activity
20222025
most citedReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents

3 citations · 6 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2025★ 1 cited

TD-EVAL: Revisiting Task-Oriented Dialogue Evaluation by Combining Turn-Level Precision with Dialogue-Level Comparisons

Emre Can Acikgoz, Carl Guo, Suvodip Dey +4

Task-oriented dialogue (TOD) systems are experiencing a revolution driven by Large Language Models (LLMs), yet the evaluation methodologies for these systems remain insufficient fo…

cs.CL2025★ 1 cited

Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems

Mert İnan, Anthony Sicilia, Suvodip Dey +6

While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems.…

cs.CL2025★ 1 cited

BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response Generation

Suvodip Dey, Maunendra Sankar Desarkar

The standard language modeling (LM) loss by itself has been shown to be inadequate for effective dialogue modeling. As a result, various training approaches, such as auxiliary loss…

cs.CL2025

Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling

Suvodip Dey, Yi-Jyun Sun, Gokhan Tur +1

Recent LLMs have enabled significant advancements for conversational agents. However, they are also well known to hallucinate, producing responses that seem plausible but are factu…

cs.CL2024★ 3 cited

ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents

Vardhan Dongre, Xiaocheng Yang, Emre Can Acikgoz +3

Large language model (LLM)-based agents are increasingly employed to interact with external environments (e.g., games, APIs, world models) to solve user-provided tasks. However, cu…

cs.CL2024

Confidence Estimation for LLM-Based Dialogue State Tracking

Yi-Jyun Sun, Suvodip Dey, Dilek Hakkani-Tur +1

Estimation of a model's confidence on its outputs is critical for Conversational AI systems based on large language models (LLMs), especially for reducing hallucination and prevent…

cs.CL2022

On Text Style Transfer via Style Masked Language Models

Sharan Narasimhan, Pooja Shekar, Suvodip Dey +1

Text Style Transfer (TST) is performable through approaches such as latent space disentanglement, cycle-consistency losses, prototype editing etc. The prototype editing approach, w…

cs.CL2022

DialoGen: Generalized Long-Range Context Representation for Dialogue Systems

Suvodip Dey, Maunendra Sankar Desarkar, Asif Ekbal +1

Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last- utt…

cs.CL2022

Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances

Suvodip Dey, Ramamohan Kummara, Maunendra Sankar Desarkar

Dialogue State Tracking (DST) is primarily evaluated using Joint Goal Accuracy (JGA) defined as the fraction of turns where the ground-truth dialogue state exactly matches the pred…