Orionnnn/hub-stats huggingface dataset:
Ablation is Not Enough to Emulate DPO: How Neuron Dynamics Drive
  Toxicity Reduction
Authors: name: Yushi Yang, name: Filip Sondej, name: Harry Mayne, name: Adam Mahdi
Published: 2024-11-10 11:07:34

Safety fine-tuning algorithms are commonly used to fine-tune language models
to reduce harmful outputs, but the exact internal mechanisms of how those
models achieve this remain unclear. In studying direct preference optimisation
(DPO) for toxicity reduction, current explanations claim that DPO works by
dampening the most toxic MLP neurons to learn an offset to avert toxic regions
in the residual stream. However, by ablating the most toxic neurons and
applying activation patching, we find this explanation incomplete. By
projecting neuron activation changes onto a toxicity probe, we find that only
31.8\% of toxicity reduction comes from dampened toxic neurons. Instead, DPO
reduces toxicity by accumulating effects across multiple neuron groups, both
reducing writing in the toxic direction and promoting anti-toxicity in the
residual stream. Moreover, DPO gives noisy adjustments to neuron activations,
with many neurons actually increasing toxicity. This indicates that DPO is a
balancing process between opposing neuron effects to achieve toxicity
reduction.
KMM: Key Frame Mask Mamba for Extended Motion Generation
Authors: name: Zeyu Zhang, name: Hang Gao, name: Akide Liu, name: Qi Chen, name: Feng Chen, name: Yiran Wang, name: Danning Li, name: Hao Tang
Published: 2024-11-10 14:41:38

Human motion generation is a cut-edge area of research in generative computer
vision, with promising applications in video creation, game development, and
robotic manipulation. The recent Mamba architecture shows promising results in
efficiently modeling long and complex sequences, yet two significant challenges
remain: Firstly, directly applying Mamba to extended motion generation is
ineffective, as the limited capacity of the implicit memory leads to memory
decay. Secondly, Mamba struggles with multimodal fusion compared to
Transformers, and lack alignment with textual queries, often confusing
directions (left or right) or omitting parts of longer text queries. To address
these challenges, our paper presents three key contributions: Firstly, we
introduce KMM, a novel architecture featuring Key frame Masking Modeling,
designed to enhance Mamba's focus on key actions in motion segments. This
approach addresses the memory decay problem and represents a pioneering method
in customizing strategic frame-level masking in SSMs. Additionally, we designed
a contrastive learning paradigm for addressing the multimodal fusion problem in
Mamba and improving the motion-text alignment. Finally, we conducted extensive
experiments on the go-to dataset, BABEL, achieving state-of-the-art performance
with a reduction of more than 57% in FID and 70% parameters compared to
previous state-of-the-art methods. See project website:
https://steve-zeyu-zhang.github.io/KMM
Add-it: Training-Free Object Insertion in Images With Pretrained
  Diffusion Models
Authors: name: Yoad Tewel, name: Rinon Gal, name: Dvir Samuel Yuval Atzmon, name: Lior Wolf, name: Gal Chechik
Published: 2024-11-11 18:50:09

Adding Object into images based on text instructions is a challenging task in
semantic image editing, requiring a balance between preserving the original
scene and seamlessly integrating the new object in a fitting location. Despite
extensive efforts, existing models often struggle with this balance,
particularly with finding a natural location for adding an object in complex
scenes. We introduce Add-it, a training-free approach that extends diffusion
models' attention mechanisms to incorporate information from three key sources:
the scene image, the text prompt, and the generated image itself. Our weighted
extended-attention mechanism maintains structural consistency and fine details
while ensuring natural object placement. Without task-specific fine-tuning,
Add-it achieves state-of-the-art results on both real and generated image
insertion benchmarks, including our newly constructed "Additing Affordance
Benchmark" for evaluating object placement plausibility, outperforming
supervised methods. Human evaluations show that Add-it is preferred in over 80%
of cases, and it also demonstrates improvements in various automated metrics.
Game-theoretic LLM: Agent Workflow for Negotiation Games
Authors: name: Wenyue Hua, name: Ollie Liu, name: Lingyao Li, name: Alfonso Amayuelas, name: Julie Chen, name: Lucas Jiang, name: Mingyu Jin, name: Lizhou Fan, name: Fei Sun, name: William Wang, name: Xintong Wang, name: Yongfeng Zhang
Published: 2024-11-08 22:02:22

