Orionnnn/hub-stats huggingface dataset:
Understanding and Diagnosing Deep Reinforcement Learning
Authors: name: Ezgi Korkmaz
Published: 2024-06-23 18:10:16

Deep neural policies have recently been installed in a diverse range of
settings, from biotechnology to automated financial systems. However, the
utilization of deep neural networks to approximate the value function leads to
concerns on the decision boundary stability, in particular, with regard to the
sensitivity of policy decision making to indiscernible, non-robust features due
to highly non-convex and complex deep neural manifolds. These concerns
constitute an obstruction to understanding the reasoning made by deep neural
policies, and their foundational limitations. Hence, it is crucial to develop
techniques that aim to understand the sensitivities in the learnt
representations of neural network policies. To achieve this we introduce a
theoretically founded method that provides a systematic analysis of the
unstable directions in the deep neural policy decision boundary across both
time and space. Through experiments in the Arcade Learning Environment (ALE),
we demonstrate the effectiveness of our technique for identifying correlated
directions of instability, and for measuring how sample shifts remold the set
of sensitive directions in the neural policy landscape. Most importantly, we
demonstrate that state-of-the-art robust training techniques yield learning of
disjoint unstable directions, with dramatically larger oscillations over time,
when compared to standard training. We believe our results reveal the
fundamental properties of the decision process made by reinforcement learning
policies, and can help in constructing reliable and robust deep neural
policies.
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large
  Language Models
Authors: name: Wenhao Shi, name: Zhiqiang Hu, name: Yi Bin, name: Junhua Liu, name: Yang Yang, name: See-Kiong Ng, name: Lidong Bing, name: Roy Ka-Wei Lee
Published: 2024-06-25 05:43:21

Large language models (LLMs) have demonstrated impressive reasoning
capabilities, particularly in textual mathematical problem-solving. However,
existing open-source image instruction fine-tuning datasets, containing limited
question-answer pairs per image, do not fully exploit visual information to
enhance the multimodal mathematical reasoning capabilities of Multimodal LLMs
(MLLMs). To bridge this gap, we address the lack of high-quality, diverse
multimodal mathematical datasets by collecting 40K high-quality images with
question-answer pairs from 24 existing datasets and synthesizing 320K new
pairs, creating the MathV360K dataset, which enhances both the breadth and
depth of multimodal mathematical questions. We introduce Math-LLaVA, a
LLaVA-1.5-based model fine-tuned with MathV360K. This novel approach
significantly improves the multimodal mathematical reasoning capabilities of
LLaVA-1.5, achieving a 19-point increase and comparable performance to GPT-4V
on MathVista's minitest split. Furthermore, Math-LLaVA demonstrates enhanced
generalizability, showing substantial improvements on the MMMU benchmark. Our
research highlights the importance of dataset diversity and synthesis in
advancing MLLMs' mathematical reasoning abilities. The code and data are
available at: https://github.com/HZQ950419/Math-LLaVA.
ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of
  Text-to-Time-lapse Video Generation
Authors: name: Shenghai Yuan, name: Jinfa Huang, name: Yongqi Xu, name: Yaoyang Liu, name: Shaofeng Zhang, name: Yujun Shi, name: Ruijie Zhu, name: Xinhua Cheng, name: Jiebo Luo, name: Li Yuan
Published: 2024-06-26 17:50:47

