??Top ML Papers of the Week

Welcome to the Top ML Papers of the Week (April 1 - April 7)!

1). Many-shot Jailbreaking - proposes a jailbreaking technique called many-shot jailbreaking to evade the safety guardrails of LLMs; this jailbreaking technique exploits the longer context window supported by many modern LLMs; it includes a very large number of faux dialogues (~256) preceding the final question which effectively steers the model to produce harmful responses. (paper | tweet )

2). SWE-Agent - a new open-source agentic system that can automatically solve GitHub issues with similar accuracy as Devin on the SWE-bench; the agent interacts with a specialized terminal and enables important processing of files and executable tests to achieve good performance; on SWE-bench, SWE-agent resolves 12.29% of issues, achieving the state-of-the-art performance on the full test set. (paper | tweet )

3). Mixture-of-Depths - demonstrates that transformer models can learn to efficiently and dynamically allocate FLOPs to specific positions in a sequence; this helps to optimize the allocation along the sequence for different layers across model depth; findings suggest that for a given FLOP budget models can be trained to perform faster and better than their baseline counterparts. (paper | tweet )

4). Local Context LLMs Struggle with Long In-Context Learning - finds that after evaluating 13 long-context LLMs on long in-context learning the LLMs perform relatively well under the token length of 20K. However, after the context window exceeds 20K, most LLMs except GPT-4 will dip dramatically. (paper | tweet )

5). Visualization-of-Thought - inspired by a human cognitive capacity to imagine unseen worlds, this new work proposes Visualization-of-Thought (VoT) prompting to elicit spatial reasoning in LLMs; VoT enables LLMs to "visualize" their reasoning traces, creating internal mental images, that help to guide subsequent reasoning steps; when tested on multi-hop spatial reasoning tasks like visual tiling and visual navigation, VoT outperforms existing multimodal LLMs. (paper | tweet )

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6). The Unreasonable Ineffectiveness of the Deeper Layers - finds that a simple layer-pruning strategy of popular open-weight pretraining LLMs shows minimal performance degradation until after a large fraction (up to half) of the layers are removed; using a layer similarity mechanism optimal blocks are identified and pruned followed by a small amount of fine-tuning to heal damage. (paper | tweet )

7). JetMoE - an 8B model trained with less than $ 0.1 million cost but outperforms LLaMA2-7B; shows that LLM training can be much cheaper than generally thought; JetMoE-8B has 24 blocks where each block has two MoE layers: Mixture of Attention heads (MoA) and Mixture of MLP Experts (MoE); each MoA and MoE layer has 8 experts, and 2 experts are activated for each input token with 2.2B active parameters. (paper | tweet )

8). Representation Finetuning for LMs - proposes a method for representation fine-tuning (ReFT) that operates on a frozen base model and learns task-specific interventions on hidden representations; in other words, by manipulating a small fraction of model representations it is possible to effectively steer model behavior to achieve better downstream performance at inference time; also proposes LoReFT as a drop-in replacement for PEFTs that is 10-50x more parameter efficient. (paper | tweet )

9). Advancing LLM Reasoning - proposes a suite of LLMs (Eurus) optimized for reasoning and achieving SoTA among open-source models on tasks such as mathematics and code generation; Eurus-70B outperforms GPT-3.5 Turbo in reasoning largely due to a newly curated, high-quality alignment dataset designed for complex reasoning tasks; the data includes instructions with preference tree consisting of reasoning chains, multi-turn interactions and pairwise data for preference learning. (paper | tweet )

10). Training LLMs over Neurally Compressed Text - explores training LLMs with neural text compressors; the proposed compression technique segments text into blocks that each compress to the same bit length; the approach improves at scale and outperforms byte-level baselines on both perplexity and inference speed benchmarks; latency is reduced to the shorter sequence length. (paper | tweet )

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