activity
20212025
most citedCoarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2025

MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments

Darshan Deshpande, Varun Gangal, Hersh Mehta +3

Recent works on context and memory benchmarking have primarily focused on conversational instances but the need for evaluating memory in dynamic enterprise environments is crucial…

cs.AI20251 cited

TRAIL: Trace Reasoning and Agentic Issue Localization

Darshan Deshpande, Varun Gangal, Hersh Mehta +3

The increasing adoption of agentic workflows across diverse domains brings a critical need to scalably and systematically evaluate the complex traces these systems generate. Curren…

cs.CL2024

Sparse Rewards Can Self-Train Dialogue Agents

Barrett Martin Lattimer, Varun Gangal, Ryan McDonald +1

Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents, especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning and…

cs.CL2022

EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation

Sedrick Scott Keh, Rohit K. Bharadwaj, Emmy Liu +3

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an…

cs.CL2021

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

Harsh Jhamtani, Varun Gangal, Eduard Hovy +1

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to h…

cs.CL20211 cited

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

Dheeraj Mekala, Varun Gangal, Jingbo Shang

Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples…