activity
20172025
most citedData Decisions and Theoretical Implications when Adversarially Learning Fair Representations

299 citations · 983 across the 34 of their papers we have counts for

collaborators

35 papers

cs.AI2025★ 1 cited

Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI

Jitao Sang, Jinlin Xiao, Jiarun Han +5

The rapid evolution of agentic AI marks a new phase in artificial intelligence, where Large Language Models (LLMs) no longer merely respond but act, reason, and adapt. This survey…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2024

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai

Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1

We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…

cs.CL2024

Mitigating Extrinsic Gender Bias for Bangla Classification Tasks

Sajib Kumar Saha Joy, Arman Hassan Mahy, Meherin Sultana +4

In this study, we investigate extrinsic gender bias in Bangla pretrained language models, a largely underexplored area in low-resource languages. To assess this bias, we construct…

cs.CL2024★ 1 cited

Beyond Performance: Quantifying and Mitigating Label Bias in LLMs

Yuval Reif, Roy Schwartz

Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…