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Small language models learn to negotiate with SocialRL, PazaBench V2 expands speech AI evaluation across African languages, and EvoLib helps agents turn experience into knowledge. Plus: new methods for more reliable A/B testing and advances in AI-driven precision oncology.
19,238 Aufrufe • vor 2 Monaten •via X (Twitter)
7 Kommentare
ZHUOLINvor 2 Monaten
Negotiation, multilingual speech, and agent memory in one update is a surprisingly wide research snapshot.
Vanarvor 2 Monaten
The next wave of AI progress will come from improving the entire ecosystem. Not just the foundation models themselves.
安叫兽|Bird🕊️ 🔶 BNBvor 2 Monaten
这几个方向跨度也太大了
AI Mastery Guidevor 2 Monaten
Small models learning to negotiate is such an underrated line in here.
รับงานลพบุรีvor 2 Monaten
Sounds like some exciting advancements in AI and language models
Pearl.comvor 2 Monaten
@MSFTResearch The thread shares a deeper pattern: AI systems improve when feedback becomes infrastructure. Negotiation, speech, experiments, and oncology all depend on better signals, not just bigger models.
Yaser Abbassvor 2 Monaten
pazabench v2 is a big step for making speech ai actually work globally.
![Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.](https://image.24vids.com/tw-1672048076469698561/ext_tw_video_thumb/1672047839378300928/pu/img/MuoOBsAyUd-HAI7D.jpg)
