We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (i) pretrained Transformer variants are currently the best performing models on this task, (ii) but to improve performance on multi-turn conversations with humans, future systems must go beyond single word metrics like perplexity to measure the performance across sequences of utterances (conversations) in terms of repetition, consistency and balance of dialogue acts (e.g. how many questions asked vs. answered).

Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero-and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, 1 while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models. * Equal contribution. † Work done while at Meta AI. 1 Following Brown et al. (2020), we use GPT-3 to refer to both the 175B model and the smaller scale models as well.2 Exceptions include work by EleutherAI, who released dense models up to 20B in size (Black et al., 2022), Salesforce (Nijkamp et al., 2022), and Meta AI, who released dense models up to 13B and sparse models up to 1. 1T (Artetxe et al., 2021). There is also ongoing work from the BigScience workshop (https://bigscience. huggingface.co/), which aims to open source very large multilingual language models and datasets.

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Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., "a man playing a guitar"). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, PERSONALITY-CAPTIONS, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 241,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations [36] with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations [32] with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance. 2 https://www.photoup.net/how-to-write-more-engaging-photo-captions/ 3 https://www.poynter.org/news/6-tips-writing-photo-captions

Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, Blended-SkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.