Orionnnn/hub-stats huggingface dataset:
Toxicity of the Commons: Curating Open-Source Pre-Training Data
Authors: name: Catherine Arnett, name: Eliot Jones, name: Ivan P. Yamshchikov, name: Pierre-Carl Langlais
Published: 2024-10-29 23:00:05

Open-source large language models are becoming increasingly available and
popular among researchers and practitioners. While significant progress has
been made on open-weight models, open training data is a practice yet to be
adopted by the leading open-weight models creators. At the same time, there
researchers are working to make language models safer. We propose a data
curation pipeline to reduce harmful outputs by models trained on public domain
data. There are unique challenges to working with public domain data, as these
sources differ from web text in both form and content. Many sources are
historical documents and are the result of Optical Character Recognition (OCR).
Consequently, current state-of-the-art approaches to toxicity filtering are
often infeasible or inappropriate for open data models. In this paper, we
introduce a new fully open-source pipeline for open-data toxicity filtering.
Our contributions are threefold. We create a custom training dataset,
ToxicCommons, which is composed of texts which have been classified across five
different dimensions (racial/origin-based, gender/sex-based, religious,
ability-based discrimination, and violence). We use this dataset to train a
custom classifier, Celadon, that can be used to detect toxic content in open
data more efficiently at a larger scale. Finally, we describe the balanced
approach to content filtration that optimizes safety filtering with respect to
the filtered data available for training.
TokenFormer: Rethinking Transformer Scaling with Tokenized Model
  Parameters
Authors: name: Haiyang Wang, name: Yue Fan, name: Muhammad Ferjad Naeem, name: Yongqin Xian, name: Jan Eric Lenssen, name: Liwei Wang, name: Federico Tombari, name: Bernt Schiele
Published: 2024-10-30 16:19:00

Transformers have become the predominant architecture in foundation models
due to their excellent performance across various domains. However, the
substantial cost of scaling these models remains a significant concern. This
problem arises primarily from their dependence on a fixed number of parameters
within linear projections. When architectural modifications (e.g., channel
dimensions) are introduced, the entire model typically requires retraining from
scratch. As model sizes continue growing, this strategy results in increasingly
high computational costs and becomes unsustainable. To overcome this problem,
we introduce TokenFormer, a natively scalable architecture that leverages the
attention mechanism not only for computations among input tokens but also for
interactions between tokens and model parameters, thereby enhancing
architectural flexibility. By treating model parameters as tokens, we replace
all the linear projections in Transformers with our token-parameter attention
layer, where input tokens act as queries and model parameters as keys and
values. This reformulation allows for progressive and efficient scaling without
necessitating retraining from scratch. Our model scales from 124M to 1.4B
parameters by incrementally adding new key-value parameter pairs, achieving
performance comparable to Transformers trained from scratch while greatly
reducing training costs. Code and models are available at
https://github.com/Haiyang-W/TokenFormer.
Stealing User Prompts from Mixture of Experts
Authors: name: Itay Yona, name: Ilia Shumailov, name: Jamie Hayes, name: Nicholas Carlini
Published: 2024-10-30 10:25:35

Mixture-of-Experts (MoE) models improve the efficiency and scalability of
dense language models by routing each token to a small number of experts in
each layer. In this paper, we show how an adversary that can arrange for their
queries to appear in the same batch of examples as a victim's queries can
exploit Expert-Choice-Routing to fully disclose a victim's prompt. We
successfully demonstrate the effectiveness of this attack on a two-layer
Mixtral model, exploiting the tie-handling behavior of the torch.topk CUDA
implementation. Our results show that we can extract the entire prompt using
O({VM}^2) queries (with vocabulary size V and prompt length M) or 100
queries on average per token in the setting we consider. This is the first
attack to exploit architectural flaws for the purpose of extracting user
prompts, introducing a new class of LLM vulnerabilities.
A Large Recurrent Action Model: xLSTM enables Fast Inference for
  Robotics Tasks
Authors: name: Thomas Schmied, name: Thomas Adler, name: Vihang Patil, name: Maximilian Beck, name: Korbinian Pöppel, name: Johannes Brandstetter, name: Günter Klambauer, name: Razvan Pascanu, name: Sepp Hochreiter
Published: 2024-10-29 17:55:47

