Orionnnn/hub-stats huggingface dataset:
High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion
Authors: name: Xiang Zhang, name: Yang Zhang, name: Lukas Mehl, name: Markus Gross, name: Christopher Schroers
Published: 2025-02-18 11:13:06

Despite recent advances in Novel View Synthesis (NVS), generating
high-fidelity views from single or sparse observations remains a significant
challenge. Existing splatting-based approaches often produce distorted geometry
due to splatting errors. While diffusion-based methods leverage rich 3D priors
to achieve improved geometry, they often suffer from texture hallucination. In
this paper, we introduce SplatDiff, a pixel-splatting-guided video diffusion
model designed to synthesize high-fidelity novel views from a single image.
Specifically, we propose an aligned synthesis strategy for precise control of
target viewpoints and geometry-consistent view synthesis. To mitigate texture
hallucination, we design a texture bridge module that enables high-fidelity
texture generation through adaptive feature fusion. In this manner, SplatDiff
leverages the strengths of splatting and diffusion to generate novel views with
consistent geometry and high-fidelity details. Extensive experiments verify the
state-of-the-art performance of SplatDiff in single-view NVS. Additionally,
without extra training, SplatDiff shows remarkable zero-shot performance across
diverse tasks, including sparse-view NVS and stereo video conversion.
TESS 2: A Large-Scale Generalist Diffusion Language Model
Authors: name: Jaesung Tae, name: Hamish Ivison, name: Sachin Kumar, name: Arman Cohan
Published: 2025-02-19 17:50:31

We introduce TESS 2, a general instruction-following diffusion language model
that outperforms contemporary instruction-tuned diffusion models, as well as
matches and sometimes exceeds strong autoregressive (AR) models. We train TESS
2 by first adapting a strong AR model via continued pretraining with the usual
cross-entropy as diffusion loss, and then performing further instruction
tuning. We find that adaptation training as well as the choice of the base
model is crucial for training good instruction-following diffusion models. We
further propose reward guidance, a novel and modular inference-time guidance
procedure to align model outputs without needing to train the underlying model.
Finally, we show that TESS 2 further improves with increased inference-time
compute, highlighting the utility of diffusion LMs in having fine-grained
controllability over the amount of compute used at inference time. Code and
models are available at https://github.com/hamishivi/tess-2.
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
  Language Models
Authors: name: DongGeon Lee, name: Hwanjo Yu
Published: 2025-02-19 10:59:05

Hallucinations in large language model (LLM) outputs severely limit their
reliability in knowledge-intensive tasks such as question answering. To address
this challenge, we introduce REFIND (Retrieval-augmented Factuality
hallucINation Detection), a novel framework that detects hallucinated spans
within LLM outputs by directly leveraging retrieved documents. As part of the
REFIND, we propose the Context Sensitivity Ratio (CSR), a novel metric that
quantifies the sensitivity of LLM outputs to retrieved evidence. This
innovative approach enables REFIND to efficiently and accurately detect
hallucinations, setting it apart from existing methods. In the evaluation,
REFIND demonstrated robustness across nine languages, including low-resource
settings, and significantly outperformed baseline models, achieving superior
IoU scores in identifying hallucinated spans. This work highlights the
effectiveness of quantifying context sensitivity for hallucination detection,
thereby paving the way for more reliable and trustworthy LLM applications
across diverse languages.
Train Small, Infer Large: Memory-Efficient LoRA Training for Large
  Language Models
Authors: name: Jun Zhang, name: Jue Wang, name: Huan Li, name: Lidan Shou, name: Ke Chen, name: Yang You, name: Guiming Xie, name: Xuejian Gong, name: Kunlong Zhou
Published: 2025-02-19 08:39:15

