1 citations · 1 across the 6 of their papers we have counts for
7 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…
Localizing and Correcting Errors for LLM-based Planners
Aditya Kumar, William W. Cohen
Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning tasks. Our studies, as well as p…
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…
CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation
Jixuan Leng, Chengsong Huang, Langlin Huang +4
Existing reasoning evaluation frameworks for Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) predominantly assess either text-based reasoning or vision-langua…
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…
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations
Chung-Ching Chang, William W. Cohen, Yun-Hsuan Sung
We propose a theoretical framework for formulating language model decoder algorithms with dynamic programming and information theory. With dynamic programming, we lift the design o…
Trusted Source Alignment in Large Language Models
Vasilisa Bashlovkina, Zhaobin Kuang, Riley Matthews +4
Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propo…