1 citations · 1 across the 5 of their papers we have counts for
5 papers
ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program Synthesis
Atharva Naik, Yash Mathur, Prakam +2
LLMs can solve program synthesis tasks but remain inefficient and unreliable on hard instances requiring large combinatorial search. Given a small set of reasoning traces, we use c…
GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models
Yangyue Wang, Harshvardhan Sikka, Yash Mathur +3
GUI grounding models report over 85% accuracy on standard benchmarks, yet drop 27-56 percentage points when instructions require spatial reasoning rather than direct element naming…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
What Makes Digital Support Effective? How Therapeutic Skills Affect Clinical Well-Being
Anna Fang, Wenjie Yang, Raj Sanjay Shah +4
Online mental health support communities have grown in recent years for providing accessible mental and emotional health support through volunteer counselors. Despite millions of p…
Program-Aided Reasoners (better) Know What They Know
Anubha Kabra, Sanketh Rangreji, Yash Mathur +3
Prior work shows that program-aided reasoning, in which large language models (LLMs) are combined with programs written in programming languages such as Python, can significantly i…