We propose a novel text-to-video (T2V) generation benchmark,
ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the
T2V models (e.g. Sora and Lumiere) in time-lapse video generation. In contrast
to existing benchmarks that focus on the visual quality and textual relevance
of generated videos, ChronoMagic-Bench focuses on the model's ability to
generate time-lapse videos with significant metamorphic amplitude and temporal
coherence. The benchmark probes T2V models for their physics, biology, and
chemistry capabilities, in a free-form text query. For these purposes,
ChronoMagic-Bench introduces 1,649 prompts and real-world videos as references,
categorized into four major types of time-lapse videos: biological,
human-created, meteorological, and physical phenomena, which are further
divided into 75 subcategories. This categorization comprehensively evaluates
the model's capacity to handle diverse and complex transformations. To
accurately align human preference with the benchmark, we introduce two new
automatic metrics, MTScore and CHScore, to evaluate the videos' metamorphic
attributes and temporal coherence. MTScore measures the metamorphic amplitude,
reflecting the degree of change over time, while CHScore assesses the temporal
coherence, ensuring the generated videos maintain logical progression and
continuity. Based on the ChronoMagic-Bench, we conduct comprehensive manual
evaluations of ten representative T2V models, revealing their strengths and
weaknesses across different categories of prompts, and providing a thorough
evaluation framework that addresses current gaps in video generation research.
Moreover, we create a large-scale ChronoMagic-Pro dataset, containing 460k
high-quality pairs of 720p time-lapse videos and detailed captions ensuring
high physical pertinence and large metamorphic amplitude.
MemServe: Context Caching for Disaggregated LLM Serving with Elastic
  Memory Pool
Authors: name: Cunchen Hu, name: Heyang Huang, name: Junhao Hu, name: Jiang Xu, name: Xusheng Chen, name: Tao Xie, name: Chenxi Wang, name: Sa Wang, name: Yungang Bao, name: Ninghui Sun, name: Yizhou Shan
Published: 2024-06-25 14:02:08

Large language model (LLM) serving has transformed from stateless to stateful
systems, utilizing techniques like context caching and disaggregated inference.
These optimizations extend the lifespan and domain of the KV cache,
necessitating a new architectural approach. We present MemServe, a unified
system that integrates both inter-request and intra-request optimizations.
MemServe introduces MemPool, an elastic memory pool managing distributed memory
and KV caches across serving instances. Using MemPool APIs, MemServe combines
context caching with disaggregated inference for the first time, supported by a
global scheduler that enhances cache reuse through a global prompt tree-based
locality-aware policy. Tests show that MemServe significantly improves job
completion time and time-to-first-time.
MatchTime: Towards Automatic Soccer Game Commentary Generation
Authors: name: Jiayuan Rao, name: Haoning Wu, name: Chang Liu, name: Yanfeng Wang, name: Weidi Xie
Published: 2024-06-26 17:57:25

Soccer is a globally popular sport with a vast audience, in this paper, we
consider constructing an automatic soccer game commentary model to improve the
audiences' viewing experience. In general, we make the following contributions:
First, observing the prevalent video-text misalignment in existing datasets, we
manually annotate timestamps for 49 matches, establishing a more robust
benchmark for soccer game commentary generation, termed as
SN-Caption-test-align; Second, we propose a multi-modal temporal alignment
pipeline to automatically correct and filter the existing dataset at scale,
creating a higher-quality soccer game commentary dataset for training, denoted
as MatchTime; Third, based on our curated dataset, we train an automatic
commentary generation model, named MatchVoice. Extensive experiments and
ablation studies have demonstrated the effectiveness of our alignment pipeline,
and training model on the curated datasets achieves state-of-the-art
performance for commentary generation, showcasing that better alignment can
lead to significant performance improvements in downstream tasks.
A Closer Look into Mixture-of-Experts in Large Language Models
Authors: name: Ka Man Lo, name: Zeyu Huang, name: Zihan Qiu, name: Zili Wang, name: Jie Fu
Published: 2024-06-26 10:07:57

Mixture-of-experts (MoE) is gaining increasing attention due to its unique
properties and remarkable performance, especially for language tasks. By
sparsely activating a subset of parameters for each token, MoE architecture
could increase the model size without sacrificing computational efficiency,
achieving a better trade-off between performance and training costs. However,
the underlying mechanism of MoE still lacks further exploration, and its
modularization degree remains questionable. In this paper, we make an initial
attempt to understand the inner workings of MoE-based large language models.
Concretely, we comprehensively study the parametric and behavioral features of
three recent MoE-based models and reveal some intriguing observations,
including (1) Neurons act like fine-grained experts. (2) The router of MoE
usually selects experts with larger output norms. (3) The expert diversity
increases as the layer increases, while the last layer is an outlier. Based on
the observations, we also provide suggestions for a broad spectrum of MoE
practitioners, such as router design and expert allocation. We hope this work
could shed light on future research on the MoE framework and other modular
architectures. Code is available at
https://github.com/kamanphoebe/Look-into-MoEs.
Adam-mini: Use Fewer Learning Rates To Gain More
Authors: name: Yushun Zhang, name: Congliang Chen, name: Ziniu Li, name: Tian Ding, name: Chenwei Wu, name: Yinyu Ye, name: Zhi-Quan Luo, name: Ruoyu Sun
Published: 2024-06-24 16:56:41

