3 citations · 5 across the 10 of their papers we have counts for
11 papers
SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents
Vivek Kulkarni, Sudipta Paul, Aounon Kumar +2
Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over time. Recently, methods like…
Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering
Aounon Kumar, Sudipta Paul, Vivek Kulkarni +2
The effective use of search engines by large language models (LLMs) remains a significant challenge, particularly in complex, multi-hop question-answering (MHQA) tasks. These tasks…
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards…
TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory
Tianyu Yang, Sudipta Paul, Vijay Srinivasan +2
Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. Existing memory agents active…
From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media
Maria Ryskina, Matthew R. Gormley, Kyle Mahowald +3
Living languages are shaped by a host of conflicting internal and external evolutionary pressures. While some of these pressures are universal across languages and cultures, others…
VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
Avinash Amballa, Yashas Malur Saidutta, Chi-Heng Lin +2
Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that suc…
APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
Artem Chernodub, Aman Saini, Yejin Huh +2
Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. C…
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
Aman Saini, Artem Chernodub, Vipul Raheja +1
We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-onl…
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Gourab Dey, Adithya V Ganesan, Yash Kumar Lal +6
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of trai…
Personalized Text Generation with Fine-Grained Linguistic Control
Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar +1
As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to m…
mEdIT: Multilingual Text Editing via Instruction Tuning
Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni +2
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lin…