7 papers
LIFT: Last-Mile Fine-Tuning for Table Explicitation
Divij Khaitan, Ashish Tiwari
We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard text, and a fine-tuned small…
IndiMathBench: Autoformalizing Mathematical Reasoning Problems with a Human Touch
Param Biyani, Shashank Kirtania, Yasharth Bajpai +2
Reliable autoformalization remains challenging even in the era of large language models (LLMs). The scarcity of high-quality training data is a major bottleneck. Expert annotation…
ConDABench: Interactive Evaluation of Language Models for Data Analysis
Avik Dutta, Priyanshu Gupta, Hosein Hasanbeig +6
Real-world data analysis tasks often come with under-specified goals and unclean data. User interaction is necessary to understand and disambiguate a user's intent, and hence, esse…
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
Jenny T. Liang, Aayush Kumar, Yasharth Bajpai +7
Spreadsheet programming is challenging. Programmers use spreadsheet programming knowledge (e.g., formulas) and problem-solving skills to combine actions into complex tasks. Advance…
TEN: Table Explicitization, Neurosymbolically
Nikita Mehrotra, Aayush Kumar, Sumit Gulwani +2
We present a neurosymbolic approach, TEN, for extracting tabular data from semistructured input text. This task is particularly challenging for text input that does not use special…
LLM-Guided Compositional Program Synthesis
Ruhma Khan, Sumit Gulwani, Vu Le +3
Program synthesis from input-output examples, also called programming by example (PBE), has had tremendous impact on automating end-user tasks. Large language models (LLMs) have th…