Search Results for author: Eric Michael Smith

Found 17 papers, 8 papers with code

Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models

no code implementations1 Apr 2024 Yi-Lin Tuan, Xilun Chen, Eric Michael Smith, Louis Martin, Soumya Batra, Asli Celikyilmaz, William Yang Wang, Daniel M. Bikel

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

no code implementations29 Nov 2023 David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Michael Smith

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.

Benchmarking Fairness

Improving Open Language Models by Learning from Organic Interactions

no code implementations7 Jun 2023 Jing Xu, Da Ju, Joshua Lane, Mojtaba Komeili, Eric Michael Smith, Megan Ung, Morteza Behrooz, William Ngan, Rashel Moritz, Sainbayar Sukhbaatar, Y-Lan Boureau, Jason Weston, Kurt Shuster

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

2 code implementations5 Aug 2022 Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, Jason Weston

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.

Continual Learning

"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset

2 code implementations18 May 2022 Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, Adina Williams

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.


Multi-Modal Open-Domain Dialogue

no code implementations EMNLP 2021 Kurt Shuster, Eric Michael Smith, Da Ju, Jason Weston

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).

Visual Dialog

Controlling Style in Generated Dialogue

1 code implementation22 Sep 2020 Eric Michael Smith, Diana Gonzalez-Rico, Emily Dinan, Y-Lan Boureau

Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.

Dialogue Generation

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

no code implementations22 Jun 2020 Stephen Roller, Y-Lan Boureau, Jason Weston, Antoine Bordes, Emily Dinan, Angela Fan, David Gunning, Da Ju, Margaret Li, Spencer Poff, Pratik Ringshia, Kurt Shuster, Eric Michael Smith, Arthur Szlam, Jack Urbanek, Mary Williamson

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.

Continual Learning

I Know the Feeling: Learning to Converse with Empathy

no code implementations ICLR 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.

Dialogue Generation

Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset

9 code implementations ACL 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.

Dialogue Generation

Multiple-Attribute Text Style Transfer

3 code implementations1 Nov 2018 Sandeep Subramanian, Guillaume Lample, Eric Michael Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

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".

Attribute Disentanglement +3

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