In recent years, there has been a trend in the field of Reinforcement
Learning (RL) towards large action models trained offline on large-scale
datasets via sequence modeling. Existing models are primarily based on the
Transformer architecture, which result in powerful agents. However, due to slow
inference times, Transformer-based approaches are impractical for real-time
applications, such as robotics. Recently, modern recurrent architectures, such
as xLSTM and Mamba, have been proposed that exhibit parallelization benefits
during training similar to the Transformer architecture while offering fast
inference. In this work, we study the aptitude of these modern recurrent
architectures for large action models. Consequently, we propose a Large
Recurrent Action Model (LRAM) with an xLSTM at its core that comes with
linear-time inference complexity and natural sequence length extrapolation
abilities. Experiments on 432 tasks from 6 domains show that LRAM compares
favorably to Transformers in terms of performance and speed.
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation
  Generation
Authors: name: Yiruo Cheng, name: Kelong Mao, name: Ziliang Zhao, name: Guanting Dong, name: Hongjin Qian, name: Yongkang Wu, name: Tetsuya Sakai, name: Ji-Rong Wen, name: Zhicheng Dou
Published: 2024-10-30 15:06:32

Retrieval-Augmented Generation (RAG) has become a powerful paradigm for
enhancing large language models (LLMs) through external knowledge retrieval.
Despite its widespread attention, existing academic research predominantly
focuses on single-turn RAG, leaving a significant gap in addressing the
complexities of multi-turn conversations found in real-world applications. To
bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess
RAG systems in realistic multi-turn conversational settings. CORAL includes
diverse information-seeking conversations automatically derived from Wikipedia
and tackles key challenges such as open-domain coverage, knowledge intensity,
free-form responses, and topic shifts. It supports three core tasks of
conversational RAG: passage retrieval, response generation, and citation
labeling. We propose a unified framework to standardize various conversational
RAG methods and conduct a comprehensive evaluation of these methods on CORAL,
demonstrating substantial opportunities for improving existing approaches.
AutoMIR: Effective Zero-Shot Medical Information Retrieval without
  Relevance Labels
Authors: name: Lei Li, name: Xiangxu Zhang, name: Xiao Zhou, name: Zheng Liu
Published: 2024-10-26 02:53:20

Medical information retrieval (MIR) is essential for retrieving relevant
medical knowledge from diverse sources, including electronic health records,
scientific literature, and medical databases. However, achieving effective
zero-shot dense retrieval in the medical domain poses substantial challenges
due to the lack of relevance-labeled data. In this paper, we introduce a novel
approach called Self-Learning Hypothetical Document Embeddings (SL-HyDE) to
tackle this issue. SL-HyDE leverages large language models (LLMs) as generators
to generate hypothetical documents based on a given query. These generated
documents encapsulate key medical context, guiding a dense retriever in
identifying the most relevant documents. The self-learning framework
progressively refines both pseudo-document generation and retrieval, utilizing
unlabeled medical corpora without requiring any relevance-labeled data.
Additionally, we present the Chinese Medical Information Retrieval Benchmark
(CMIRB), a comprehensive evaluation framework grounded in real-world medical
scenarios, encompassing five tasks and ten datasets. By benchmarking ten models
on CMIRB, we establish a rigorous standard for evaluating medical information
retrieval systems. Experimental results demonstrate that SL-HyDE significantly
surpasses existing methods in retrieval accuracy while showcasing strong
generalization and scalability across various LLM and retriever configurations.
CMIRB data and evaluation code are publicly available at:
https://github.com/CMIRB-benchmark/CMIRB.
Task Vectors are Cross-Modal
Authors: name: Grace Luo, name: Trevor Darrell, name: Amir Bar
Published: 2024-10-29 17:59:45

