2 citations · 2 across the 3 of their papers we have counts for
8 papers
Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +7
Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seaso…
Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation
Xiaoxin Lu, Ranran Haoran Zhang, Yusen Zhang +1
People get informed of a daily task plan through diverse media involving both texts and images. However, most prior research only focuses on LLM's capability of textual plan genera…
Efficient PRM Training Data Synthesis via Formal Verification
Ryo Kamoi, Yusen Zhang, Nan Zhang +4
Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over reasoning traces. However, existing…
GREATERPROMPT: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang +1
LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significan…
Verbosity Veracity: Demystify Verbosity Compensation Behavior of Large Language Models
Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang
Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as halluc…
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang +3
The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range…