1 citations · 1 across the 4 of their papers we have counts for
6 papers
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
Arkajyoti Chakraborty, Adam Zernik, Ishika Kulkarni +6
Task-oriented conversational agents remain fragile under realistic conditions, especially when users exhibit non-cooperative behavior. Existing function-calling benchmarks underrep…
ToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation
Arkajyoti Chakraborty, Aryan Tayal, Ishika Agarwal +5
Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when users exhibit non-cooperative…
LLMs Get Smarter from Targeted Synthetic Multilingual Data
Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen +2
Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other words, a language model output…
When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents
Mrinal Rawat, Arkajyoti Chakraborty, Neha Gupta +1
Supervised fine-tuning (SFT) has emerged as one of the most effective ways to improve the performance of large language models (LLMs) in downstream tasks. However, SFT can have dif…
DySK-Attn: A Framework for Efficient, Real-Time Knowledge Updating in Large Language Models via Dynamic Sparse Knowledge Attention
Kabir Khan, Priya Sharma, Arjun Mehta +2
Large Language Models (LLMs) suffer from a critical limitation: their knowledge is static and quickly becomes outdated. Retraining these massive models is computationally prohibiti…
Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
Mrinal Rawat, Ambuje Gupta, Rushil Goomer +3
The ReAct (Reasoning + Action) capability in large language models (LLMs) has become the foundation of modern agentic systems. Recent LLMs, such as DeepSeek-R1 and OpenAI o1/o3, ex…