We investigate the internal representations of vision-and-language models
(VLMs) and how they encode task representations. We consider tasks specified
through examples or instructions, using either text or image inputs.
Surprisingly, we find that conceptually similar tasks are mapped to similar
task vector representations, regardless of how they are specified. Our findings
suggest that to output answers, tokens in VLMs undergo three distinct phases:
input, task, and answer, a process which is consistent across different
modalities and specifications. The task vectors we identify in VLMs are general
enough to be derived in one modality (e.g., text) and transferred to another
(e.g., image). Additionally, we find that ensembling exemplar and instruction
based task vectors produce better task representations. Taken together, these
insights shed light on the underlying mechanisms of VLMs, particularly their
ability to represent tasks in a shared manner across different modalities and
task specifications. Project page:
https://task-vectors-are-cross-modal.github.io.
Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'
Authors: name: Shanchao Liang, name: Yiran Hu, name: Nan Jiang, name: Lin Tan
Published: 2024-10-29 01:21:05

Large language models (LLMs) have shown remarkable ability in code generation
with more than 90 pass@1 in solving Python coding problems in HumanEval and
MBPP. Such high accuracy leads to the question: can LLMs replace human
programmers? Existing manual crafted, simple, or single-line code generation
benchmarks cannot answer this question due to their gap with real-world
software development. To answer this question, we propose REPOCOD, a code
generation benchmark with 980 problems collected from 11 popular real-world
projects, with more than 58% of them requiring file-level or repository-level
context information. In addition, REPOCOD has the longest average canonical
solution length (331.6 tokens) and the highest average cyclomatic complexity
(9.00) compared to existing benchmarks. In our evaluations on ten LLMs, none of
the models can achieve more than 30 pass@1 on REPOCOD, disclosing the necessity
of building stronger LLMs that can help developers in real-world software
development.
Accelerating Direct Preference Optimization with Prefix Sharing
Authors: name: Franklin Wang, name: Sumanth Hegde
Published: 2024-10-27 02:06:17

Offline paired preference optimization algorithms have become a popular
approach for fine-tuning on preference data, outperforming traditional
supervised fine-tuning in various tasks. However, traditional implementations
often involve redundant computations, especially for tasks with long shared
prompts. We introduce prefix sharing for preference tuning, a novel technique
that processes chosen and rejected responses as one sequence with a shared
prefix. To prevent cross-response contamination, we use a custom block-sparse
attention mask. Our method achieves 1.1-1.5times improvement in training
throughput on popular DPO datasets, without any effect on convergence. When
combined with sequence packing, we observe consistent 1.3-1.6times
speedups, benefiting even datasets with smaller sequence lengths. While we
focus on Direct Preference Optimization (DPO), our approach is applicable to
other paired preference tuning methods. By enhancing computational efficiency,
our work contributes to making preference-based fine-tuning more accessible for
a wider range of applications and model sizes. We open-source our code at
https://github.com/frankxwang/dpo-prefix-sharing.
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Authors: name: Nour Jedidi, name: Yung-Sung Chuang, name: Leslie Shing, name: James Glass
Published: 2024-10-28 17:40:40

Building effective dense retrieval systems remains difficult when relevance
supervision is not available. Recent work has looked to overcome this challenge
by using a Large Language Model (LLM) to generate hypothetical documents that
can be used to find the closest real document. However, this approach relies
solely on the LLM to have domain-specific knowledge relevant to the query,
which may not be practical. Furthermore, generating hypothetical documents can
be inefficient as it requires the LLM to generate a large number of tokens for
each query. To address these challenges, we introduce Real Document Embeddings
from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF
proposes to re-frame hypothetical document generation as a relevance estimation
task, using an LLM to select which documents should be used for nearest
neighbor search. Through this re-framing, the LLM no longer needs
domain-specific knowledge but only needs to judge what is relevant.
Additionally, relevance estimation only requires the LLM to output a single
token, thereby improving search latency. Our experiments show that ReDE-RF
consistently surpasses state-of-the-art zero-shot dense retrieval methods
across a wide range of low-resource retrieval datasets while also making
significant improvements in latency per-query.
Measuring memorization through probabilistic discoverable extraction
Authors: name: Jamie Hayes, name: Marika Swanberg, name: Harsh Chaudhari, name: Itay Yona, name: Ilia Shumailov
Published: 2024-10-25 11:37:04

