Search Results for author:
Found 16 papers, 10 papers with code
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities.
MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator
This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation.
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses.
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit context underlying that relationship.
Mixed Pseudo Labels for Semi-Supervised Object Detection
While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Discovering Knowledge-Critical Subnetworks in Pretrained Language Models
In this work, we investigate whether pretrained language models contain various knowledge-critical subnetworks: particular sparse computational subgraphs that can, if removed, precisely suppress specific knowledge the model has memorized.
Mitigating Label Biases for In-context Learning
In this work, we define a typology for three types of label biases in ICL for text classification: vanilla-label bias, context-label bias, and domain-label bias (which we conceptualize and detect for the first time).
RECKONING: Reasoning through Dynamic Knowledge Encoding
In the outer loop, the model learns to use the updated weights to reproduce and answer reasoning questions about the memorized knowledge.
DISCO: Distilling Counterfactuals with Large Language Models
However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale.
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding
In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models' abilities and limitations on natural language understanding.
Probing Linguistic Information For Logical Inference In Pre-trained Language Models
We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations.
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning
Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI).
Ranked #1 on
Natural Language Inference
on MED
Monotonicity Marking from Universal Dependency Trees
Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics.
Attentive Tree-structured Network for Monotonicity Reasoning
We show and attempt to explain that our model outperforms existing models on MED.
