Orionnnn/hub-stats huggingface dataset:
From Commands to Prompts: LLM-based Semantic File System for AIOS
Authors: name: Zeru Shi, name: Kai Mei, name: Mingyu Jin, name: Yongye Su, name: Chaoji Zuo, name: Wenyue Hua, name: Wujiang Xu, name: Yujie Ren, name: Zirui Liu, name: Mengnan Du, name: Dong Deng, name: Yongfeng Zhang
Published: 2024-09-23 08:39:16

Large language models (LLMs) have demonstrated significant potential in the
development of intelligent applications and systems such as LLM-based agents
and agent operating systems (AIOS). However, when these applications and
systems interact with the underlying file system, the file system still remains
the traditional paradigm: reliant on manual navigation through precise
commands. This paradigm poses a bottleneck to the usability of these systems as
users are required to navigate complex folder hierarchies and remember cryptic
file names. To address this limitation, we propose an LLM-based semantic file
system ( LSFS ) for prompt-driven file management. Unlike conventional
approaches, LSFS incorporates LLMs to enable users or agents to interact with
files through natural language prompts, facilitating semantic file management.
At the macro-level, we develop a comprehensive API set to achieve semantic file
management functionalities, such as semantic file retrieval, file update
monitoring and summarization, and semantic file rollback). At the micro-level,
we store files by constructing semantic indexes for them, design and implement
syscalls of different semantic operations (e.g., CRUD, group by, join) powered
by vector database. Our experiments show that LSFS offers significant
improvements over traditional file systems in terms of user convenience, the
diversity of supported functions, and the accuracy and efficiency of file
operations. Additionally, with the integration of LLM, our system enables more
intelligent file management tasks, such as content summarization and version
comparison, further enhancing its capabilities.
FLARE: Faithful Logic-Aided Reasoning and Exploration
Authors: name: Erik Arakelyan, name: Pasquale Minervini, name: Pat Verga, name: Patrick Lewis, name: Isabelle Augenstein
Published: 2024-10-14 19:39:11

Modern Question Answering (QA) and Reasoning approaches based on Large
Language Models (LLMs) commonly use prompting techniques, such as
Chain-of-Thought (CoT), assuming the resulting generation will have a more
granular exploration and reasoning over the question space and scope. However,
such methods struggle with generating outputs that are faithful to the
intermediate chain of reasoning produced by the model. On the other end of the
spectrum, neuro-symbolic methods such as Faithful CoT (F-CoT) propose to
combine LLMs with external symbolic solvers. While such approaches boast a high
degree of faithfulness, they usually require a model trained for code
generation and struggle with tasks that are ambiguous or hard to formalise
strictly. We introduce Faithful Logic-Aided
Reasoning and Exploration (\ours), a novel
interpretable approach for traversing the problem space using task
decompositions. We use the LLM to plan a solution, soft-formalise the query
into facts and predicates using a logic programming code and simulate that code
execution using an exhaustive multi-hop search over the defined space. Our
method allows us to compute the faithfulness of the reasoning process w.r.t.
the generated code and analyse the steps of the multi-hop search without
relying on external solvers. Our methods achieve SOTA results on 7
out of 9 diverse reasoning benchmarks. We also show that model
faithfulness positively correlates with overall performance and further
demonstrate that {\ours} allows pinpointing the decisive factors
sufficient for and leading to the correct answer with optimal reasoning during
the multi-hop search.
Taming Overconfidence in LLMs: Reward Calibration in RLHF
Authors: name: Jixuan Leng, name: Chengsong Huang, name: Banghua Zhu, name: Jiaxin Huang
Published: 2024-10-13 04:48:40

