output
20122025
most citedThe CoNLL-2013 Shared Task on Grammatical Error Correction

146 citations

Showing 2025 · cs.CLShow all

7 papers · 2 filters

cs.CL2025

Lucy: edgerunning agentic web search on mobile with machine generated task vectors

Alan Dao, Dinh Bach Vu, Alex Nguyen +1

Small language models (SLMs) are inherently limited in knowledge-intensive tasks due to their constrained capacity. While test-time computation offers a path to enhanced performanc…

cs.CL2025

Jan-nano Technical Report

Alan Dao, Dinh Bach Vu

Most language models face a fundamental tradeoff where powerful capabilities require substantial computational resources. We shatter this constraint with Jan-nano, a 4B parameter l…

cs.CL2025★ 146 cited

The CoNLL-2013 Shared Task on Grammatical Error Correction

Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu +2

The CoNLL-2013 shared task was devoted to grammatical error correction. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and sc…

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.CL2025

ReZero: Enhancing LLM search ability by trying one-more-time

Alan Dao, Thinh Le

Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM) performance on knowledge-intensive tasks but depends heavily on initial search query quality. Current metho…

cs.CL2025

AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and Symbolic Reasoning

Alan Dao, Dinh Bach Vu, Bui Quang Huy

This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of language models for robotic manipulation in 3D Cartesian space. AlphaS…

cs.CL2025★ 1 cited

AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO

Alan Dao, Dinh Bach Vu

Large Language Models (LLMs) have demonstrated impressive capabilities in language processing, yet they often struggle with tasks requiring genuine visual spatial reasoning. In thi…