answers, the nature and quality of those explanations are still poorly understood. In response, our goal is to define…
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen +5
Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, witho…
Lila: A Unified Benchmark for Mathematical Reasoning
Swaroop Mishra, Matthew Finlayson, Pan Lu +8
Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI…