565 citations · 621 across the 33 of their papers we have counts for
8 papers · 1 filter
Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents
Brendan King, Farima Fatahi Bayat, Jean-Flavien Bussotti +2
When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confidence in the agent's success.…
MAWILE: Multi-Axis Workbench for Inspecting LLM Evaluators
Jackson Hassell, Farima Fatahi Bayat, Pouya Pezeshkpour +1
Large language model (LLM) judges provide a flexible and scalable method for evaluating model and agent outputs, but their verdicts can be sensitive to incidental changes in the ev…
Reflective Prompt Tuning through Language Model Function-Calling
Farima Fatahi Bayat, Moin Aminnaseri, Pouya Pezeshkpour +1
Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without par…
AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
Pouya Pezeshkpour, Estevam Hruschka
Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current verifiers face a fundamental…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
Align Then Adapt: Label-Efficient Adapter Learning for Asymmetric Dense Retrieval
Seiji Maekawa, Moin Aminnaseri, Pouya Pezeshkpour +1
Dense retrieval systems increasingly face an asymmetry between complex instruction-like queries and relatively simple, static document collections. While stronger embedders can bet…
From Task Solving to Robust Real-World Adaptation in LLM Agents
Pouya Pezeshkpour, Estevam Hruschka
Large language models are increasingly deployed as specialized agents that plan, call tools, and take actions over extended horizons. Yet many existing evaluations assume a "clean…
Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models
Arash Gholami Davoodi, Navid Rezazadeh, Seyed Pouyan Mousavi Davoudi +1
Large language models (LLMs) must balance diversity and creativity against logical coherence in open-ended generation. Existing truncation-based samplers are effective but largely…