We propose Adam-mini, an optimizer that achieves on-par or better performance
than AdamW with 45% to 50% less memory footprint. Adam-mini reduces memory by
cutting down the learning rate resources in Adam (i.e., 1/v). We find
that geq 90% of these learning rates in v could be harmlessly removed if
we (1) carefully partition the parameters into blocks following our proposed
principle on Hessian structure; (2) assign a single but good learning rate to
each parameter block. We further find that, for each of these parameter blocks,
there exists a single high-quality learning rate that can outperform Adam,
provided that sufficient resources are available to search it out. We then
provide one cost-effective way to find good learning rates and propose
Adam-mini. Empirically, we verify that Adam-mini performs on par or better than
AdamW on various language models sized from 125M to 7B for pre-training,
supervised fine-tuning, and RLHF. The reduced memory footprint of Adam-mini
also alleviates communication overheads among GPUs and CPUs, thereby increasing
throughput. For instance, Adam-mini achieves 49.6% higher throughput than AdamW
when pre-training Llama2-7B on 2times A800-80GB GPUs, which saves 33%
wall-clock time for pre-training.
WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks,
  and Refusals of LLMs
Authors: name: Seungju Han, name: Kavel Rao, name: Allyson Ettinger, name: Liwei Jiang, name: Bill Yuchen Lin, name: Nathan Lambert, name: Yejin Choi, name: Nouha Dziri
Published: 2024-06-26 16:58:20

We introduce WildGuard -- an open, light-weight moderation tool for LLM
safety that achieves three goals: (1) identifying malicious intent in user
prompts, (2) detecting safety risks of model responses, and (3) determining
model refusal rate. Together, WildGuard serves the increasing needs for
automatic safety moderation and evaluation of LLM interactions, providing a
one-stop tool with enhanced accuracy and broad coverage across 13 risk
categories. While existing open moderation tools such as Llama-Guard2 score
reasonably well in classifying straightforward model interactions, they lag far
behind a prompted GPT-4, especially in identifying adversarial jailbreaks and
in evaluating models' refusals, a key measure for evaluating safety behaviors
in model responses.
  To address these challenges, we construct WildGuardMix, a large-scale and
carefully balanced multi-task safety moderation dataset with 92K labeled
examples that cover vanilla (direct) prompts and adversarial jailbreaks, paired
with various refusal and compliance responses. WildGuardMix is a combination of
WildGuardTrain, the training data of WildGuard, and WildGuardTest, a
high-quality human-annotated moderation test set with 5K labeled items covering
broad risk scenarios. Through extensive evaluations on WildGuardTest and ten
existing public benchmarks, we show that WildGuard establishes state-of-the-art
performance in open-source safety moderation across all the three tasks
compared to ten strong existing open-source moderation models (e.g., up to
26.4% improvement on refusal detection). Importantly, WildGuard matches and
sometimes exceeds GPT-4 performance (e.g., up to 3.9% improvement on prompt
harmfulness identification). WildGuard serves as a highly effective safety
moderator in an LLM interface, reducing the success rate of jailbreak attacks
from 79.8% to 2.4%.
EHRCon: Dataset for Checking Consistency between Unstructured Notes and
  Structured Tables in Electronic Health Records
Authors: name: Yeonsu Kwon, name: Jiho Kim, name: Gyubok Lee, name: Seongsu Bae, name: Daeun Kyung, name: Wonchul Cha, name: Tom Pollard, name: Alistair Johnson, name: Edward Choi
Published: 2024-06-24 06:26:50

