Reinforcement Learning from Human Feedback (RLHF) uses a reward model trained on human preferences to align large language models (LLMs) with the aim of optimizing rewards. Yet, there are issues such as the model becoming too specialized, the potential for the LLM to exploit flaws in the reward model, and a reduction in output variety.…
Artificial Intelligence (AI) systems are tested rigorously before their release to ensure they cannot be used for dangerous activities like bioterrorism or manipulation. Such safety measures are essential as powerful AI systems are coded to reject commands that may harm them, unlike less potent open-source models. However, researchers from UC Berkeley recently found that guaranteeing…
Multimodal large language models (MLLMs) are crucial tools for combining the capabilities of natural language processing (NLP) and computer vision, which are needed to analyze visual and textual data. Particularly useful for interpreting complex charts in scientific, financial, and other documents, the prime challenge lies in improving these models to understand and interpret charts accurately.…
The field of research that aims to enhance large multimodal models (LMMs) to effectively interpret long video sequences faces challenges stemming from the extensive amount of visual tokens vision encoders generate. These visual tokens pile up, particularly with LLaVA-1.6 model, which generates between 576 and 2880 visual tokens for one image, a number that significantly…
Group Relative Policy Optimization (GRPO) is a recent reinforcement learning method introduced in the DeepSeekMath paper. Developed as an upgrade to the Proximal Policy Optimization (PPO) framework, GRPO aims to improve mathematical reasoning skills while lessening memory use. This technique is especially suitable for functions that require sophisticated mathematical reasoning.
The implementation of GRPO involves several…
Scientists at Sierra presented τ-bench, an innovative benchmark intended to test the performance of language agents in dynamic, realistic scenarios. Current evaluation methods are insufficient and unable to effectively assess if these agents are capable of interacting with human users or comply with complex, domain-specific rules, all of which are crucial for practical implementation. Most…
The field of software engineering has made significant strides with the development of Large Language Models (LLMs). These models are trained on comprehensive datasets, allowing them to efficiently perform a myriad of tasks which comprise of code generation, translation, and optimization. LLMs are increasingly being employed for compiler optimization. However, traditional code optimization methods require…
Large Language Models (LLMs) have made significant strides in addressing various reasoning tasks, such as math problems, code generation, and planning. However, as these tasks become more complex, LLMs struggle with inconsistencies, hallucinations, and errors. This is especially true for tasks requiring multiple reasoning steps, which often operate on a "System 1" level of thinking…
Natural Language Processing (NLP), a field within artificial intelligence, is focused on creating ways for computers and human language to interact. It's used in many technology sectors such as machine translation, sentiment analysis, and information retrieval. The challenge presently faced is the evaluation of long-context language models, which are necessary for understanding and generating text…
A team of researchers from institutions including the Arc Institute and UC Berkeley discovered that certain mobile genetic elements found extensively in bacteria and archaea known as IS110 insertion sequences or MGEs express a structured non-coding RNA (ncRNA) that interacts with their recombinase. This unique RNA, called "bridge" RNA, contains two loops that specifically interact…
Large language models (LLMs), despite their significant advancements, often struggle in situations where information is spread across long stretches of text. This issue, referred to as the "lost-in-the-middle" problem, results in a diminished ability for LLMs to accurately find and use information that isn't located near the start or end of the text. Consequently, LLMs…
Large language models (LLMs), despite their advancements, often face difficulties in managing long contexts where information is scattered across the entire text. This phenomenon is referred to as the ‘lost-in-the-middle’ problem, where LLMs struggle to accurately identify and utilize information within such contexts, especially as it becomes distant from the beginning or end. Researchers from…
