Search Results for author:
Found 16 papers, 12 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especially when model parameters are less than 100B.
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks.
SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA).
Scaling up Masked Diffusion Models on Text
Masked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as text generation and language understanding, remain underexplored.
EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
The code solution with the lowest execution time and memory consumption is selected as the final output for each task.
RegMix: Data Mixture as Regression for Language Model Pre-training
RegMix trains many small models on diverse data mixtures, uses regression to predict performance of unseen mixtures, and applies the best predicted mixture to train a large-scale model with orders of magnitude more compute.
Long Context Transfer from Language to Vision
By simply extrapolating the context length of the language backbone, we enable LMMs to comprehend orders of magnitude more visual tokens without any video training.
Ranked #8 on
Video Question Answering
on OVBench
MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation
Our study analyzed two common benchmarks, HumanEval and MBPP, and found that these might not thoroughly evaluate LLMs' code generation capacities due to limitations in quality, difficulty, and granularity.
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
TinyLlama: An Open-Source Small Language Model
We present TinyLlama, a compact 1. 1B language model pretrained on around 1 trillion tokens for approximately 3 epochs.
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models
We show that MechanisticProbe is able to detect the information of the reasoning tree from the model's attentions for most examples, suggesting that the LM indeed is going through a process of multi-step reasoning within its architecture in many cases.
One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning
Fine-tuning pre-trained language models for multiple tasks tends to be expensive in terms of storage.
Unsupervised Non-transferable Text Classification
Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
On the Generation of Medical Dialogues for COVID-19
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
