Search Results for author:
Found 19 papers, 15 papers with code
Speculative Contrastive Decoding
Large language models~(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias.
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods.
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies.
Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization
In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?
Ranked #50 on
Math Word Problem Solving
on MATH
(using extra training data)
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data.
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs.
Ranked #100 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions between humans and models.
Revisiting Automatic Question Summarization Evaluation in the Biomedical Domain
To better understand whether commonly used evaluation metrics are capable of evaluating automatic summarization in the biomedical domain, we conduct human evaluations of summarization quality from four different aspects of a biomedical question summarization task.
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED).
How well do Large Language Models perform in Arithmetic tasks?
Large language models have emerged abilities including chain-of-thought to answer math word problems step by step.
EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models
Electronic health records (EHR) contain a wealth of biomedical information, serving as valuable resources for the development of precision medicine systems.
RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training
We propose to retrieve similar image-text pairs based on ITC from pretraining datasets and introduce a novel retrieval-attention module to fuse the representation of the image and the question with the retrieved images and texts.
SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation.
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information.
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
Ranked #3 on
Entity Linking
on MedMentions
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification
To address this issue, we devise an adaptive mechanism to align reinforcement learning and classification methods using distribution entropy as the medium.
