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forum id stringlengths 10 10	title stringlengths 21 154	scores listlengths 3 8	text stringlengths 52.4k 300k
bR1J7SpzrD	Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data	[ 8, 8, 5, 6 ]	Under review as a conference paper at ICLR 2025 SYNTHIO: AUGMENTING SMALL-SCALE AUDIO CLAS- SIFICATION DATASETS WITH SYNTHETIC DATA Anonymous authors Paper under double-blind review ABSTRACT We present Synthio, a novel approach for augmenting small-scale audio1 classi- fication datasets with synthetic data. Our goa...
9QPH1YQCMn	Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models	[ 3, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 INFILLING SCORE ✼ A PRETRAINING DATA DETECTION ALGORITHM FOR LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT In pretraining data detection, the goal is to detect whether a given sentence is in the dataset used for training a Large Lang...
nDvgHIBRxQ	Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist	[ 8, 6, 6, 5 ]	Under review as a conference paper at ICLR 2025 IS YOUR MODEL REALLY A GOOD MATH REASONER? EVALUATING MATHEMATICAL REASONING WITH CHECKLIST Anonymous authors Paper under double-blind review ABSTRACT Exceptional mathematical reasoning ability is one of the key features that demon- strate the power of large language ...
leSbzBtofH	AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses	[ 8, 5, 8, 6, 5, 5 ]	Under review as a conference paper at ICLR 2025 AUTOADVEXBENCH: BENCHMARKING AUTONOMOUS EXPLOITATION OF ADVERSARIAL EXAMPLE DEFENSES Anonymous authors Paper under double-blind review ABSTRACT We introduce AutoAdvExBench, a benchmark to evaluate if large language mod- els (LLMs) can autonomously exploit defenses to ...
44CoQe6VCq	Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning	[ 8, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 TEST OF TIME: A BENCHMARK FOR EVALUATING LLMS ON TEMPORAL REASONING Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in tempora...
6RiBl5sCDF	GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training	[ 6, 8, 6, 8 ]	Under review as a conference paper at ICLR 2025 GEOX: GEOMETRIC PROBLEM SOLVING THROUGH UNIFIED FORMALIZED VISION-LANGUAGE PRE- TRAINING Anonymous authors Paper under double-blind review ABSTRACT Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry P...
rawj2PdHBq	Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?	[ 8, 5, 5 ]	Under review as a conference paper at ICLR 2025 CAN MEDICAL VISION-LANGUAGE PRE-TRAINING SUCCEED WITH PURELY SYNTHETIC DATA? Anonymous authors Paper under double-blind review ABSTRACT Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understand...
y3zswp3gek	HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models	[ 6, 6, 10, 6 ]	Under review as a conference paper at ICLR 2025 HARMAUG: EFFECTIVE DATA AUGMENTATION FOR KNOWLEDGE DISTILLATION OF SAFETY GUARD MODELS Anonymous authors Paper under double-blind review ABSTRACT Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the sec...
KvaDHPhhir	Sketch2Diagram: Generating Vector Diagrams from Hand-Drawn Sketches	[ 8, 6, 5, 6 ]	Under review as a conference paper at ICLR 2025 SKETCH2DIAGRAM: GENERATING VECTOR DIA- GRAMS FROM HAND-DRAWN SKETCHES Anonymous authors Paper under double-blind review ABSTRACT We address the challenge of automatically generating high-quality vector dia- grams from hand-drawn sketches. Vector diagrams are essential...
y9A2TpaGsE	Language Agents Meet Causality -- Bridging LLMs and Causal World Models	[ 6, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 LANGUAGE AGENTS MEET CAUSALITY – BRIDGING LLMS AND CAUSAL WORLD MODELS Anonymous authors Paper under double-blind review ABSTRACT Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, ...
o5TsWTUSeF	ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding	[ 6, 5, 8, 8 ]	Under review as a conference paper at ICLR 2025 CHARTMOE: MIXTURE OF DIVERSELY ALIGNED EX- PERT CONNECTOR FOR CHART UNDERSTANDING Anonymous authors Paper under double-blind review ABSTRACT Automatic chart understanding is crucial for content comprehension and docu- ment parsing. Multimodal Large Language Models (ML...
OZbFRNhpwr	SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION	[ 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION Anonymous authors Paper under double-blind review ABSTRACT Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-ba...
