Search Results for author:
Found 27 papers, 21 papers with code
Matryoshka: Learning to Drive Black-Box LLMs with LLMs
To address this challenge, we introduce Matryoshika, a lightweight white-box LLM controller that guides a large-scale black-box LLM generator by decomposing complex tasks into a series of intermediate outputs.
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable.
Aligning Large Language Models with Representation Editing: A Control Perspective
To achieve alignment for specific objectives, we introduce external control signals into the state space of this language dynamical system.
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
To address these challenges, we propose HYDRA, a model factorization framework that captures both user-specific behavior patterns from historical data and shared general knowledge among all users to deliver personalized generation.
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources.
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
BBox-Adapter distinguishes target and source domain data by treating target data as positive and source data as negative.
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
The best-performing models aim to learn a static representation by combining document and hierarchical label information.
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
The teacher LLM generates problem-solving instructions and corrective principles based on the student LLM's errors.
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark.
PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan.
DF2: Distribution-Free Decision-Focused Learning
However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error.
Autoregressive Diffusion Model for Graph Generation
However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
ToolQA: A Dataset for LLM Question Answering with External Tools
To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering.
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling
Most existing methods for learning from noisy labels use static input features for denoising, but these methods are limited by the information they can provide on true label distributions and can result in biased or incorrect predictions.
AdaPlanner: Adaptive Planning from Feedback with Language Models
We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback.
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks.
Ranked #1 on
Zero-Shot Text Classification
on AG News
End-to-End Stochastic Optimization with Energy-Based Model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
We study the problem of extracting N-ary relation tuples from scientific articles.
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.
