Search Results for author:
Found 26 papers, 11 papers with code
Entropy-Based Adaptive Weighting for Self-Training
The mathematical problem-solving capabilities of large language models have become a focal point of research, with growing interests in leveraging self-generated reasoning paths as a promising way to refine and enhance these models.
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice.
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection
We introduce an evaluation of a comprehensive collection of decoding-free candidate selection approaches on a comprehensive set of tasks, including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with a massive amount of candidates, some with 10k+ options.
MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process.
Are Large-Language Models Graph Algorithmic Reasoners?
To address this gap, we introduce a novel benchmark designed to evaluate LLM performance on classical algorithmic reasoning tasks on explicit graphs.
GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation
Existing retrieval-based reasoning approaches for large language models (LLMs) heavily rely on the density and quality of the non-parametric knowledge source to provide domain knowledge and explicit reasoning chain.
CLIMB: A Benchmark of Clinical Bias in Large Language Models
We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in Large Language Models), a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks.
MIRAI: Evaluating LLM Agents for Event Forecasting
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care.
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs.
Improving Event Definition Following For Zero-Shot Event Detection
We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types.
Instructional Fingerprinting of Large Language Models
The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (e. g. restricting commercial use).
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach.
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance.
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights, including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information.
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performance.
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models
Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document.
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage.
Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces
The chronological hierarchies between knowledge graphs at different timestamps are represented by embedding the knowledge graphs as vectors in a common hyperbolic space.
DICE: Data-Efficient Clinical Event Extraction with Generative Models
Event extraction for the clinical domain is an under-explored research area.
Summarization as Indirect Supervision for Relation Extraction
Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i. e., extracting a kind of synoptical information that describes the relation of entity mentions.
Ranked #7 on
Relation Extraction
on TACRED
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability.
EventPlus: A Temporal Event Understanding Pipeline
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.
Implicit Discourse Relation Identification for Open-domain Dialogues
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system.
Implicit Discourse Relation Classification
Implicit Relations
+2