Language model calibration refers to the alignment between the confidence of
the model and the actual performance of its responses. While previous studies
point out the overconfidence phenomenon in Large Language Models (LLMs) and
show that LLMs trained with Reinforcement Learning from Human Feedback (RLHF)
are overconfident with a more sharpened output probability, in this study, we
reveal that RLHF tends to lead models to express verbalized overconfidence in
their own responses. We investigate the underlying cause of this overconfidence
and demonstrate that reward models used for Proximal Policy Optimization (PPO)
exhibit inherent biases towards high-confidence scores regardless of the actual
quality of responses. Building upon this insight, we propose two PPO variants:
PPO-M: PPO with Calibrated Reward Modeling and PPO-C: PPO with Calibrated
Reward Calculation. PPO-M integrates explicit confidence scores in reward model
training, which calibrates reward models to better capture the alignment
between response quality and verbalized confidence. PPO-C adjusts the reward
score during PPO based on the difference between the current reward and the
moving average of past rewards. Both PPO-M and PPO-C can be seamlessly
integrated into the current PPO pipeline and do not require additional golden
labels. We evaluate our methods on both Llama3-8B and Mistral-7B across six
diverse datasets including multiple-choice and open-ended generation.
Experiment results demonstrate that both of our methods can reduce calibration
error and maintain performance comparable to standard PPO. We further show that
they do not compromise model capabilities in open-ended conversation settings.
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified
  Perspective
Authors: name: Yongxin Zhu, name: Bocheng Li, name: Hang Zhang, name: Xin Li, name: Linli Xu, name: Lidong Bing
Published: 2024-10-16 12:13:17

Latent-based image generative models, such as Latent Diffusion Models (LDMs)
and Mask Image Models (MIMs), have achieved notable success in image generation
tasks. These models typically leverage reconstructive autoencoders like VQGAN
or VAE to encode pixels into a more compact latent space and learn the data
distribution in the latent space instead of directly from pixels. However, this
practice raises a pertinent question: Is it truly the optimal choice? In
response, we begin with an intriguing observation: despite sharing the same
latent space, autoregressive models significantly lag behind LDMs and MIMs in
image generation. This finding contrasts sharply with the field of NLP, where
the autoregressive model GPT has established a commanding presence. To address
this discrepancy, we introduce a unified perspective on the relationship
between latent space and generative models, emphasizing the stability of latent
space in image generative modeling. Furthermore, we propose a simple but
effective discrete image tokenizer to stabilize the latent space for image
generative modeling. Experimental results show that image autoregressive
modeling with our tokenizer (DiGIT) benefits both image understanding and image
generation with the next token prediction principle, which is inherently
straightforward for GPT models but challenging for other generative models.
Remarkably, for the first time, a GPT-style autoregressive model for images
outperforms LDMs, which also exhibits substantial improvement akin to GPT when
scaling up model size. Our findings underscore the potential of an optimized
latent space and the integration of discrete tokenization in advancing the
capabilities of image generative models. The code is available at
https://github.com/DAMO-NLP-SG/DiGIT.
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in
  Multiple Domains
Authors: name: Yein Park, name: Chanwoong Yoon, name: Jungwoo Park, name: Donghyeon Lee, name: Minbyul Jeong, name: Jaewoo Kang
Published: 2024-10-13 15:08:49

Large language models (LLMs) have significantly impacted many aspects of our
lives. However, assessing and ensuring their chronological knowledge remains
challenging. Existing approaches fall short in addressing the accumulative
nature of knowledge, often relying on a single time stamp. To overcome this, we
introduce ChroKnowBench, a benchmark dataset designed to evaluate
chronologically accumulated knowledge across three key aspects: multiple
domains, time dependency, temporal state. Our benchmark distinguishes between
knowledge that evolves (e.g., scientific discoveries, amended laws) and
knowledge that remain constant (e.g., mathematical truths, commonsense facts).
Building on this benchmark, we present ChroKnowledge (Chronological
Categorization of Knowledge), a novel sampling-based framework for evaluating
and updating LLMs' non-parametric chronological knowledge. Our evaluation
shows: (1) The ability of eliciting temporal knowledge varies depending on the
data format that model was trained on. (2) LLMs partially recall knowledge or
show a cut-off at temporal boundaries rather than recalling all aspects of
knowledge correctly. Thus, we apply our ChroKnowPrompt, an in-depth prompting
to elicit chronological knowledge by traversing step-by-step through the
surrounding time spans. We observe that our framework successfully updates the
overall knowledge across the entire timeline in both the biomedical domain
(+11.9%) and the general domain (+2.8%), demonstrating its effectiveness in
refining temporal knowledge. This non-parametric approach also enables
knowledge updates not only in open-source models but also in proprietary LLMs,
ensuring comprehensive applicability across model types. We perform a
comprehensive analysis based on temporal characteristics of ChroKnowPrompt and
validate the potential of various models to elicit intrinsic temporal knowledge
through our method.
Controllable Safety Alignment: Inference-Time Adaptation to Diverse
  Safety Requirements
Authors: name: Jingyu Zhang, name: Ahmed Elgohary, name: Ahmed Magooda, name: Daniel Khashabi, name: Benjamin Van Durme
Published: 2024-10-11 16:38:01