Large Language Models (LLMs) have significantly advanced natural language
processing with exceptional task generalization capabilities. Low-Rank Adaption
(LoRA) offers a cost-effective fine-tuning solution, freezing the original
model parameters and training only lightweight, low-rank adapter matrices.
However, the memory footprint of LoRA is largely dominated by the original
model parameters. To mitigate this, we propose LoRAM, a memory-efficient LoRA
training scheme founded on the intuition that many neurons in
over-parameterized LLMs have low training utility but are essential for
inference. LoRAM presents a unique twist: it trains on a pruned (small) model
to obtain pruned low-rank matrices, which are then recovered and utilized with
the original (large) model for inference. Additionally, minimal-cost continual
pre-training, performed by the model publishers in advance, aligns the
knowledge discrepancy between pruned and original models. Our extensive
experiments demonstrate the efficacy of LoRAM across various pruning strategies
and downstream tasks. For a model with 70 billion parameters, LoRAM enables
training on a GPU with only 20G HBM, replacing an A100-80G GPU for LoRA
training and 15 GPUs for full fine-tuning. Specifically, QLoRAM implemented by
structured pruning combined with 4-bit quantization, for LLaMA-3.1-70B
(LLaMA-2-70B), reduces the parameter storage cost that dominates the memory
usage in low-rank matrix training by 15.81times (16.95times), while
achieving dominant performance gains over both the original LLaMA-3.1-70B
(LLaMA-2-70B) and LoRA-trained LLaMA-3.1-8B (LLaMA-2-13B).
Noise May Contain Transferable Knowledge: Understanding Semi-supervised
  Heterogeneous Domain Adaptation from an Empirical Perspective
Authors: name: Yuan Yao, name: Xiaopu Zhang, name: Yu Zhang, name: Jian Jin, name: Qiang Yang
Published: 2025-02-19 09:27:03

Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning
across domains with distinct feature representations and distributions, where
source samples are labeled while most target samples are unlabeled, with only a
small fraction labeled. Moreover, there is no one-to-one correspondence between
source and target samples. Although various SHDA methods have been developed to
tackle this problem, the nature of the knowledge transferred across
heterogeneous domains remains unclear. This paper delves into this question
from an empirical perspective. We conduct extensive experiments on about 330
SHDA tasks, employing two supervised learning methods and seven representative
SHDA methods. Surprisingly, our observations indicate that both the category
and feature information of source samples do not significantly impact the
performance of the target domain. Additionally, noise drawn from simple
distributions, when used as source samples, may contain transferable knowledge.
Based on this insight, we perform a series of experiments to uncover the
underlying principles of transferable knowledge in SHDA. Specifically, we
design a unified Knowledge Transfer Framework (KTF) for SHDA. Based on the KTF,
we find that the transferable knowledge in SHDA primarily stems from the
transferability and discriminability of the source domain. Consequently,
ensuring those properties in source samples, regardless of their origin (e.g.,
image, text, noise), can enhance the effectiveness of knowledge transfer in
SHDA tasks. The codes and datasets are available at
https://github.com/yyyaoyuan/SHDA.
GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge
  Benchmarking
Authors: name: Florian Schneider, name: Carolin Holtermann, name: Chris Biemann, name: Anne Lauscher
Published: 2025-02-19 14:27:40

Large Vision-Language Models (LVLMs) have recently gained attention due to
their distinctive performance and broad applicability. While it has been
previously shown that their efficacy in usage scenarios involving non-Western
contexts falls short, existing studies are limited in scope, covering just a
narrow range of cultures, focusing exclusively on a small number of cultural
aspects, or evaluating a limited selection of models on a single task only.
Towards globally inclusive LVLM research, we introduce GIMMICK, an extensive
multimodal benchmark designed to assess a broad spectrum of cultural knowledge
across 144 countries representing six global macro-regions. GIMMICK comprises
six tasks built upon three new datasets that span 728 unique cultural events or
facets on which we evaluated 20 LVLMs and 11 LLMs, including five proprietary
and 26 open-weight models of all sizes. We systematically examine (1) regional
cultural biases, (2) the influence of model size, (3) input modalities, and (4)
external cues. Our analyses reveal strong biases toward Western cultures across
models and tasks and highlight strong correlations between model size and
performance, as well as the effectiveness of multimodal input and external
geographic cues. We further find that models have more knowledge of tangible
than intangible aspects (e.g., food vs. rituals) and that they excel in
recognizing broad cultural origins but struggle with a more nuanced
understanding.
InfiR : Crafting Effective Small Language Models and Multimodal Small
  Language Models in Reasoning
Authors: name: Congkai Xie, name: Shuo Cai, name: Wenjun Wang, name: Pengxiang Li, name: Zhijie Sang, name: Kejing Yang, name: Yiming Zhang, name: Zhen Li, name: Guanghao Zhu, name: Zeyu Liu, name: Yang Yu, name: Yuhang Liu, name: Su Lu, name: Baoyi He, name: Qi Zhou, name: Xiaotian Han, name: Jianbo Yuan, name: Shengyu Zhang, name: Fei Wu, name: Hongxia Yang
Published: 2025-02-17 09:07:32

