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Found 17 papers, 8 papers with code
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience.
ROBBIE: Robust Bias Evaluation of Large Generative Language Models
In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages
We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Improving Open Language Models by Learning from Organic Interactions
We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety.
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks.
"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset
As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms.
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
At the heart of improving conversational AI is the open problem of how to evaluate conversations.
Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models
All AI models are susceptible to learning biases in data that they are trained on.
Multi-Modal Open-Domain Dialogue
Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020).
Ranked #1 on
Visual Dialog
on Wizard of Wikipedia
Controlling Style in Generated Dialogue
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills
Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent.
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles
Text style transfer is usually performed using attributes that can take a handful of discrete values (e. g., positive to negative reviews).
I Know the Feeling: Learning to Converse with Empathy
Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
Multiple-Attribute Text Style Transfer
The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".