The current paradigm for safety alignment of large language models (LLMs)
follows a one-size-fits-all approach: the model refuses to interact with any
content deemed unsafe by the model provider. This approach lacks flexibility in
the face of varying social norms across cultures and regions. In addition,
users may have diverse safety needs, making a model with static safety
standards too restrictive to be useful, as well as too costly to be re-aligned.
  We propose Controllable Safety Alignment (CoSA), a framework designed to
adapt models to diverse safety requirements without re-training. Instead of
aligning a fixed model, we align models to follow safety configs -- free-form
natural language descriptions of the desired safety behaviors -- that are
provided as part of the system prompt. To adjust model safety behavior,
authorized users only need to modify such safety configs at inference time. To
enable that, we propose CoSAlign, a data-centric method for aligning LLMs to
easily adapt to diverse safety configs. Furthermore, we devise a novel
controllability evaluation protocol that considers both helpfulness and
configured safety, summarizing them into CoSA-Score, and construct CoSApien, a
human-authored benchmark that consists of real-world LLM use cases with diverse
safety requirements and corresponding evaluation prompts.
  We show that CoSAlign leads to substantial gains of controllability over
strong baselines including in-context alignment. Our framework encourages
better representation and adaptation to pluralistic human values in LLMs, and
thereby increasing their practicality.
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Authors: name: Thong Nguyen, name: Shubham Chatterjee, name: Sean MacAvaney, name: Iain Mackie, name: Jeff Dalton, name: Andrew Yates
Published: 2024-10-10 08:41:34

Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained
transformers, which often split entities into nonsensical fragments. Splitting
entities can reduce retrieval accuracy and limits the model's ability to
incorporate up-to-date world knowledge not included in the training data. In
this work, we enhance the LSR vocabulary with Wikipedia concepts and entities,
enabling the model to resolve ambiguities more effectively and stay current
with evolving knowledge. Central to our approach is a Dynamic Vocabulary (DyVo)
head, which leverages existing entity embeddings and an entity retrieval
component that identifies entities relevant to a query or document. We use the
DyVo head to generate entity weights, which are then merged with word piece
weights to create joint representations for efficient indexing and retrieval
using an inverted index. In experiments across three entity-rich document
ranking datasets, the resulting DyVo model substantially outperforms
state-of-the-art baselines.
Tracking Universal Features Through Fine-Tuning and Model Merging
Authors: name: Niels Horn, name: Desmond Elliott
Published: 2024-10-16 09:18:39

We study how features emerge, disappear, and persist across models fine-tuned
on different domains of text. More specifically, we start from a base one-layer
Transformer language model that is trained on a combination of the BabyLM
corpus, and a collection of Python code from The Stack. This base model is
adapted to two new domains of text: TinyStories, and the Lua programming
language, respectively; and then these two models are merged using these two
models using spherical linear interpolation. Our exploration aims to provide
deeper insights into the stability and transformation of features across
typical transfer-learning scenarios using small-scale models and sparse
auto-encoders.
Insights from the Inverse: Reconstructing LLM Training Goals Through
  Inverse RL
Authors: name: Jared Joselowitz, name: Arjun Jagota, name: Satyapriya Krishna, name: Sonali Parbhoo
Published: 2024-10-16 12:14:25

