9 citations · 20 across the 11 of their papers we have counts for
11 papers
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Shayan Talaei, Abhinav Chinta, Devvrit Khatri +3
Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be intro…
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
When to Trust Context: Self-Reflective Debates for Context Reliability
Zeqi Zhou, Fang Wu, Shayan Talaei +5
Large language models frequently encounter conflicts between their parametric knowledge and contextual input, often resulting in factual inconsistencies or hallucinations. We propo…
StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Shayan Talaei, Meijin Li, Kanu Grover +3
Every individual carries a unique and personal life story shaped by their memories and experiences. However, these memories are often scattered and difficult to organize into a coh…
SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models
Emil Biju, Shayan Talaei, Zhemin Huang +3
Large reasoning models (LRMs) excel at complex reasoning tasks but typically generate lengthy sequential chains-of-thought, resulting in long inference times before arriving at the…
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
Mohammadreza Pourreza, Shayan Talaei, Ruoxi Sun +5
Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query fo…
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
Mohammadreza Pourreza, Hailong Li, Ruoxi Sun +7
In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-t…
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg +3
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than opt…
CHESS: Contextual Harnessing for Efficient SQL Synthesis
Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang +2
Translating natural language questions into SQL queries, known as text-to-SQL, is a long-standing research problem. Effective text-to-SQL synthesis can become very challenging due…
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
Matin Ansaripour, Shayan Talaei, Giorgi Nadiradze +1
Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based metho…
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze +1
Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL a…