Large language models (LLMs) are susceptible to memorizing training data,
raising concerns due to the potential extraction of sensitive information.
Current methods to measure memorization rates of LLMs, primarily discoverable
extraction (Carlini et al., 2022), rely on single-sequence greedy sampling,
potentially underestimating the true extent of memorization. This paper
introduces a probabilistic relaxation of discoverable extraction that
quantifies the probability of extracting a target sequence within a set of
generated samples, considering various sampling schemes and multiple attempts.
This approach addresses the limitations of reporting memorization rates through
discoverable extraction by accounting for the probabilistic nature of LLMs and
user interaction patterns. Our experiments demonstrate that this probabilistic
measure can reveal cases of higher memorization rates compared to rates found
through discoverable extraction. We further investigate the impact of different
sampling schemes on extractability, providing a more comprehensive and
realistic assessment of LLM memorization and its associated risks. Our
contributions include a new probabilistic memorization definition, empirical
evidence of its effectiveness, and a thorough evaluation across different
models, sizes, sampling schemes, and training data repetitions.
RARe: Retrieval Augmented Retrieval with In-Context Examples
Authors: name: Atula Tejaswi, name: Yoonsang Lee, name: Sujay Sanghavi, name: Eunsol Choi
Published: 2024-10-26 05:46:20

We investigate whether in-context examples, widely used in decoder-only
language models (LLMs), can improve embedding model performance in retrieval
tasks. Unlike in LLMs, naively prepending in-context examples (query-document
pairs) to the target query at inference time does not work out of the box. We
introduce a simple approach to enable retrievers to use in-context examples.
Our approach, RARe, finetunes a pre-trained model with in-context examples
whose query is semantically similar to the target query. This can be applied to
adapt various base architectures (i.e., decoder-only language models, retriever
models) and consistently achieves performance gains of up to +2.72% nDCG across
various open-domain retrieval datasets (BeIR, RAR-b). In particular, we find
RARe exhibits stronger out-of-domain generalization compared to models using
queries without in-context examples, similar to what is seen for in-context
learning in LLMs. We further provide analysis on the design choices of
in-context example augmentation and lay the foundation for future work in this
space.
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on
  Tasks where Thinking Makes Humans Worse
Authors: name: Ryan Liu, name: Jiayi Geng, name: Addison J. Wu, name: Ilia Sucholutsky, name: Tania Lombrozo, name: Thomas L. Griffiths
Published: 2024-10-27 18:30:41

Chain-of-thought (CoT) prompting has become a widely used strategy for
working with large language and multimodal models. While CoT has been shown to
improve performance across many tasks, determining the settings in which it is
effective remains an ongoing effort. In particular, it is still an open
question in what settings CoT systematically reduces model performance. In this
paper, we seek to identify the characteristics of tasks where CoT reduces
performance by drawing inspiration from cognitive psychology, looking at cases
where (i) verbal thinking or deliberation hurts performance in humans, and (ii)
the constraints governing human performance generalize to language models.
Three such cases are implicit statistical learning, visual recognition, and
classifying with patterns containing exceptions. In extensive experiments
across all three settings, we find that a diverse collection of
state-of-the-art models exhibit significant drop-offs in performance (e.g., up
to 36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o) when using
inference-time reasoning compared to zero-shot counterparts. We also identify
three tasks that satisfy condition (i) but not (ii), and find that while verbal
thinking reduces human performance in these tasks, CoT retains or increases
model performance. Overall, our results show that while there is not an exact
parallel between the cognitive processes of models and those of humans,
considering cases where thinking has negative consequences for human
performance can help us identify settings where it negatively impacts models.
By connecting the literature on human deliberation with evaluations of CoT, we
offer a new tool that can be used in understanding the impact of prompt choices
and inference-time reasoning.
CLEAR: Character Unlearning in Textual and Visual Modalities
Authors: name: Alexey Dontsov, name: Dmitrii Korzh, name: Alexey Zhavoronkin, name: Boris Mikheev, name: Denis Bobkov, name: Aibek Alanov, name: Oleg Y. Rogov, name: Ivan Oseledets, name: Elena Tutubalina
Published: 2024-10-23 17:30:50