This paper investigates the rationality of large language models (LLMs) in
strategic decision-making contexts, specifically within the framework of game
theory. We evaluate several state-of-the-art LLMs across a spectrum of
complete-information and incomplete-information games. Our findings reveal that
LLMs frequently deviate from rational strategies, particularly as the
complexity of the game increases with larger payoff matrices or deeper
sequential trees.
  To address these limitations, we design multiple game-theoretic workflows
that guide the reasoning and decision-making processes of LLMs. These workflows
aim to enhance the models' ability to compute Nash Equilibria and make rational
choices, even under conditions of uncertainty and incomplete information.
Experimental results demonstrate that the adoption of these workflows
significantly improves the rationality and robustness of LLMs in game-theoretic
tasks. Specifically, with the workflow, LLMs exhibit marked improvements in
identifying optimal strategies, achieving near-optimal allocations in
negotiation scenarios, and reducing susceptibility to exploitation during
negotiations. Furthermore, we explore the meta-strategic considerations of
whether it is rational for agents to adopt such workflows, recognizing that the
decision to use or forgo the workflow constitutes a game-theoretic issue in
itself.
  Our research contributes to a deeper understanding of LLMs' decision-making
capabilities in strategic contexts and provides insights into enhancing their
rationality through structured workflows. The findings have implications for
the development of more robust and strategically sound AI agents capable of
navigating complex interactive environments. Code and data supporting this
study are available at https://github.com/Wenyueh/game_theory.
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language
  Models
Authors: name: Yancheng He, name: Shilong Li, name: Jiaheng Liu, name: Yingshui Tan, name: Hui Huang, name: Weixun Wang, name: Xingyuan Bu, name: Hangyu Guo, name: Chengwei Hu, name: Boren Zheng, name: Xuepeng Liu, name: Dekai Sun, name: Wenbo Su, name: Bo Zheng
Published: 2024-11-11 17:10:56

New LLM evaluation benchmarks are important to align with the rapid
development of Large Language Models (LLMs). In this work, we present Chinese
SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality
ability of language models to answer short questions, and Chinese SimpleQA
mainly has five properties (i.e., Chinese, Diverse, High-quality, Static,
Easy-to-evaluate). Specifically, first, we focus on the Chinese language over 6
major topics with 99 diverse subtopics. Second, we conduct a comprehensive
quality control process to achieve high-quality questions and answers, where
the reference answers are static and cannot be changed over time. Third,
following SimpleQA, the questions and answers are very short, and the grading
process is easy-to-evaluate based on OpenAI API. Based on Chinese SimpleQA, we
perform a comprehensive evaluation on the factuality abilities of existing
LLMs. Finally, we hope that Chinese SimpleQA could guide the developers to
better understand the Chinese factuality abilities of their models and
facilitate the growth of foundation models.
OmniEdit: Building Image Editing Generalist Models Through Specialist
  Supervision
Authors: name: Cong Wei, name: Zheyang Xiong, name: Weiming Ren, name: Xinrun Du, name: Ge Zhang, name: Wenhu Chen
Published: 2024-11-11 18:21:43