IwhvaDrL39	Research Town: Simulator of Research Community	[ 6, 6, 5, 6 ]	000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 RESEARCHTOWN: SIMULATOR OF HUMAN RESEARCH COMMUNITY Anonymous authors Paper under double-blin...
TuOTSAiHDn	MIND: Math Informed syNthetic Dialogues for Pretraining LLMs	[ 8, 5, 5 ]	MIND MIND: MATH INFORMED SYNTHETIC DIALOGUES FOR PRETRAINING LLMS Anonymous authors Paper under double-blind review ABSTRACT The utility of synthetic data to enhance pretraining data quality and hence to im- prove downstream task accuracy has been widely explored in recent large lan- guage models (LLMs). Yet, these...
0bmGL4q7vJ	Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage	[ 6, 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 MULTI-MODAL AGENT TUNING: BUILDING A VLM- DRIVEN AGENT FOR EFFICIENT TOOL USAGE Anonymous authors Paper under double-blind review Figure 1: The comparison of the LLM (GPT-4)-driven agent and our T3-Agent. Our agent chooses more precise tools based on the given files a...
wg1PCg3CUP	Scaling Laws for Precision	[ 8, 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 SCALING LAWS FOR PRECISION Anonymous authors Paper under double-blind review ABSTRACT Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise “precision-aware” ...
Tn8EQIFIMQ	Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice	[ 8, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 LANGUAGE MODELS TRAINED TO DO ARITHMETIC PREDICT HUMAN RISKY AND INTERTEMPORAL CHOICE Anonymous authors Paper under double-blind review ABSTRACT The observed similarities in the behavior of humans and Large Language Mod- els (LLMs) have prompted researchers to conside...
oI5tZaWkF9	Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification	[ 6, 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 NOT ALL LLM-GENERATED DATA ARE EQUAL: RETHINKING DATA WEIGHTING IN TEXT CLASSIFICA- TION Anonymous authors Paper under double-blind review ABSTRACT Synthetic data augmentation via Large Language Models (LLMs) allows re- searchers to leverage additional training data, ...
kGvXIlIVLM	Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment	[ 6, 8, 6, 6, 8, 8 ]	Under review as a conference paper at ICLR 2025 TOWARD GUIDANCE-FREE AR VISUAL GENERATION VIA CONDITION CONTRASTIVE ALIGNMENT Anonymous authors Paper under double-blind review ABSTRACT Classifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in au...
GR0y0F3Ipd	MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science	[ 8, 6, 6, 6 ]	000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 MAPS: ADVANCING MULTI-MODAL REASONING IN EXPERT-LEVEL PHYSICAL SCIENCE Anonymous authors Pape...
F5R0lG74Tu	DataGen: Unified Synthetic Dataset Generation via Large Language Models	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 DATAGEN: UNIFIED SYNTHETIC DATASET VIA LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation ...
YrycTjllL0	BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions	[ 8, 8, 10, 10 ]	Under review as a conference paper at ICLR 2025 BI GCO D EBE N C H: BENCHMARKING CODE GENERA- TION WITH DIVERSE FUNCTION CALLS AND COMPLEX INSTRUCTIONS Anonymous authors Paper under double-blind review ABSTRACT Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Pyt...
v8qABSeeKO	MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge	[ 8, 6, 5, 6 ]	Under review as a conference paper at ICLR 2025 MMKE-BENCH: A MULTIMODAL EDITING BENCH- MARK FOR DIVERSE VISUAL KNOWLEDGE Anonymous authors Paper under double-blind review ABSTRACT Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and mul...
pXlmOmlHJZ	In-Context Learning of Representations	[ 8, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 IN-CONTEXT LEARNING OF REPRESENTATIONS Anonymous authors Paper under double-blind review ABSTRACT Recent work demonstrates that structured patterns in pretraining data influence how representations of different concepts are organized in a large language model’s (LLM) ...
w5ZtXOzMeJ	Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation	[ 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 AUTO-GDA: AUTOMATIC DOMAIN ADAPTATION FOR GROUNDING VERIFICATION IN RETRIEVAL AUG- MENTED GENERATION Anonymous authors Paper under double-blind review ABSTRACT While retrieval augmented generation (RAG) has been shown to enhance factual- ity of large language model (L...
zG459X3Xge	VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 VISRAG: VISION-BASED RETRIEVAL-AUGMENTED GENERATION ON MULTI-MODALITY DOCUMENTS Anonymous authors Paper under double-blind review ABSTRACT Retrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external know...
