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Found 7 papers, 5 papers with code
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.
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.
Retrieval Augmentation Reduces Hallucination in Conversation
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020).
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.
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Language Modelling
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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.
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers.
Can Large Language Models Infer Causation from Correlation?
In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs).
