🥇Top ML Papers of the Week

The top ML Papers of the Week (Jan 1 - Jan 7)

1). Mobile ALOHA - proposes a system that learns bimanual mobile manipulation with low-cost whole-body teleoperation; it first collects high-quality demonstrations and then performs supervised behavior cloning; finds that co-training with existing ALOHA datasets increases performance on complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots while keeping the budget under $32K. (paper | tweet)

---

2). Mitigating Hallucination in LLMs - summarizes 32 techniques to mitigate hallucination in LLMs; introduces a taxonomy categorizing methods like RAG, Knowledge Retrieval, CoVe, and more; provides tips on how to apply these methods and highlights the challenges and limitations inherent in them. (paper | tweet)

---

3). Self-Play Fine-tuning - shows that without acquiring additional human-annotated data, a supervised fine-tuned LLM can be improved; inspired by self-play, it first uses the LLM to generate its training data from its previous iterations; it then refines its policy by distinguishing the self-generated responses from those obtained from human-annotated data; shows that the method can improve LLM’s performance and outperform models trained via DPO with GPT-4 preference data. (paper | tweet)

---

4). LLaMA Pro - proposes a post-pretraining method to improve an LLM’s knowledge without catastrophic forgetting; it achieves this by tuning expanded identity blocks using only new corpus while freezing the inherited blocks; uses math and code data to train a LLaMA Pro-8.3B initialized from Llama2-7B; these models achieve advanced performance on various benchmarks compared to base models while preserving the original general capabilities. (paper | tweet)

---

5). LLM Augmented LLMs - explore composing existing foundation models with specific models to expand capabilities; introduce cross-attention between models to compose representations that enable new capabilities; as an example, a PaLM2-S model was augmented with a smaller model trained on low-resource languages to improve English translation and arithmetic reasoning for low-resource languages; this was also done with a code-specific model which led to a 40% improvement over the base code model on code generation and explanation tasks. (paper | tweet)

---

6). Fast Inference of Mixture-of-Experts - achieves efficient inference of Mixtral-8x7B models through offloading; it applies separate quantization for attention layers and experts to fit the model in combined GPU and CPU memory; designs a MoE-specific offloading strategy that enables running Mixtral-8x7B on desktop hardware and free-tier Google Colab instances. (paper | tweet)

---

7). GPT-4V is a Generalist Web Agent - explores the potential of GPT-4V as a generalist web agent; in particular, can such a model follow natural language instructions to complete tasks on a website? the authors first developed a tool to enable web agents to run on live websites; findings suggest that GPT-4V can complete 50% of tasks on live websites, possible through manual grounding of its textual plans into actions on the websites. (paper | tweet)

---

8). DocLLM - a lightweight extension to traditional LLMs for reasoning over visual documents; focuses on using bounding box information to incorporate spatial layout structure; proposes a pre-training objective that addresses irregular layout and heterogeneous content present in visual documents; it’s then fine-tuned on an instruction-dataset and demonstrate SoTA performance on 14 out of 16 datasets across several document intelligence tasks. (paper | tweet)

---

9). How Code Empowers LLMs - a comprehensive overview of the benefits of training LLMs with code-specific data. Some capabilities include enhanced code generation, enabling reasoning, function calling, automated self-improvements, and serving intelligent agents. (paper | tweet)

---

10). Instruct-Imagen - proposes an image generation model that tackles heterogeneous image generation tasks and generalizes across unseen tasks; it first enhances the model’s ability to ground its generation on external multimodal context and then fine-tunes on image generation tasks with multimodal instructions. (paper | tweet)

---