The world of Artificial Intelligence (AI) has taken another step forward with the introduction of the recent Yi-1.5-34B model by 01.AI. This model is considered a significant upgrade over prior versions, providing a bridge between the capabilities of the Llama 3 8B and the 70B models.
The distinguishing features of the Yi-1.5-34B include improvements in multimodal…
Large language models (LLMs) have been successful in areas like natural language tasks and following instructions, yet they have limitations when dealing with non-textual data such as images and audio. But presently, an approach integrating textual LLMs with speech encoders in one training setup could revolutionize this. One option is multimodal audio-language models, proving advantageous…
The standard method for aligning Language Learning Models (LLMs) is known as RLHF, or Reinforcement Learning from Human Feedback. However, new developments in offline alignment methods - such as Direct Preference Optimization (DPO) - challenge RLHF's reliance on on-policy sampling. Unlike online methods, offline algorithms use existing datasets, making them simpler, cheaper, and often more…
Natural Language Processing (NLP) is a revolutionary field that allows machines to understand, interpret, and generate human language. It is widely used in various sectors, including language translation, text summarization, sentiment analysis, and the creation of conversational agents. Large language models (LLMs), which have greatly improved these applications, require huge computational and energy demands for…
Recent multimodal foundation models are often limited in their ability to fuse various modalities, as they typically utilize distinct encoders or decoders for each modality. This structure limits their capability to effectively integrate varied content types and create multimodal documents with interwoven sequences of images and text.
Meta researchers, in response to this limitation, have…
Artificial Intelligence (AI) systems have demonstrated a fascinating trend of converging data representations across different architectures, training objectives, and modalities. Researchers propose the "Platonic Representation Hypothesis" to explain this phenomenon. Essentially, this hypothesizes that various AI models are striving to capture a unified representation of the underlying reality that forms the basis for observable data.…
Artificial intelligence is extensively utilized in today's world by both businesses and individuals, with a particular reliance on large language models (LLMs). Despite their broad range of applications, LLMs have certain limitations that restrict their effectiveness. Key among these limitations is their inability to retain long-term conversations, which hampers their capacity to deliver consistent and…
Large Language Models (LLMs) such as GPT 3.5 and GPT 4 have recently garnered substantial attention in the Artificial Intelligence (AI) community for their ability to process vast amounts of data, detect patterns, and simulate human-like language in response to prompts. These LLMs are capable of self-improvement over time, drawing upon new information and user…