Machine Unlearning (MU) is critical for enhancing privacy and security in
deep learning models, particularly in large multimodal language models (MLLMs),
by removing specific private or hazardous information. While MU has made
significant progress in textual and visual modalities, multimodal unlearning
(MMU) remains significantly underexplored, partially due to the absence of a
suitable open-source benchmark. To address this, we introduce CLEAR, a new
benchmark designed to evaluate MMU methods. CLEAR contains 200 fictitious
individuals and 3,700 images linked with corresponding question-answer pairs,
enabling a thorough evaluation across modalities. We assess 10 MU methods,
adapting them for MMU, and highlight new challenges specific to multimodal
forgetting. We also demonstrate that simple ell_1 regularization on LoRA
weights significantly mitigates catastrophic forgetting, preserving model
performance on retained data. The dataset is available at
https://huggingface.co/datasets/therem/CLEAR
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World
  Exploration, Feedback and Optimization
Authors: name: Hongliang He, name: Wenlin Yao, name: Kaixin Ma, name: Wenhao Yu, name: Hongming Zhang, name: Tianqing Fang, name: Zhenzhong Lan, name: Dong Yu
Published: 2024-10-25 15:01:27

The rapid development of large language and multimodal models has sparked
significant interest in using proprietary models, such as GPT-4o, to develop
autonomous agents capable of handling real-world scenarios like web navigation.
Although recent open-source efforts have tried to equip agents with the ability
to explore environments and continuously improve over time, they are building
text-only agents in synthetic environments where the reward signals are clearly
defined. Such agents struggle to generalize to realistic settings that require
multimodal perception abilities and lack ground-truth signals. In this paper,
we introduce an open-source framework designed to facilitate the development of
multimodal web agent that can autonomously conduct real-world exploration and
improve itself. We first train the base model with imitation learning to gain
the basic abilities. We then let the agent explore the open web and collect
feedback on its trajectories. After that, it further improves its policy by
learning from well-performing trajectories judged by another general-purpose
model. This exploration-feedback-optimization cycle can continue for several
iterations. Experimental results show that our web agent successfully improves
itself after each iteration, demonstrating strong performance across multiple
test sets.
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science
  Competitions
Authors: name: Ziming Li, name: Qianbo Zang, name: David Ma, name: Jiawei Guo, name: Tuney Zheng, name: Minghao Liu, name: Xinyao Niu, name: Yue Wang, name: Jian Yang, name: Jiaheng Liu, name: Wanjun Zhong, name: Wangchunshu Zhou, name: Wenhao Huang, name: Ge Zhang
Published: 2024-10-27 12:44:25

Data science tasks involving tabular data present complex challenges that
require sophisticated problem-solving approaches. We propose AutoKaggle, a
powerful and user-centric framework that assists data scientists in completing
daily data pipelines through a collaborative multi-agent system. AutoKaggle
implements an iterative development process that combines code execution,
debugging, and comprehensive unit testing to ensure code correctness and logic
consistency. The framework offers highly customizable workflows, allowing users
to intervene at each phase, thus integrating automated intelligence with human
expertise. Our universal data science toolkit, comprising validated functions
for data cleaning, feature engineering, and modeling, forms the foundation of
this solution, enhancing productivity by streamlining common tasks. We selected
8 Kaggle competitions to simulate data processing workflows in real-world
application scenarios. Evaluation results demonstrate that AutoKaggle achieves
a validation submission rate of 0.85 and a comprehensive score of 0.82 in
typical data science pipelines, fully proving its effectiveness and
practicality in handling complex data science tasks.