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Found 18 papers, 6 papers with code
Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning
How are LLM-based agents used in the future?
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning
In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symbolic form via a fine-tuned Large Language Model, and produces optimal travel itineraries with Mixed Integer Linear Programming solvers.
Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality
People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow.
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.
Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls
The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve.
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.
Staircase Attention for Recurrent Processing of Sequences
Attention mechanisms have become a standard tool for sequence modeling tasks, in particular by stacking self-attention layers over the entire input sequence as in the Transformer architecture.
The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks.
Bot-Adversarial Dialogue for Safe Conversational Agents
Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior.
Not All Memories are Created Equal: Learning to Forget by Expiring
We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality.
Ranked #4 on
Language Modelling
on enwik8
Not All Memories are Created Equal: Learning to Expire
We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve state of the art results on long-context language modeling, reinforcement learning, and algorithmic tasks.
Recipes for Safety in Open-domain Chatbots
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.
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
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.
Recipes for building an open-domain chatbot
Building open-domain chatbots is a challenging area for machine learning research.
All-in-One Image-Grounded Conversational Agents
As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore.
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images.
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy.
