As a scientific researcher, you need to search and browse a large amount of academic literature every day to obtain the latest scientific and technological progress and research results. However, traditional retrieval and reading methods can no longer meet the needs of researchers.
ChatPaper, a document knowledge tool that integrates retrieval, reading, and knowledge question-and-answer. Help you quickly improve the efficiency of searching and reading papers, obtain the latest research trends in the field, and make scientific research work more easily.
Combined with the cutting-edge dynamic subscription function, select arXiv's popular new papers of the day to form a summary of papers, so that everyone can understand cutting-edge trends more quickly.
If you want to have an in-depth dialogue on a certain paper, you can directly copy the link of the paper to your browser or go directly to the ChatPaper page: https://www.aminer.cn/chat/g/
List of Featured New Papers for July 19, 2023:
1.Communicative Agents for Software Development
Link: https://www.aminer.cn/pub/64b60eaa3fda6d7f06eaea2a/
ChatPaper review: This paper proposes an innovative paradigm that utilizes large language models (LLMs) to streamline and unify key processes through natural language communication throughout the software development process, thereby eliminating the need for specialized models at each stage. At the heart of this paradigm is ChatDev, a fictitious chat-based software development company that simulates the established waterfall model, subdividing the development process into four distinct time periods of design, coding, testing, and documentation. Each stage involves a team of agents such as programmers, code reviewers, and test engineers, facilitating collaborative dialogue and facilitating a seamless workflow. The chat chain acts as a facilitator, decomposing each stage into atomic subtasks. This enables dual roles to efficiently solve specific subtasks by proposing and validating solutions through context-aware communication. ChatDev's empirical analysis highlights its remarkable effectiveness in software generation, enabling the entire software development process to be completed in less than seven minutes at a cost of less than a dollar. It not only identifies and mitigates potential vulnerabilities, but also corrects underlying illusions while remaining efficient and cost-effective. The potential of ChatDev reveals new opportunities to integrate LLMs into the field of software development.
2.Llama 2: Open Foundation and Fine-Tuned Chat Models
Link: https://www.aminer.cn/pub/64b758dd1a5852438b7976ff/
ChatPaper roundup: Llama 2 is a pre-trained and fine-tuned collection of large language models (LLMs) with parameters ranging from 7 billion to 70 billion. Among them, the fine-tuned LLM is called Llama 2-Chat, which is specially optimized for conversational use cases. Our model outperforms open-source chat models on most of the benchmarks we test, and may be a suitable replacement for closed-source models based on human evaluations we conduct regarding usefulness and safety. We describe in detail our approach to fine-tuning and security improvements to Llama 2-Chat in order to enable the community to build on our foundation and contribute to the responsible development of LLM
3.Augmenting CLIP with Improved Visio-Linguistic Reasoning
Link: https://www.aminer.cn/pub/64b76c703fda6d7f068eecf3/
ChatPaper review: The paper points out the performance problem of existing contrastive image-text models on synthetic vision-language tasks (such as Winoground), whose performance is equivalent to random guessing. Then, the paper proposes a method called SDS-CLIP, which improves the synthesis of CLIP by fine-tuning CLIP using differentiable image parameterization by distilling targets from text-to-image generation models such as Stable-Diffusion. Visual-verbal reasoning skills. On the challenging Winoground synthetic inference benchmark, the method improves the absolute visual-language performance of different CLIP models by up to 7%, and on the ARO dataset, the method improves the visual-language performance by up to 3%. Slight improvements in zero-shot performance on various downstream datasets are also found by introducing visual-linguistic reasoning into CLIP. The approach highlights that well-designed distillation objectives from generative models can be used to extend existing contrastive image-text models and improve their visual-language reasoning capabilities.
4.How is ChatGPT’s behavior changing over time?
Link: https://www.aminer.cn/pub/64b76c6a3fda6d7f068ee31b/
ChatPaper roundup: The article points out that the behavior of two large language model (LLM) services, GPT-3.5 and GPT-4, will change over time. The authors support this notion by evaluating the performance of GPT-3.5 and GPT-4 on four different tasks: 1) solving math problems, 2) answering sensitive/dangerous questions, 3) generating code, and 4) visual reasoning. The study found that the performance and behavior of both GPT-3.5 and GPT-4 will vary greatly over time. For example, GPT-4 (March 2023) does very well at finding prime numbers (97.6% accuracy), but GPT-4 (June 2023) performs very poorly on the same problem (2.4% accuracy ). Interestingly, GPT-3.5 (June 2023) performs better than GPT-3.5 (March 2023) on this task. In June, GPT-4 was less willing to answer sensitive questions than in March, and both GPT-4 and GPT-3.5 were more prone to malformed codes in June than March. Collectively, these findings suggest that the behavior of the same LLM service can vary significantly over a relatively short period of time, emphasizing the need for continuous monitoring of LLM quality.