8KQzoD5XAr	CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair	[ 8, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 CRAFTRTL: HIGH-QUALITY SYNTHETIC DATA GENERATION FOR VERILOG CODE MODELS WITH CORRECT-BY-CONSTRUCTION NON-TEXTUAL REP- RESENTATIONS AND TARGETED CODE REPAIR Anonymous authors Paper under double-blind review ABSTRACT Despite the significant progress made in code genera...
mVCcWCjeEz	ToEdit: How to Synthesize Text Data to Avoid Model Collapse?	[ 3, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 TOEDIT: HOW TO SYNTHESIZE TEXT DATA TO AVOID MODEL COLLAPSE? Anonymous authors Paper under double-blind review ABSTRACT We explore model collapse caused by synthetic data, where AI models trained on such data experience a gradual decline in performance. Our initial an...
DgaY5mDdmT	MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs	[ 5, 8, 8 ]	Under review as a conference paper at ICLR 2025 MLLMS KNOW WHERE TO LOOK: TRAINING-FREE PERCEPTION OF SMALL VISUAL DE- TAILS WITH MULTIMODAL LLMS Anonymous authors Paper under double-blind review ABSTRACT Multimodal Large Language Models (MLLMs) have recently achieved promising performance on visual question answer...
fGIqGfmgkW	OpenPRM: Building Open-domain Process-based Reward Models with Preference Trees	[ 8, 5, 5, 6 ]	Under review as a conference paper at ICLR 2025 OPENPRM: BUILDING OPEN-DOMAIN PROCESS- BASED REWARD MODELS WITH PREFERENCE TREES Anonymous authors Paper under double-blind review ABSTRACT Scaling inference-time computation is increasingly seen as the next frontier in scaling laws for large language models. Previous...
WjKea8bGFF	Building Math Agents with Multi-Turn Iterative Preference Learning	[ 6, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 BUILDING MATH AGENTS WITH MULTI-TURN ITERA- TIVE PREFERENCE LEARNING Anonymous authors Paper under double-blind review ABSTRACT Recent studies have shown that large language models’ (LLMs) mathematical problem-solving capabilities can be enhanced by integrating extern...
E2PFv7ad3p	Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs	[ 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 HAVE THE VISION-LANGUAGE MODELS LOST CONFI- DENCE? A STUDY OF SYCOPHANCY IN VLMS Anonymous authors Paper under double-blind review ABSTRACT Sycophancy, a common hallucination issue in large language models (LLMs), leads them to blindly agree with users, even when user...
599F4CZ0HB	Bench-O-Matic: Automating Benchmark Curation from Crowdsourced Data	[ 5, 8, 5 ]	Under review as a conference paper at ICLR 2025 BENCH-O-MATIC: AUTOMATING BENCHMARK CURATION FROM CROWDSOURCED DATA Anonymous authors Paper under double-blind review ABSTRACT The rapid evolution of Large Language Models (LLMs) has outpaced the develop- ment of model evaluation, highlighting the need for continuous ...
lja4JMesmC	From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning	[ 5, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 FROM GENERALIST TO SPECIALIST: ADAPTING VI- SION LANGUAGE MODELS VIA TASK-SPECIFIC VI- SUAL INSTRUCTION TUNING Anonymous authors Paper under double-blind review ABSTRACT Large vision language models (VLMs) combine large language models with vi- sion encoders, demonstr...
txoJvjfI9w	PEARL: Towards Permutation-Resilient LLMs	[ 6, 8, 8, 3 ]	Under review as a conference paper at ICLR 2025 PEARL: TOWARDS PERMUTATION-RESILIENT LLMS Anonymous authors Paper under double-blind review ABSTRACT The in-context learning (ICL) ability of large language models (LLMs) enables them to undertake challenging tasks using provided demonstrations. However, it is prone t...
x1Bk51SCL9	Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants	[ 8, 3, 6, 6 ]	Under review as a conference paper at ICLR 2025 FACE-HUMAN-BENCH: A COMPREHENSIVE BENCHMARK OF FACE AND HUMAN UNDERSTANDING FOR MULTI-MODAL ASSISTANTS Anonymous authors Paper under double-blind review ABSTRACT Faces and humans are crucial elements in social interaction and are widely in- cluded in everyday photos a...
