2 citations · 2 across the 4 of their papers we have counts for
5 papers
Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data
Aditya Kumar, Cassandra A. Cohen, William W. Cohen
Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this…
Learning to Construct Practical Agentic Systems
Aditya Kumar, Zhihan Lei, Jerry Yan +6
Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns. However…
Semi-structured LLM Reasoners Can Be Rigorously Audited
Jixuan Leng, Cassandra A. Cohen, Zhixian Zhang +2
Although Large Language Models (LLMs) have become capable reasoners, the problem of faithfulness persists: their reasoning can contain errors and omissions that are difficult to de…
Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews
Mengqiao Liu, Tevin Wang, Cassandra A. Cohen +2
Which large language model (LLM) is better? Every evaluation tells a story, but what do users really think about current LLMs? This paper presents CLUE, an LLM-powered interviewer…
Watch Your Steps: Observable and Modular Chains of Thought
Cassandra A. Cohen, William W. Cohen
We propose a variant of chain of thought (CoT) prompting called Program Trace Prompting that makes explanations more observable while preserving the power, generality and flexibili…