5.DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion
Link: https://www.aminer.cn/pub/6417d04090e50fcafd83db60/
ChatPaper review: Introduces a new approach to automatically generate artistic fonts by styling one or more fonts to visually convey the semantics of input words while ensuring that the output is still readable. To address the various challenges we face, including conflicting goals (artistic stylization vs. readability), lack of benchmark data, and huge search space, our method leverages a large language model to discriminate between text and visual images. Stylization is modeled and an unsupervised generative model is built, the backbone of which is a diffusion model. Specifically, we adopt the denoising generator in Latent Diffusion Model (LDM), and adapt the input style to the input text through a CNN-based discriminator. The discriminator uses the rasterized image of the given font as the real sample and the output of the denoising generator as the fake sample. Our model is called DS-Fusion, where DS stands for Discriminative and Styled Diffusion. We demonstrate the quality and versatility of our method with numerous examples, qualitative and quantitative evaluations, and ablation studies. The powerful performance of DS-Fusion is demonstrated through user studies with strong baselines and artist-made fonts including CLIPDraw and DALL-E 2. From the title and abstract, it can be concluded that this paper addresses the problem of automatically generating artistic fonts that combine artistic style with readability.
6.NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Link: https://www.aminer.cn/pub/64b76c6a3fda6d7f068ee3b5/
ChatPaper review: pointed out that the MCC method has two key problems in the field of 3D reconstruction under single-view RGB-D input: 1) Transformer decoder is inefficient when processing a large number of query points; 2) 3D representation is difficult to recover high-fidelity details . To solve these problems, a new method called NU-MCC is proposed in this paper. NU-MCC includes two key innovations: Neighborhood Decoder and Repulsive Unsigned Distance Function (Repulsive UDF). First, a neighborhood decoder introduces center points as effective proxies for input visual features, such that each query point can only be associated with a small neighborhood. This design not only improves the inference speed, but also utilizes finer visual features to improve the restoration of 3D textures. Second, the Repulsive UDF is a novel alternative to the occupancy field used in MCC, which significantly improves the quality of 3D object reconstruction. Compared with standard UDFs with holes in the result, our proposed Repulsive UDF can achieve more complete surface reconstruction. Experimental results show that NU-MCC is able to learn powerful 3D representations and has made remarkable progress in the field of single-view 3D reconstruction. In particular, it achieves a 9.7% higher F1 score than MCC on the CO3D-v2 dataset, while running faster by more than 5 times.
7.Biomaker CA: a Biome Maker project using Cellular Automata
Link: https://www.aminer.cn/pub/64b76c703fda6d7f068eed4c/
ChatPaper review: Introduces a project called Biomaker CA, which uses cellular automata (Cellular Automata) to simulate the generation of biological communities. In Biomaker CA, morphogenesis is a top priority, and small seeds need to grow into plant-like organisms in a nutrient-poor environment to survive and eventually reproduce in mutated ways to sustain the long-term survival of the biome. Simulate complex biomes by using cellular automaton rules on 2D grids, and parallelize computation on GPUs via the Python JAX framework. The project allows the use of different kinds of environments and "laws of physics", as well as different model architectures and mutation strategies. The authors further analyzed some configurations, showing how individual plant species grow, survive, reproduce, and evolve to form stable and unstable biomes. The authors then show how to make models survive harsh environments through end-to-end meta-evolution or a more precise and efficient method called Petri Dish meta-evolution. Finally, the authors show how to do interactive evolution, where users can decide how to evolve interactively with plant models and deploy them in larger environments.
How to use ChatPaper?
The method of using ChatPaper is very simple. Open the AMiner homepage and enter the ChatPaper page from the navigation bar at the top of the page or the lower right corner.
On the ChatPaper page, you can choose to have a dialogue based on a single document or a dialogue based on the entire library (personal library), and you can choose to upload a local PDF or directly search for documents on AMiner.