Machine translation models sometimes lead to added toxicity: translated outputs may contain more toxic content that the original input. In this paper, we introduce MinTox, a novel pipeline to automatically identify and mitigate added toxicity at inference time, without further model training. MinTox leverages a multimodal (speech and text) toxicity classifier that can scale across languages.We demonstrate the capabilities of MinTox when applied to SEAMLESSM4T, a multi-modal and massively multilingual machine translation system. MinTox significantly reduces added toxicity: across all domains, modalities and language directions, 25% to95% of added toxicity is successfully filtered out, while preserving translation quality

Despite broad interest in modeling spoken dialogue agents, most approaches are inherently "half-duplex" -restricted to turn-based interaction with responses requiring explicit prompting by the user or implicit tracking of interruption or silence events.Human dialogue, by contrast, is "full-duplex" allowing for rich synchronicity in the form of quick and dynamic turn-taking, overlapping speech, and backchanneling.Technically, the challenge of achieving full-duplex dialogue with LLMs lies in modeling synchrony as pre-trained LLMs do not have a sense of "time".To bridge this gap, we propose Synchronous LLMs for fullduplex spoken dialogue modeling.We design a novel mechanism to integrate time information into Llama3-8b so that they run synchronously with the real-world clock.We also introduce a training recipe that uses 212k hours of synthetic spoken dialogue data generated from text dialogue data to create a model that generates meaningful and natural spoken dialogue, with just 2k hours of real-world spoken dialogue data.Synchronous LLMs outperform state-of-the-art in dialogue meaningfulness while maintaining naturalness.Finally, we demonstrate the model's ability to participate in full-duplex dialogue by simulating interaction between two agents trained on different datasets, while considering Internet-scale latencies of up to 240ms.