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs)
have made significant advancements in reasoning capabilities. However, they
still face challenges such as high computational demands and privacy concerns.
This paper focuses on developing efficient Small Language Models (SLMs) and
Multimodal Small Language Models (MSLMs) that retain competitive reasoning
abilities. We introduce a novel training pipeline that enhances reasoning
capabilities and facilitates deployment on edge devices, achieving
state-of-the-art performance while minimizing development costs. \InfR~ aims to
advance AI systems by improving reasoning, reducing adoption barriers, and
addressing privacy concerns through smaller model sizes. Resources are
available at https://github. com/Reallm-Labs/InfiR.
ActionPiece: Contextually Tokenizing Action Sequences for Generative
  Recommendation
Authors: name: Yupeng Hou, name: Jianmo Ni, name: Zhankui He, name: Noveen Sachdeva, name: Wang-Cheng Kang, name: Ed H. Chi, name: Julian McAuley, name: Derek Zhiyuan Cheng
Published: 2025-02-19 09:45:29

Generative recommendation (GR) is an emerging paradigm where user actions are
tokenized into discrete token patterns and autoregressively generated as
predictions. However, existing GR models tokenize each action independently,
assigning the same fixed tokens to identical actions across all sequences
without considering contextual relationships. This lack of context-awareness
can lead to suboptimal performance, as the same action may hold different
meanings depending on its surrounding context. To address this issue, we
propose ActionPiece to explicitly incorporate context when tokenizing action
sequences. In ActionPiece, each action is represented as a set of item
features, which serve as the initial tokens. Given the action sequence corpora,
we construct the vocabulary by merging feature patterns as new tokens, based on
their co-occurrence frequency both within individual sets and across adjacent
sets. Considering the unordered nature of feature sets, we further introduce
set permutation regularization, which produces multiple segmentations of action
sequences with the same semantics. Experiments on public datasets demonstrate
that ActionPiece consistently outperforms existing action tokenization methods,
improving NDCG@10 by 6.00% to 12.82%.
MoM: Linear Sequence Modeling with Mixture-of-Memories
Authors: name: Jusen Du, name: Weigao Sun, name: Disen Lan, name: Jiaxi Hu, name: Yu Cheng
Published: 2025-02-19 12:53:55

Linear sequence modeling methods, such as linear attention, state space
modeling, and linear RNNs, offer significant efficiency improvements by
reducing the complexity of training and inference. However, these methods
typically compress the entire input sequence into a single fixed-size memory
state, which leads to suboptimal performance on recall-intensive downstream
tasks. Drawing inspiration from neuroscience, particularly the brain's ability
to maintain robust long-term memory while mitigating "memory interference", we
introduce a novel architecture called Mixture-of-Memories (MoM). MoM utilizes
multiple independent memory states, with a router network directing input
tokens to specific memory states. This approach greatly enhances the overall
memory capacity while minimizing memory interference. As a result, MoM performs
exceptionally well on recall-intensive tasks, surpassing existing linear
sequence modeling techniques. Despite incorporating multiple memory states, the
computation of each memory state remains linear in complexity, allowing MoM to
retain the linear-complexity advantage during training, while
constant-complexity during inference. Our experimental results show that MoM
significantly outperforms current linear sequence models on downstream language
tasks, particularly recall-intensive tasks, and even achieves performance
comparable to Transformer models. The code is released at
https://github.com/OpenSparseLLMs/MoM and is also released as a part of
https://github.com/OpenSparseLLMs/Linear-MoE.
Presumed Cultural Identity: How Names Shape LLM Responses
Authors: name: Siddhesh Pawar, name: Arnav Arora, name: Lucie-Aimée Kaffee, name: Isabelle Augenstein
Published: 2025-02-17 16:35:15