Electronic Health Records (EHRs) are integral for storing comprehensive
patient medical records, combining structured data (e.g., medications) with
detailed clinical notes (e.g., physician notes). These elements are essential
for straightforward data retrieval and provide deep, contextual insights into
patient care. However, they often suffer from discrepancies due to unintuitive
EHR system designs and human errors, posing serious risks to patient safety. To
address this, we developed EHRCon, a new dataset and task specifically designed
to ensure data consistency between structured tables and unstructured notes in
EHRs. EHRCon was crafted in collaboration with healthcare professionals using
the MIMIC-III EHR dataset, and includes manual annotations of 3,943 entities
across 105 clinical notes checked against database entries for consistency.
EHRCon has two versions, one using the original MIMIC-III schema, and another
using the OMOP CDM schema, in order to increase its applicability and
generalizability. Furthermore, leveraging the capabilities of large language
models, we introduce CheckEHR, a novel framework for verifying the consistency
between clinical notes and database tables. CheckEHR utilizes an eight-stage
process and shows promising results in both few-shot and zero-shot settings.
The code is available at https://github.com/dustn1259/EHRCon.
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal
  LLMs
Authors: name: Zirui Wang, name: Mengzhou Xia, name: Luxi He, name: Howard Chen, name: Yitao Liu, name: Richard Zhu, name: Kaiqu Liang, name: Xindi Wu, name: Haotian Liu, name: Sadhika Malladi, name: Alexis Chevalier, name: Sanjeev Arora, name: Danqi Chen
Published: 2024-06-26 17:50:11

Chart understanding plays a pivotal role when applying Multimodal Large
Language Models (MLLMs) to real-world tasks such as analyzing scientific papers
or financial reports. However, existing datasets often focus on oversimplified
and homogeneous charts with template-based questions, leading to an
over-optimistic measure of progress. We demonstrate that although open-source
models can appear to outperform strong proprietary models on these benchmarks,
a simple stress test with slightly different charts or questions can
deteriorate performance by up to 34.5%. In this work, we propose CharXiv, a
comprehensive evaluation suite involving 2,323 natural, challenging, and
diverse charts from arXiv papers. CharXiv includes two types of questions: 1)
descriptive questions about examining basic chart elements and 2) reasoning
questions that require synthesizing information across complex visual elements
in the chart. To ensure quality, all charts and questions are handpicked,
curated, and verified by human experts. Our results reveal a substantial,
previously underestimated gap between the reasoning skills of the strongest
proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the
strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%.
All models lag far behind human performance of 80.5%, underscoring weaknesses
in the chart understanding capabilities of existing MLLMs. We hope CharXiv
facilitates future research on MLLM chart understanding by providing a more
realistic and faithful measure of progress. Project page and leaderboard:
https://charxiv.github.io/
Octo-planner: On-device Language Model for Planner-Action Agents
Authors: name: Wei Chen, name: Zhiyuan Li, name: Zhen Guo, name: Yikang Shen
Published: 2024-06-26 05:40:10

AI agents have become increasingly significant in various domains, enabling
autonomous decision-making and problem-solving. To function effectively, these
agents require a planning process that determines the best course of action and
then executes the planned actions. In this paper, we present an efficient
on-device Planner-Action framework that separates planning and action execution
into two distinct components: a planner agent based on Phi-3 Mini, a 3.8
billion parameter LLM optimized for edge devices, and an action agent using the
Octopus model for function execution. The planner agent first responds to user
queries by decomposing tasks into a sequence of sub-steps, which are then
executed by the action agent. To optimize performance on resource-constrained
devices, we employ model fine-tuning instead of in-context learning, reducing
computational costs and energy consumption while improving response times. Our
approach involves using GPT-4 to generate diverse planning queries and
responses based on available functions, with subsequent validations to ensure
data quality. We fine-tune the Phi-3 Mini model on this curated dataset,
achieving a 97\% success rate in our in-domain test environment. To address
multi-domain planning challenges, we developed a multi-LoRA training method
that merges weights from LoRAs trained on distinct function subsets. This
approach enables flexible handling of complex, multi-domain queries while
maintaining computational efficiency on resource-constrained devices. To
support further research, we have open-sourced our model weights at
https://huggingface.co/NexaAIDev/octopus-planning. For the demo, please
refer to https://www.nexa4ai.com/octo-planner.
APIGen: Automated Pipeline for Generating Verifiable and Diverse
  Function-Calling Datasets
Authors: name: Zuxin Liu, name: Thai Hoang, name: Jianguo Zhang, name: Ming Zhu, name: Tian Lan, name: Shirley Kokane, name: Juntao Tan, name: Weiran Yao, name: Zhiwei Liu, name: Yihao Feng, name: Rithesh Murthy, name: Liangwei Yang, name: Silvio Savarese, name: Juan Carlos Niebles, name: Huan Wang, name: Shelby Heinecke, name: Caiming Xiong
Published: 2024-06-26 17:49:11

