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Found 23 papers, 20 papers with code
On Prompt-Driven Safeguarding for Large Language Models
Prepending model inputs with safety prompts is a common practice for safeguarding large language models (LLMs) from complying with queries that contain harmful intents.
Large Language Models Are Not Robust Multiple Choice Selectors
This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent "selection bias", namely, they prefer to select specific option IDs as answers (like "Option A").
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition.
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Dialogue contradiction is a critical issue in open-domain dialogue systems.
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
Applying this approach, we construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus.
COLD: A Benchmark for Chinese Offensive Language Detection
To this end, we propose a benchmark --COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset --COLDATASET and a baseline detector --COLDETECTOR which is trained on the dataset.
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings, with focuses on context-sensitive unsafety, which is under-explored in prior works.
Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation
In this work, we focus on the few-shot learning for grounded dialog generation (GDG).
CEM: Commonsense-aware Empathetic Response Generation
We evaluate our approach on EmpatheticDialogues, which is a widely-used benchmark dataset for empathetic response generation.
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
In this paper, we present a denoising sequence-to-sequence (seq2seq) autoencoder via contrastive learning for abstractive text summarization.
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support
In this paper, we propose PsyQA, a Chinese dataset of psychological health support in the form of question and answer pair.
Towards Emotional Support Dialog Systems
Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats.
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
A Two-Phase Approach for Abstractive Podcast Summarization
Podcast summarization is different from summarization of other data formats, such as news, patents, and scientific papers in that podcasts are often longer, conversational, colloquial, and full of sponsorship and advertising information, which imposes great challenges for existing models.
Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
We introduce a new approach for abstractive text summarization, Topic-Guided Abstractive Summarization, which calibrates long-range dependencies from topic-level features with globally salient content.
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation
In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research.
A Baseline Analysis for Podcast Abstractive Summarization
Podcast summary, an important factor affecting end-users' listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications.
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation
The research of knowledge-driven conversational systems is largely limited due to the lack of dialog data which consist of multi-turn conversations on multiple topics and with knowledge annotations.
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora.