Bgz3okeZ7H	AoPS Dataset: Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation	[ 8, 8, 6, 3 ]	Under review as a conference paper at ICLR 2025 AOPS DATASET: LEVERAGING ONLINE OLYMPIAD- LEVEL MATH PROBLEMS FOR LLMS TRAINING AND CONTAMINATION-RESISTANT EVALUATION Anonymous authors Paper under double-blind review ABSTRACT Advances in Large Language Models (LLMs) have sparked interest in their abil- ity to solve...
yaQbTAD2JJ	Language-Image Models with 3D Understanding	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 LANGUAGE-IMAGE MODELS WITH 3D UNDERSTANDING Anonymous authors Paper under double-blind review ABSTRACT Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs’ perceptual capabilities t...
1GTARJhxtq	Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models	[ 8, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 PERPLEXED BY PERPLEXITY: PERPLEXITY-BASED DATA PRUNING WITH SMALL REFERENCE MODELS Anonymous authors Paper under double-blind review ABSTRACT In this work, we investigate whether small language models can determine high- quality subsets of large-scale text datasets th...
GHJzxPgFa6	Chain of Ideas: Revolutionizing Research in Idea Development with LLM Agents	[ 5, 8, 5, 5 ]	Under review as a conference paper at ICLR 2025 CHAIN OF IDEAS: REVOLUTIONIZING RESEARCH IN NOVEL IDEA DEVELOPMENT WITH LLM AGENTS Anonymous authors Paper under double-blind review ABSTRACT Effective research ideation is a critical step for scientific research. However, the exponential increase in scientific litera...
M23dTGWCZy	Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers	[ 6, 6, 6, 5 ]	Under review as a conference paper at ICLR 2025 CAN LLMS GENERATE NOVEL RESEARCH IDEAS? A LARGE-SCALE HUMAN STUDY WITH 100+ NLP RESEARCHERS Anonymous authors Paper under double-blind review ABSTRACT Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scienti...
oqsQbn4XfT	On the Diversity of Synthetic Data and its Impact on Training Large Language Models	[ 8, 6, 6, 3, 6 ]	Under review as a conference paper at ICLR 2025 ON THE DIVERSITY OF SYNTHETIC DATA AND ITS IM- PACT ON TRAINING LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic da...
e9yfCY7Q3U	Improved Techniques for Optimization-Based Jailbreaking on Large Language Models	[ 6, 8, 6, 5 ]	Under review as a conference paper at ICLR 2025 IMPROVED TECHNIQUES FOR OPTIMIZATION-BASED JAILBREAKING ON LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) are being rapidly developed, and a key com- ponent of their widespread deployment is their safety-r...
XrsOu4KgDE	Attributing Culture-Conditioned Generations to Pretraining Corpora	[ 5, 6, 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 ATTRIBUTING CULTURE-CONDITIONED GENERATIONS TO PRETRAINING CORPORA Anonymous authors Paper under double-blind review ABSTRACT In open-ended generative tasks such as narrative writing or dialog interaction, large language models are known to manifest culture biases, sh...
tZCqSVncRf	MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models	[ 5, 6, 8, 5 ]	Under review as a conference paper at ICLR 2025 MIRAGE: EVALUATING AND EXPLAINING INDUCTIVE REASONING PROCESS IN LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Inductive reasoning is an essential capability for large language models (LLMs) to achieve higher intelligence, which requires t...
5RUM1aIdok	GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation	[ 8, 5, 8, 6 ]	Under review as a conference paper at ICLR 2025 GR A P HEV A L: A LIGHTWEIGHT GRAPH-BASED LLM FRAMEWORK FOR IDEA EVALUATION Anonymous authors Paper under double-blind review ABSTRACT The powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, partic...
X9OfMNNepI	Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses	[ 6, 5, 8, 6 ]	Under review as a conference paper at ICLR 2025 MOOSE-CHEM: LARGE LANGUAGE MODELS FOR REDISCOVERING UNSEEN CHEMISTRY SCIENTIFIC HYPOTHESES Anonymous authors Paper under double-blind review ABSTRACT Scientific discovery contributes largely to human society’s prosperity, and recent progress shows that LLMs could pote...
9QYJu1cGfE	Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models	[ 8, 6, 6, 5, 5 ]	Under review as a conference paper at ICLR 2025 QUO VADIS, MOTION GENERATION? FROM LARGE LANGUAGE MODELS TO LARGE MOTION MODELS Anonymous authors Paper under double-blind review ABSTRACT Inspired by recent success of LLMs, the field of human motion understanding has increasingly shifted towards the development of l...
