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Found 32 papers, 16 papers with code
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label
Compared with DNNC, our proposed method is more efficient in both training and serving since it is based upon the entailment between query utterance and labels instead of all the training examples.
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging
The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking.
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
To investigate RPAs' performance when faced with different types of conflicting requests, we develop an evaluation benchmark that includes contextual knowledge conflicting requests, parametric knowledge conflicting requests, and non-conflicting requests to assess RPAs' ability to identify conflicts and refuse to answer appropriately without over-refusing.
Towards Biologically Plausible Computing: A Comprehensive Comparison
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs.
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process.
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters.
Rethinking Jailbreaking through the Lens of Representation Engineering
The recent surge in jailbreaking methods has revealed the vulnerability of Large Language Models (LLMs) to malicious inputs.
Aligning Large Language Models with Human Preferences through Representation Engineering
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons
Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs.
SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network
Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and linguistic tasks while offering the advantage of improved energy efficiency.
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings.
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification.
Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets
Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model's output over another is often necessary.
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap.
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
Our evaluation results on the single-task polarity prediction show that our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Converse: A Tree-Based Modular Task-Oriented Dialogue System
At the core of the struggle is the need to script every single turn of interactions between the bot and the human user.
Structure Extraction in Task-Oriented Dialogues with Slot Clustering
Extracting structure information from dialogue data can help us better understand user and system behaviors.
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Factual consistency is an essential quality of text summarization models in practical settings.
Exploring Neural Models for Query-Focused Summarization
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
To enlarge the set of base classes, we propose a method to automatically generate pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs.
DialFact: A Benchmark for Fact-Checking in Dialogue
Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation.
MixQG: Neural Question Generation with Mixed Answer Types
Asking good questions is an essential ability for both human and machine intelligence.
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Then, we fine-tune a base summarization model, which is trained on all training samples, on the clean (noisy) subset to obtain an \textit{expert} (\textit{anti-expert}) model.
Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
In this work, we empirically show that catastrophic forgetting occurs in such methods by evaluating them with general NLP tasks in GLUE.
HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Summarization systems make numerous "decisions" about summary properties during inference, e. g. degree of copying, specificity and length of outputs, etc.
HydraSum - Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Existing abstractive summarization models lack explicit control mechanisms that would allow users to influence the stylistic features of the model outputs.
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models
Predicting the fitness, i. e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data.
Controllable Abstractive Dialogue Summarization with Sketch Supervision
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
QAConv: Question Answering on Informative Conversations
This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source.
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity
2) A simple strategy to control the granularity of the final summary.
Learning from Mistakes: Using Mis-predictions as Harm Alerts in Language Pre-Training
According to recent studies and our empirical observations, one possible reason is that some easy-to-fit patterns in the training data, such as frequently co-occurring word combinations, dominate and harm pre-training, making it hard for the model to fit more complex information.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.
