The field of machine learning is constantly evolving, and recent developments have the potential to revolutionize the way we interact with technology. From large language models to value-guided decoding algorithms, researchers are pushing the boundaries of what is possible with machine learning. This newsletter will explore the latest breakthroughs in machine learning research, and how they could create a lasting impact in the field.
This paper presents a technique for integrating Large Language Models (LLMs) into cognitive architectures for autonomous robots. The proposed llama\_ros tool allows for easy integration of LLMs into ROS 2-based environments, and the results show potential for creating a lasting impact in academic research. This survey provides an extensive exploration of alignment techniques for large language models, with the aim of ensuring they exhibit behaviors consistent with human values. It categorizes existing methods and proposes new ones, and evaluates their effectiveness. It is hoped that this survey will bridge the gap between AI alignment research and LLM capability exploration, leading to lasting impact in academic research.
RankVicuna presents a fully open-source large language model (LL
This paper presents a technique for integrating Large Language Models (LLMs) into cognitive architectures for autonomous robots. The proposed llama\_ros tool allows for easy integration of LLMs into ROS 2-based environments, and the results show potential for creating a lasting impact in academic research.
This survey provides an extensive exploration of alignment techniques for large language models, with the aim of ensuring they exhibit behaviors consistent with human values. It categorizes existing methods and proposes new ones, and evaluates their effectiveness. It is hoped that this survey will bridge the gap between AI alignment research and LLM capability exploration, leading to lasting impact in academic research.
RankVicuna presents a fully open-source large language model (LLM) capable of performing listwise reranking in a zero-shot setting. Results on TREC 2019 and 2020 Deep Learning Tracks show that it can achieve effectiveness comparable to GPT-3.5 with a much smaller model, though slightly behind GPT-4. This work has the potential to create a lasting impact in academic research, providing a reproducible and deterministic foundation for future research on reranking with modern LLMs.
This paper presents Label Deconvolution (LD), a novel technique to alleviate learning bias in node representation learning on large-scale attributed graphs. LD approximates the inverse mapping of GNNs to incorporate GNNs in the training phase of NEs, and is shown to converge to the optimal objective function values by the joint training. Experiments demonstrate LD's potential to create a lasting impact in academic research of the described techniques.
The Random Language Model (De Giuli 2019) suggests a single continuous transition to grammatical syntax, which is robust to explicit symmetry breaking. Comparison with human data suggests that the transition is equivalent to that experienced by children at age 24 months, potentially creating a lasting impact in academic research of language learning techniques.
NtUA is a noise-tolerant unsupervised adapter that enables learning superior target models with few-shot unlabelled target samples. It consists of two complementary designs to combat pseudo-label noises and correct both pair values and cache weights. Experiments show that NtUA can create a lasting impact in academic research by achieving superior performance across multiple benchmarks.
InternLM-XComposer is a vision-language large model that enables advanced image-text comprehension and composition. It can generate contextual articles with images, comprehend visual content with multilingual knowledge, and achieve state-of-the-art performance. This model has the potential to revolutionize vision-language interaction and create a lasting impact in academic research.
This paper presents a new approach to understanding the internal behavior of Transformer-based Large Language Models (LLMs) when generating factually incorrect text. By modeling factual queries as Constraint Satisfaction Problems, the authors discover a strong correlation between the model's attention to constraint tokens and the accuracy of its responses. The proposed SAT Probe method can predict constraint satisfaction and factual errors, and has the potential to create a lasting impact in academic research by enhancing the reliability of LLMs.
This paper presents a novel value-guided decoding algorithm, PPO-MCTS, which combines Proximal Policy Optimization (PPO) and Monte-Carlo Tree Search (MCTS) to generate natural language text. Evaluation on four text generation tasks shows that PPO-MCTS significantly improves the quality of generated text compared to PPO alone. The potential for this technique to create a lasting impact in academic research is promising.
This paper presents updated corpora and benchmarks for long-form speech recognition, which could have a lasting impact on academic research. It demonstrates that attention-based encoder-decoders are more susceptible to the train-test mismatch problem, and provides a simple long-form training to improve model robustness.