Search Results for author:
Found 24 papers, 15 papers with code
LongEmbed: Extending Embedding Models for Long Context Retrieval
This paper explores context window extension of existing embedding models, pushing the limit to 32k without requiring additional training.
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of parallel data.
Robust Pronoun Use Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
We find that while models can mostly faithfully reuse previously-specified pronouns in the presence of no distractors, they are significantly worse at processing she/her/her, singular they and neopronouns.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
Retrieval-based Full-length Wikipedia Generation for Emergent Events
In today's fast-paced world, the growing demand to quickly generate comprehensive and accurate Wikipedia documents for emerging events is both crucial and challenging.
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
LawBench: Benchmarking Legal Knowledge of Large Language Models
We hope this benchmark provides in-depth understanding of the LLMs' domain-specified capabilities and speed up the development of LLMs in the legal domain.
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
To decouple train length from target length for efficient context window extension, we propose Positional Skip-wisE (PoSE) training that smartly simulates long inputs using a fixed context window.
RestGPT: Connecting Large Language Models with Real-World RESTful APIs
To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection.
Large Language Models are not Fair Evaluators
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e. g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models.
Weaker Than You Think: A Critical Look at Weakly Supervised Learning
In this paper, we revisit the setup of these approaches and find that the benefits brought by these approaches are significantly overestimated.
RepCL: Exploring Effective Representation for Continual Text Classification
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages
However, text classification in low-resource languages is still challenging due to the lack of annotated data.
Meta Self-Refinement for Robust Learning with Weak Supervision
Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost.
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision.
GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction
In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms.
Neural Data-to-Text Generation with LM-based Text Augmentation
Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.
Analysing the Noise Model Error for Realistic Noisy Label Data
Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors.
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages.
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way.
Low Resource Named Entity Recognition
named-entity-recognition
+3
An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification
This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking.
Image Manipulation with Natural Language using Two-sidedAttentive Conditional Generative Adversarial Network
We propose the Two-sidEd Attentive conditional Generative Adversarial Network (TEA-cGAN) to generate semantically manipulated images while preserving other contents such as background intact.