Large language models (LLMs) trained with Reinforcement Learning from Human
Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying
reward functions and decision-making processes remain opaque. This paper
introduces a novel approach to interpreting LLMs by applying inverse
reinforcement learning (IRL) to recover their implicit reward functions. We
conduct experiments on toxicity-aligned LLMs of varying sizes, extracting
reward models that achieve up to 80.40% accuracy in predicting human
preferences. Our analysis reveals key insights into the non-identifiability of
reward functions, the relationship between model size and interpretability, and
potential pitfalls in the RLHF process. We demonstrate that IRL-derived reward
models can be used to fine-tune new LLMs, resulting in comparable or improved
performance on toxicity benchmarks. This work provides a new lens for
understanding and improving LLM alignment, with implications for the
responsible development and deployment of these powerful systems.
Neural Metamorphosis
Authors: name: Xingyi Yang, name: Xinchao Wang
Published: 2024-10-10 14:49:58

This paper introduces a new learning paradigm termed Neural Metamorphosis
(NeuMeta), which aims to build self-morphable neural networks. Contrary to
crafting separate models for different architectures or sizes, NeuMeta directly
learns the continuous weight manifold of neural networks. Once trained, we can
sample weights for any-sized network directly from the manifold, even for
previously unseen configurations, without retraining. To achieve this ambitious
goal, NeuMeta trains neural implicit functions as hypernetworks. They accept
coordinates within the model space as input, and generate corresponding weight
values on the manifold. In other words, the implicit function is learned in a
way, that the predicted weights is well-performed across various models sizes.
In training those models, we notice that, the final performance closely relates
on smoothness of the learned manifold. In pursuit of enhancing this smoothness,
we employ two strategies. First, we permute weight matrices to achieve
intra-model smoothness, by solving the Shortest Hamiltonian Path problem.
Besides, we add a noise on the input coordinates when training the implicit
function, ensuring models with various sizes shows consistent outputs. As such,
NeuMeta shows promising results in synthesizing parameters for various network
configurations. Our extensive tests in image classification, semantic
segmentation, and image generation reveal that NeuMeta sustains full-size
performance even at a 75% compression rate.
Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
Authors: name: Cheng Lu, name: Yang Song
Published: 2024-10-14 20:43:25

Consistency models (CMs) are a powerful class of diffusion-based generative
models optimized for fast sampling. Most existing CMs are trained using
discretized timesteps, which introduce additional hyperparameters and are prone
to discretization errors. While continuous-time formulations can mitigate these
issues, their success has been limited by training instability. To address
this, we propose a simplified theoretical framework that unifies previous
parameterizations of diffusion models and CMs, identifying the root causes of
instability. Based on this analysis, we introduce key improvements in diffusion
process parameterization, network architecture, and training objectives. These
changes enable us to train continuous-time CMs at an unprecedented scale,
reaching 1.5B parameters on ImageNet 512x512. Our proposed training algorithm,
using only two sampling steps, achieves FID scores of 2.06 on CIFAR-10, 1.48 on
ImageNet 64x64, and 1.88 on ImageNet 512x512, narrowing the gap in FID scores
with the best existing diffusion models to within 10%.
WorldMedQA-V: a multilingual, multimodal medical examination dataset for
  multimodal language models evaluation
Authors: name: João Matos, name: Shan Chen, name: Siena Placino, name: Yingya Li, name: Juan Carlos Climent Pardo, name: Daphna Idan, name: Takeshi Tohyama, name: David Restrepo, name: Luis F. Nakayama, name: Jose M. M. Pascual-Leone, name: Guergana Savova, name: Hugo Aerts, name: Leo A. Celi, name: A. Ian Wong, name: Danielle S. Bitterman, name: Jack Gallifant
Published: 2024-10-16 16:31:24