Names are deeply tied to human identity. They can serve as markers of
individuality, cultural heritage, and personal history. However, using names as
a core indicator of identity can lead to over-simplification of complex
identities. When interacting with LLMs, user names are an important point of
information for personalisation. Names can enter chatbot conversations through
direct user input (requested by chatbots), as part of task contexts such as CV
reviews, or as built-in memory features that store user information for
personalisation. We study biases associated with names by measuring cultural
presumptions in the responses generated by LLMs when presented with common
suggestion-seeking queries, which might involve making assumptions about the
user. Our analyses demonstrate strong assumptions about cultural identity
associated with names present in LLM generations across multiple cultures. Our
work has implications for designing more nuanced personalisation systems that
avoid reinforcing stereotypes while maintaining meaningful customisation.
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song
  Generation
Authors: name: Zihan Liu, name: Shuangrui Ding, name: Zhixiong Zhang, name: Xiaoyi Dong, name: Pan Zhang, name: Yuhang Zang, name: Yuhang Cao, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-02-18 18:52:21

Text-to-song generation, the task of creating vocals and accompaniment from
textual inputs, poses significant challenges due to domain complexity and data
scarcity. Existing approaches often employ multi-stage generation procedures,
resulting in cumbersome training and inference pipelines. In this paper, we
propose SongGen, a fully open-source, single-stage auto-regressive transformer
designed for controllable song generation. The proposed model facilitates
fine-grained control over diverse musical attributes, including lyrics and
textual descriptions of instrumentation, genre, mood, and timbre, while also
offering an optional three-second reference clip for voice cloning. Within a
unified auto-regressive framework, SongGen supports two output modes: mixed
mode, which generates a mixture of vocals and accompaniment directly, and
dual-track mode, which synthesizes them separately for greater flexibility in
downstream applications. We explore diverse token pattern strategies for each
mode, leading to notable improvements and valuable insights. Furthermore, we
design an automated data preprocessing pipeline with effective quality control.
To foster community engagement and future research, we will release our model
weights, training code, annotated data, and preprocessing pipeline. The
generated samples are showcased on our project page at
https://liuzh-19.github.io/SongGen/ , and the code will be available at
https://github.com/LiuZH-19/SongGen .
Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety
  Mechanisms Tend to Be Anchored in The Template Region
Authors: name: Chak Tou Leong, name: Qingyu Yin, name: Jian Wang, name: Wenjie Li
Published: 2025-02-19 18:42:45

The safety alignment of large language models (LLMs) remains vulnerable, as
their initial behavior can be easily jailbroken by even relatively simple
attacks. Since infilling a fixed template between the input instruction and
initial model output is a common practice for existing LLMs, we hypothesize
that this template is a key factor behind their vulnerabilities: LLMs'
safety-related decision-making overly relies on the aggregated information from
the template region, which largely influences these models' safety behavior. We
refer to this issue as template-anchored safety alignment. In this paper, we
conduct extensive experiments and verify that template-anchored safety
alignment is widespread across various aligned LLMs. Our mechanistic analyses
demonstrate how it leads to models' susceptibility when encountering
inference-time jailbreak attacks. Furthermore, we show that detaching safety
mechanisms from the template region is promising in mitigating vulnerabilities
to jailbreak attacks. We encourage future research to develop more robust
safety alignment techniques that reduce reliance on the template region.
Qwen2.5-VL Technical Report
Authors: name: Shuai Bai, name: Keqin Chen, name: Xuejing Liu, name: Jialin Wang, name: Wenbin Ge, name: Sibo Song, name: Kai Dang, name: Peng Wang, name: Shijie Wang, name: Jun Tang, name: Humen Zhong, name: Yuanzhi Zhu, name: Mingkun Yang, name: Zhaohai Li, name: Jianqiang Wan, name: Pengfei Wang, name: Wei Ding, name: Zheren Fu, name: Yiheng Xu, name: Jiabo Ye, name: Xi Zhang, name: Tianbao Xie, name: Zesen Cheng, name: Hang Zhang, name: Zhibo Yang, name: Haiyang Xu, name: Junyang Lin
Published: 2025-02-19 18:00:14

