activity
20202026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 572 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

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…

cs.AI2026

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…

cs.IR2026

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…

cs.CL2026

A Dynamic Self-Evolving Extraction System

Moin Amin-Naseri, Hannah Kim, Estevam Hruschka

The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-qu…

cs.AI2025

Mixed Signals: Decoding VLMs' Reasoning and Underlying Bias in Vision-Language Conflict

Pouya Pezeshkpour, Moin Aminnaseri, Estevam Hruschka

Vision-language models (VLMs) have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks. However, it is not clear ho…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…

cs.CL2020★ 7 cited

ParsiNLU: A Suite of Language Understanding Challenges for Persian

Daniel Khashabi, Arman Cohan, Siamak Shakeri +22

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich l…