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Found 31 papers, 18 papers with code
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving
With the advancements in natural language processing tasks, math word problem solving has received increasing attention.
Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
Large language models (LLMs) exhibit remarkable in-context learning (ICL) capabilities.
Cross-Modality Safety Alignment
As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount.
SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model
To address these limitations, we propose a Safety Preference Alignment dataset for Vision Language Models named SPA-VL.
Chain of Thought Explanation for Dialogue State Tracking
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding values.
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge.
Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge
Large Language Models (LLMs) have recently showcased remarkable generalizability in various domains.
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs).
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks.
GPTScore: Evaluate as You Desire
Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the utilization of large pre-trained models.
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally.
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary.
Polyglot Prompt: Multilingual Multitask PrompTraining
This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i. e. without any task/language-specific module?
DataLab: A Platform for Data Analysis and Intervention
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.
A Partition Filter Network for Joint Entity and Relation Extraction
In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Ranked #1 on
Relation Extraction
on ADE Corpus
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning".
SpanNER: Named Entity Re-/Recognition as Span Prediction
Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others.
ExplainaBoard: An Explainable Leaderboard for NLP
In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)
Larger-Context Tagging: When and Why Does It Work?
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Towards More Fine-grained and Reliable NLP Performance Prediction
We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.
Interpretable Multi-dataset Evaluation for Named Entity Recognition
With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits.
RethinkCWS: Is Chinese Word Segmentation a Solved Task?
The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models.
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
While neural network-based models have achieved impressive performance on a large body of NLP tasks, the generalization behavior of different models remains poorly understood: Does this excellent performance imply a perfect generalization model, or are there still some limitations?
A Lexicon-Based Graph Neural Network for Chinese NER
Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.
Ranked #13 on
Chinese Named Entity Recognition
on OntoNotes 4
Towards Interpretable Evaluations: A Case Study of Named Entity Recognition
With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits.
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries.
Learning Task-specific Representation for Novel Words in Sequence Labeling
However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance.
