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Found 18 papers, 14 papers with code
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.
PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.
HiMemFormer: Hierarchical Memory-Aware Transformer for Multi-Agent Action Anticipation
Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI.
$\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics
Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios.
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Recent studies show the growing significance of document retrieval in the generation of LLMs, i. e., RAG, within the scientific domain by bridging their knowledge gap.
Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
In this work, we study whether retrievers can recognize and respond to different perspectives of the queries -- beyond finding relevant documents for a claim, can retrievers distinguish supporting vs. opposing documents?
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance.
"Merge Conflicts!" Exploring the Impacts of External Distractors to Parametric Knowledge Graphs
Large language models (LLMs) acquire extensive knowledge during pre-training, known as their parametric knowledge.
Video State-Changing Object Segmentation
Learning about object state changes in Video Object Segmentation (VOS) is crucial for understanding and interacting with the visual world.
On Measuring the Intrinsic Few-Shot Hardness of Datasets
While advances in pre-training have led to dramatic improvements in few-shot learning of NLP tasks, there is limited understanding of what drives successful few-shot adaptation in datasets.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Weakly Supervised Text Classification using Supervision Signals from a Language Model
In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals.
Probing Toxic Content in Large Pre-Trained Language Models
Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.
Learning Contextual Causality from Time-consecutive Images
Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations.
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
Experimental results prove that even though pre-trained language representation models have achieved promising progress on the original WSC dataset, they are still struggling at WinoWhy.
