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Works17 from public data
- TinyLlama: An Open-Source Small Language Model934
TinyLlama is presented, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs that significantly outperforms existing open-source language models with comparable sizes.
- Long Context Transfer from Language to Vision590
To effectively measure LMMs' ability to generalize to long contexts in the vision modality, V-NIAH (Visual Needle-In-A-Haystack), a purely synthetic long vision benchmark inspired by the language model's NIAH test is developed.
- RegMix: Data Mixture as Regression for Language Model Pre-training196
RegMix is proposed to automatically identify a high-performing data mixture by formulating it as a regression task and consistently outperforms human selection in experiments involving models up to 7B models trained on 100B tokens, while matching or exceeding DoReMi using just 10% of the computational resources.
- MedDialog: Large-scale Medical Dialogue Datasets173
It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation.
- Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search58
This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (i.e., an extended reasoning process with self-reflection and self-exploration of new strategies) and proposes the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm.
- On the Generation of Medical Dialogues for COVID-1932
This work collects two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19 and trains several dialogue generation models based on Transformer, GPT, and BERT-GPT to develop a medical dialogue system that can provide COVID19-related consultations.
- On the Generation of Medical Dialogs for COVID-1929
A multi-task learning approach is developed, which regularizes the data-deficient dialog generation task with a masked token prediction task, and shows that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct.
- BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems17
BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems, aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way and is designed to be both modular and extensible.
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- BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization5
This work proposes structuring SWE agents as orchestrators coordinating specialized sub-agents for sub-tasks such as localization, editing, and validation, and demonstrates that automatically discovered hierarchical multi-agent systems significantly improve generalization on challenging long-horizon SWE tasks.
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- SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages4
SailCompass is introduced, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA) and it is derived that SEA-specialized LLMs still outperform general LLMs, although the gap has narrowed.
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- SW-$A^2$-Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web–
This paper strictly defines the A2A-Agentization process, decomposing it into critical stages and identifying key technical hurdles on top of the A2A protocol, and develops an Agentization Agent to agentize digital assets for the Agentic Web.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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