The advancement of function-calling agent models requires diverse, reliable,
and high-quality datasets. This paper presents APIGen, an automated data
generation pipeline designed to synthesize verifiable high-quality datasets for
function-calling applications. We leverage APIGen and collect 3,673 executable
APIs across 21 different categories to generate diverse function-calling
datasets in a scalable and structured manner. Each data in our dataset is
verified through three hierarchical stages: format checking, actual function
executions, and semantic verification, ensuring its reliability and
correctness. We demonstrate that models trained with our curated datasets, even
with only 7B parameters, can achieve state-of-the-art performance on the
Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models.
Moreover, our 1B model achieves exceptional performance, surpassing
GPT-3.5-Turbo and Claude-3 Haiku. We release a dataset containing 60,000
high-quality entries, aiming to advance the field of function-calling agent
domains. The dataset is available on Huggingface:
https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k and the
project homepage: https://apigen-pipeline.github.io/
Cross-Modality Safety Alignment
Authors: name: Siyin Wang, name: Xingsong Ye, name: Qinyuan Cheng, name: Junwen Duan, name: Shimin Li, name: Jinlan Fu, name: Xipeng Qiu, name: Xuanjing Huang
Published: 2024-06-21 16:14:15

As Artificial General Intelligence (AGI) becomes increasingly integrated into
various facets of human life, ensuring the safety and ethical alignment of such
systems is paramount. Previous studies primarily focus on single-modality
threats, which may not suffice given the integrated and complex nature of
cross-modality interactions. We introduce a novel safety alignment challenge
called Safe Inputs but Unsafe Output (SIUO) to evaluate cross-modality safety
alignment. Specifically, it considers cases where single modalities are safe
independently but could potentially lead to unsafe or unethical outputs when
combined. To empirically investigate this problem, we developed the SIUO, a
cross-modality benchmark encompassing 9 critical safety domains, such as
self-harm, illegal activities, and privacy violations. Our findings reveal
substantial safety vulnerabilities in both closed- and open-source LVLMs, such
as GPT-4V and LLaVA, underscoring the inadequacy of current models to reliably
interpret and respond to complex, real-world scenarios.
Large Language Models Assume People are More Rational than We Really are
Authors: name: Ryan Liu, name: Jiayi Geng, name: Joshua C. Peterson, name: Ilia Sucholutsky, name: Thomas L. Griffiths
Published: 2024-06-24 18:15:27

In order for AI systems to communicate effectively with people, they must
understand how we make decisions. However, people's decisions are not always
rational, so the implicit internal models of human decision-making in Large
Language Models (LLMs) must account for this. Previous empirical evidence seems
to suggest that these implicit models are accurate -- LLMs offer believable
proxies of human behavior, acting how we expect humans would in everyday
interactions. However, by comparing LLM behavior and predictions to a large
dataset of human decisions, we find that this is actually not the case: when
both simulating and predicting people's choices, a suite of cutting-edge LLMs
(GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more
rational than we really are. Specifically, these models deviate from human
behavior and align more closely with a classic model of rational choice --
expected value theory. Interestingly, people also tend to assume that other
people are rational when interpreting their behavior. As a consequence, when we
compare the inferences that LLMs and people draw from the decisions of others
using another psychological dataset, we find that these inferences are highly
correlated. Thus, the implicit decision-making models of LLMs appear to be
aligned with the human expectation that other people will act rationally,
rather than with how people actually act.