We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language
series, which demonstrates significant advancements in both foundational
capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap
forward in understanding and interacting with the world through enhanced visual
recognition, precise object localization, robust document parsing, and
long-video comprehension. A standout feature of Qwen2.5-VL is its ability to
localize objects using bounding boxes or points accurately. It provides robust
structured data extraction from invoices, forms, and tables, as well as
detailed analysis of charts, diagrams, and layouts. To handle complex inputs,
Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding,
enabling it to process images of varying sizes and videos of extended durations
(up to hours) with second-level event localization. This allows the model to
natively perceive spatial scales and temporal dynamics without relying on
traditional normalization techniques. By training a native dynamic-resolution
Vision Transformer (ViT) from scratch and incorporating Window Attention, we
reduce computational overhead while maintaining native resolution. As a result,
Qwen2.5-VL excels not only in static image and document understanding but also
as an interactive visual agent capable of reasoning, tool usage, and task
execution in real-world scenarios such as operating computers and mobile
devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases
from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model
matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly
excelling in document and diagram understanding. Additionally, Qwen2.5-VL
maintains robust linguistic performance, preserving the core language
competencies of the Qwen2.5 LLM.
Is That Your Final Answer? Test-Time Scaling Improves Selective Question
  Answering
Authors: name: William Jurayj, name: Jeffrey Cheng, name: Benjamin Van Durme
Published: 2025-02-19 18:58:31

Scaling the test-time compute of large language models has demonstrated
impressive performance on reasoning benchmarks. However, existing evaluations
of test-time scaling make the strong assumption that a reasoning system should
always give an answer to any question provided. This overlooks concerns about
whether a model is confident in its answer, and whether it is appropriate to
always provide a response. To address these concerns, we extract confidence
scores during reasoning for thresholding model responses. We find that
increasing compute budget at inference time not only helps models answer more
questions correctly, but also increases confidence in correct responses. We
then extend the current paradigm of zero-risk responses during evaluation by
considering settings with non-zero levels of response risk, and suggest a
recipe for reporting evaluations under these settings.
Thinking Preference Optimization
Authors: name: Wang Yang, name: Hongye Jin, name: Jingfeng Yang, name: Vipin Chaudhary, name: Xiaotian Han
Published: 2025-02-17 19:56:21

Supervised Fine-Tuning (SFT) has been a go-to and effective method for
enhancing long chain-of-thought (CoT) reasoning in relatively small LLMs by
fine-tuning them with long CoT responses from larger LLMs. To continually
improve reasoning abilities, we can either collect new high-quality long CoT
reasoning SFT data or repeatedly train on existing SFT datasets. However,
acquiring new long CoT SFT data is costly and limited, while repeated training
often results in a performance plateau or decline. To further boost the
performance with the SFT data, we propose Thinking Preference Optimization
(ThinkPO), a simple yet effective post-SFT method that enhances long CoT
reasoning without requiring new long CoT responses. Instead, ThinkPO utilizes
readily available or easily obtainable short CoT reasoning responses as
rejected answers and long CoT responses as chosen answers for the same
question. It then applies direct preference optimization to encourage the model
to favor longer reasoning outputs. Experiments show that ThinkPO further
improves the reasoning performance of SFT-ed models, e.g. it increases math
reasoning accuracy of SFT-ed models by 8.6% and output length by 25.9%.
Notably, ThinkPO is capable of continually boosting the performance of the
publicly distilled SFT model, e.g., increasing the official
DeepSeek-R1-Distill-Qwen-7B's performance on MATH500 from 87.4% to 91.2%.