Multimodal/vision language models (VLMs) are increasingly being deployed in
healthcare settings worldwide, necessitating robust benchmarks to ensure their
safety, efficacy, and fairness. Multiple-choice question and answer (QA)
datasets derived from national medical examinations have long served as
valuable evaluation tools, but existing datasets are largely text-only and
available in a limited subset of languages and countries. To address these
challenges, we present WorldMedQA-V, an updated multilingual, multimodal
benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V
includes 568 labeled multiple-choice QAs paired with 568 medical images from
four countries (Brazil, Israel, Japan, and Spain), covering original languages
and validated English translations by native clinicians, respectively. Baseline
performance for common open- and closed-source models are provided in the local
language and English translations, and with and without images provided to the
model. The WorldMedQA-V benchmark aims to better match AI systems to the
diverse healthcare environments in which they are deployed, fostering more
equitable, effective, and representative applications.
Large Language Model Evaluation via Matrix Nuclear-Norm
Authors: name: Yahan Li, name: Tingyu Xia, name: Yi Chang, name: Yuan Wu
Published: 2024-10-14 16:15:57

As large language models (LLMs) continue to evolve, efficient evaluation
metrics are vital for assessing their ability to compress information and
reduce redundancy. While traditional metrics like Matrix Entropy offer valuable
insights, they are computationally intensive for large-scale models due to
their \( O(n^3) \) time complexity with Singular Value Decomposition (SVD). To
mitigate this issue, we introduce the Matrix Nuclear-Norm, which not only
serves as a metric to quantify the data compression proficiency of LLM but also
provides a convex approximation of matrix rank to capture both predictive
discriminability and diversity. By employing the \( L_{1,2}-norm \) to
further approximate the nuclear norm, we can effectively assess the model's
information compression capabilities. This approach reduces the time complexity
to \( O(n^2) \) and eliminates the need for SVD computation. Consequently, the
Matrix Nuclear-Norm achieves speeds 8 to 24 times faster than Matrix Entropy
for the CEREBRAS-GPT model as sizes increase from 111M to 6.7B. This
performance gap becomes more pronounced with larger models, as validated in
tests with other models like Pythia. Additionally, evaluations on benchmarks
and model responses confirm that our proposed Matrix Nuclear-Norm is a
reliable, scalable, and efficient tool for assessing LLMs' performance,
striking a balance between accuracy and computational efficiency. The code is
available at https://github.com/MLGroupJLU/MatrixNuclearNorm.
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for
  Embodied AI
Authors: name: Sijie Cheng, name: Kechen Fang, name: Yangyang Yu, name: Sicheng Zhou, name: Bohao Li, name: Ye Tian, name: Tingguang Li, name: Lei Han, name: Yang Liu
Published: 2024-10-15 14:08:53

Recent advancements in Multi-modal Large Language Models (MLLMs) have opened
new avenues for applications in Embodied AI. Building on previous work,
EgoThink, we introduce VidEgoThink, a comprehensive benchmark for evaluating
egocentric video understanding capabilities. To bridge the gap between MLLMs
and low-level control in Embodied AI, we design four key interrelated tasks:
video question-answering, hierarchy planning, visual grounding and reward
modeling. To minimize manual annotation costs, we develop an automatic data
generation pipeline based on the Ego4D dataset, leveraging the prior knowledge
and multimodal capabilities of GPT-4o. Three human annotators then filter the
generated data to ensure diversity and quality, resulting in the VidEgoThink
benchmark. We conduct extensive experiments with three types of models:
API-based MLLMs, open-source image-based MLLMs, and open-source video-based
MLLMs. Experimental results indicate that all MLLMs, including GPT-4o, perform
poorly across all tasks related to egocentric video understanding. These
findings suggest that foundation models still require significant advancements
to be effectively applied to first-person scenarios in Embodied AI. In
conclusion, VidEgoThink reflects a research trend towards employing MLLMs for
egocentric vision, akin to human capabilities, enabling active observation and
interaction in the complex real-world environments.