Instruction-guided image editing methods have demonstrated significant
potential by training diffusion models on automatically synthesized or manually
annotated image editing pairs. However, these methods remain far from
practical, real-life applications. We identify three primary challenges
contributing to this gap. Firstly, existing models have limited editing skills
due to the biased synthesis process. Secondly, these methods are trained with
datasets with a high volume of noise and artifacts. This is due to the
application of simple filtering methods like CLIP-score. Thirdly, all these
datasets are restricted to a single low resolution and fixed aspect ratio,
limiting the versatility to handle real-world use cases. In this paper, we
present \omniedit, which is an omnipotent editor to handle seven different
image editing tasks with any aspect ratio seamlessly. Our contribution is in
four folds: (1) \omniedit is trained by utilizing the supervision from seven
different specialist models to ensure task coverage. (2) we utilize importance
sampling based on the scores provided by large multimodal models (like GPT-4o)
instead of CLIP-score to improve the data quality. (3) we propose a new editing
architecture called EditNet to greatly boost the editing success rate, (4) we
provide images with different aspect ratios to ensure that our model can handle
any image in the wild. We have curated a test set containing images of
different aspect ratios, accompanied by diverse instructions to cover different
tasks. Both automatic evaluation and human evaluations demonstrate that
\omniedit can significantly outperform all the existing models. Our code,
dataset and model will be available at
https://tiger-ai-lab.github.io/OmniEdit/
Edify Image: High-Quality Image Generation with Pixel Space Laplacian
  Diffusion Models
Authors: name: NVIDIA, name: Yuval Atzmon, name: Maciej Bala, name: Yogesh Balaji, name: Tiffany Cai, name: Yin Cui, name: Jiaojiao Fan, name: Yunhao Ge, name: Siddharth Gururani, name: Jacob Huffman, name: Ronald Isaac, name: Pooya Jannaty, name: Tero Karras, name: Grace Lam, name: J. P. Lewis, name: Aaron Licata, name: Yen-Chen Lin, name: Ming-Yu Liu, name: Qianli Ma, name: Arun Mallya, name: Ashlee Martino-Tarr, name: Doug Mendez, name: Seungjun Nah, name: Chris Pruett, name: Fitsum Reda, name: Jiaming Song, name: Ting-Chun Wang, name: Fangyin Wei, name: Xiaohui Zeng, name: Yu Zeng, name: Qinsheng Zhang
Published: 2024-11-11 16:58:31

We introduce Edify Image, a family of diffusion models capable of generating
photorealistic image content with pixel-perfect accuracy. Edify Image utilizes
cascaded pixel-space diffusion models trained using a novel Laplacian diffusion
process, in which image signals at different frequency bands are attenuated at
varying rates. Edify Image supports a wide range of applications, including
text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama
generation, and finetuning for image customization.
IOPO: Empowering LLMs with Complex Instruction Following via
  Input-Output Preference Optimization
Authors: name: Xinghua Zhang, name: Haiyang Yu, name: Cheng Fu, name: Fei Huang, name: Yongbin Li
Published: 2024-11-09 15:12:43

In the realm of large language models (LLMs), the ability of models to
accurately follow instructions is paramount as more agents and applications
leverage LLMs for construction, where the complexity of instructions are
rapidly increasing. However, on the one hand, there is only a certain amount of
complex instruction evaluation data; on the other hand, there are no dedicated
algorithms to improve the ability to follow complex instructions. To this end,
this paper introduces TRACE, a benchmark for improving and evaluating the
complex instructionfollowing ability, which consists of 120K training data and
1K evaluation data. Furthermore, we propose IOPO (Input-Output Preference
Optimization) alignment method which takes both input and output preference
pairs into consideration, where LLMs not only rapidly align with response
preferences but also meticulously explore the instruction preferences.
Extensive experiments on both in-domain and outof-domain datasets confirm the
effectiveness of IOPO, showing 8.15%, 2.18% improvements on in-domain data and
6.29%, 3.13% on outof-domain data compared to SFT and DPO respectively.
Counterfactual Generation from Language Models
Authors: name: Shauli Ravfogel, name: Anej Svete, name: Vésteinn Snæbjarnarson, name: Ryan Cotterell
Published: 2024-11-11 17:57:30

Understanding and manipulating the causal generation mechanisms in language
models is essential for controlling their behavior. Previous work has primarily
relied on techniques such as representation surgery -- e.g., model ablations or
manipulation of linear subspaces tied to specific concepts -- to intervene on
these models. To understand the impact of interventions precisely, it is useful
to examine counterfactuals -- e.g., how a given sentence would have appeared
had it been generated by the model following a specific intervention. We
highlight that counterfactual reasoning is conceptually distinct from
interventions, as articulated in Pearl's causal hierarchy. Based on this
observation, we propose a framework for generating true string counterfactuals
by reformulating language models as Generalized Structural-equation. Models
using the Gumbel-max trick. This allows us to model the joint distribution over
original strings and their counterfactuals resulting from the same
instantiation of the sampling noise. We develop an algorithm based on hindsight
Gumbel sampling that allows us to infer the latent noise variables and generate
counterfactuals of observed strings. Our experiments demonstrate that the
approach produces meaningful counterfactuals while at the same time showing
that commonly used intervention techniques have considerable undesired side
effects.
M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding
  And A Retrieval-Aware Tuning Framework
Authors: name: Yew Ken Chia, name: Liying Cheng, name: Hou Pong Chan, name: Chaoqun Liu, name: Maojia Song, name: Sharifah Mahani Aljunied, name: Soujanya Poria, name: Lidong Bing
Published: 2024-11-09 13:30:38

The ability to understand and answer questions over documents can be useful
in many business and practical applications. However, documents often contain
lengthy and diverse multimodal contents such as texts, figures, and tables,
which are very time-consuming for humans to read thoroughly. Hence, there is an
urgent need to develop effective and automated methods to aid humans in this
task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an
automated framework to evaluate the performance of large multimodal models. We
further propose a retrieval-aware tuning approach for efficient and effective
multimodal document reading. Compared to existing works, our benchmark consists
of more recent and lengthy documents with hundreds of pages, while also
requiring open-ended solutions and not just extractive answers. To our
knowledge, our training framework is the first to directly address the
retrieval setting for multimodal long documents. To enable tuning open-source
models, we construct a training corpus in a fully automatic manner for the
question-answering task over such documents. Experiments show that our tuning
approach achieves a relative improvement of 4.6% for the correctness of model
responses, compared to the baseline open-source models. Our data, code, and
models are available at https://multimodal-documents.github.io.
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating
  Financial Large Language Models
Authors: name: Xiaojun Wu, name: Junxi Liu, name: Huanyi Su, name: Zhouchi Lin, name: Yiyan Qi, name: Chengjin Xu, name: Jiajun Su, name: Jiajie Zhong, name: Fuwei Wang, name: Saizhuo Wang, name: Fengrui Hua, name: Jia Li, name: Jian Guo
Published: 2024-11-09 20:09:11

As large language models become increasingly prevalent in the financial
sector, there is a pressing need for a standardized method to comprehensively
assess their performance. However, existing finance benchmarks often suffer
from limited language and task coverage, as well as challenges such as
low-quality datasets and inadequate adaptability for LLM evaluation. To address
these limitations, we propose "Golden Touchstone", the first comprehensive
bilingual benchmark for financial LLMs, which incorporates representative
datasets from both Chinese and English across eight core financial NLP tasks.
Developed from extensive open source data collection and industry-specific
demands, this benchmark includes a variety of financial tasks aimed at
thoroughly assessing models' language understanding and generation
capabilities. Through comparative analysis of major models on the benchmark,
such as GPT-4o Llama3, FinGPT and FinMA, we reveal their strengths and
limitations in processing complex financial information. Additionally, we
open-sourced Touchstone-GPT, a financial LLM trained through continual
pre-training and financial instruction tuning, which demonstrates strong
performance on the bilingual benchmark but still has limitations in specific
tasks.This research not only provides the financial large language models with
a practical evaluation tool but also guides the development and optimization of
future research. The source code for Golden Touchstone and model weight of
Touchstone-GPT have been made publicly available at
https://github.com/IDEA-FinAI/Golden-Touchstone, contributing to the
ongoing evolution of FinLLMs and fostering further research in this critical
area.
CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM
Authors: name: Jingwei Xu, name: Chenyu Wang, name: Zibo Zhao, name: Wen Liu, name: Yi Ma, name: Shenghua Gao
Published: 2024-11-07 18:31:08

This paper aims to design a unified Computer-Aided Design (CAD) generation
system that can easily generate CAD models based on the user's inputs in the
form of textual description, images, point clouds, or even a combination of
them. Towards this goal, we introduce the CAD-MLLM, the first system capable of
generating parametric CAD models conditioned on the multimodal input.
Specifically, within the CAD-MLLM framework, we leverage the command sequences
of CAD models and then employ advanced large language models (LLMs) to align
the feature space across these diverse multi-modalities data and CAD models'
vectorized representations. To facilitate the model training, we design a
comprehensive data construction and annotation pipeline that equips each CAD
model with corresponding multimodal data. Our resulting dataset, named
Omni-CAD, is the first multimodal CAD dataset that contains textual
description, multi-view images, points, and command sequence for each CAD
model. It contains approximately 450K instances and their CAD construction
sequences. To thoroughly evaluate the quality of our generated CAD models, we
go beyond current evaluation metrics that focus on reconstruction quality by
introducing additional metrics that assess topology quality and surface
enclosure extent. Extensive experimental results demonstrate that CAD-MLLM
significantly outperforms existing conditional generative methods and remains
highly robust to noises and missing points. The project page and more
visualizations can be found at: https://cad-mllm.github.io/
Language Models are Hidden Reasoners: Unlocking Latent Reasoning
  Capabilities via Self-Rewarding
Authors: name: Haolin Chen, name: Yihao Feng, name: Zuxin Liu, name: Weiran Yao, name: Akshara Prabhakar, name: Shelby Heinecke, name: Ricky Ho, name: Phil Mui, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang
Published: 2024-11-06 22:02:30

Large language models (LLMs) have shown impressive capabilities, but still
struggle with complex reasoning tasks requiring multiple steps. While
prompt-based methods like Chain-of-Thought (CoT) can improve LLM reasoning at
inference time, optimizing reasoning capabilities during training remains
challenging. We introduce LaTent Reasoning Optimization (LaTRO), a principled
framework that formulates reasoning as sampling from a latent distribution and
optimizes it via variational approaches. LaTRO enables LLMs to concurrently
improve both their reasoning process and ability to evaluate reasoning quality,
without requiring external feedback or reward models. We validate LaTRO through
experiments on GSM8K and ARC-Challenge datasets using multiple model
architectures. On GSM8K, LaTRO improves zero-shot accuracy by an average of
12.5% over base models and 9.6% over supervised fine-tuning across
Phi-3.5-mini, Mistral-7B, and Llama-3.1-8B. Our findings suggest that
pre-trained LLMs possess latent reasoning capabilities that can be unlocked and
enhanced through our proposed optimization approach in a self-improvement
manner. The code of LaTRO is available at
https://github.com/SalesforceAIResearch/LaTRO.
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned
  Vision-Language Models
Authors: name: Maya Varma, name: Jean-Benoit Delbrouck, name: Zhihong Chen, name: Akshay Chaudhari, name: Curtis Langlotz
Published: 2024-11-06 18:25:00

Fine-tuned vision-language models (VLMs) often capture spurious correlations
between image features and textual attributes, resulting in degraded zero-shot
performance at test time. Existing approaches for addressing spurious
correlations (i) primarily operate at the global image-level rather than
intervening directly on fine-grained image features and (ii) are predominantly
designed for unimodal settings. In this work, we present RaVL, which takes a
fine-grained perspective on VLM robustness by discovering and mitigating
spurious correlations using local image features rather than operating at the
global image level. Given a fine-tuned VLM, RaVL first discovers spurious
correlations by leveraging a region-level clustering approach to identify
precise image features contributing to zero-shot classification errors. Then,
RaVL mitigates the identified spurious correlation with a novel region-aware
loss function that enables the VLM to focus on relevant regions and ignore
spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with
various model architectures, data domains, and learned spurious correlations.
Our results show that RaVL accurately discovers (191% improvement over the
closest baseline) and mitigates (8.2% improvement on worst-group image
classification accuracy) spurious correlations. Qualitative evaluations on
general-domain and medical-domain VLMs confirm our findings.