1Iuw1jcIrf	MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code	[ 8, 6, 8 ]	Under review as a conference paper at ICLR 2025 MATHCODER2: BETTER MATH REASONING FROM CONTINUED PRETRAINING ON MODEL-TRANSLATED MATHEMATICAL CODE Anonymous authors Paper under double-blind review ABSTRACT Code has been shown to be effective in enhancing the mathematical reasoning abilities of large language models...
r7wMVdGFro	The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text	[ 5, 8, 5, 6 ]	Under review as a conference paper at ICLR 2025 THE CANARY’S ECHO: AUDITING PRIVACY RISKS OF LLM- GENERATED SYNTHETIC TEXT Anonymous authors Paper under double-blind review ABSTRACT How much information about training examples can be gleaned from synthetic data gen- erated by Large Language Models (LLMs)? Overlooki...
07yvxWDSla	Synthetic continued pretraining	[ 8, 8, 8, 8 ]	000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 SYNTHETIC CONTINUED PRETRAINING Anonymous authors Paper under double-blind review ABSTRACT ...
9RCT0ngvZP	Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning	[ 6, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 MONTESSORI-INSTRUCT: GENERATE INFLUENTIAL TRAINING DATA TAILORED FOR STUDENT LEARNING Anonymous authors Paper under double-blind review ABSTRACT Synthetic data has been widely used to train large language models, but their generative nature inevitably introduces noisy...
4hPwLg7zD3	Fourier Head: Helping Large Language Models Learn Complex Probability Distributions	[ 6, 5, 6, 8 ]	Under review as a conference paper at ICLR 2025 FOURIER HEAD: HELPING LARGE LANGUAGE MODELS LEARN COMPLEX PROBABILITY DISTRIBUTIONS Anonymous authors Paper under double-blind review ABSTRACT As the quality of large language models has improved, there has been increased interest in using them to model non-linguistic...
MnJzJ2gvuf	MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine	[ 6, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 MAVIS: MATHEMATICAL VISUAL INSTRUCTION TUNING WITH AN AUTOMATIC DATA ENGINE Anonymous authors Paper under double-blind review ABSTRACT Multi-modal Large Language Models (MLLMs) have recently showcased superior proficiency in general visual scenarios. However, we ident...
JtGPIZpOrz	Multiagent Finetuning of Language Models	[ 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 SELF IMPROVEMENT IN LANGUAGE MODELS THROUGH MULTIAGENT FINETUNING Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data...
590yfqz1LE	Measuring Non-Adversarial Reproduction of Training Data in Large Language Models	[ 6, 5, 8, 8, 8, 6, 5, 8 ]	Under review as a conference paper at ICLR 2025 MEASURING NON-ADVERSARIAL REPRODUCTION OF TRAINING DATA IN LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Large language models memorize parts of their training data. Memorizing short snippets and facts is required to answer questions...
kxnoqaisCT	Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents	[ 8, 8, 5, 10 ]	Under review as a conference paper at ICLR 2025 Navigating the Digital World as Humans Do: UNIVERSAL VISUAL GROUNDING FOR GUI AGENTS Anonymous authors Paper under double-blind review ABSTRACT Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilit...
hTphfqtafO	Large Language Models are Interpretable Learners	[ 5, 6, 8 ]	Under review as a conference paper at ICLR 2025 LARGE LANGUAGE MODELS ARE INTERPRETABLE LEARNERS Anonymous authors Paper under double-blind review ABSTRACT The trade-off between expressiveness and interpretability remains a core challenge when building human-centric models for classification and decision-making. Wh...
8m7p4k6Zeb	From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data	[ 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 FROM ARTIFICIAL NEEDLES TO REAL HAYSTACKS: IM- PROVING RETRIEVAL CAPABILITIES IN LLMS BY FINE- TUNING ON SYNTHETIC DATA Anonymous authors Paper under double-blind review ABSTRACT Recent studies have shown that Large Language Models (LLMs) struggle to accu- rately retr...
et5l9qPUhm	Strong Model Collapse	[ 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 STRONG MODEL COLLAPSE Anonymous authors Paper under double-blind review ABSTRACT Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised regression setting and establish a strong form of ...
lgsyLSsDRe	NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models	[ 8, 8, 6, 8 ]	Under review as a conference paper at ICLR 2025 NV-EMBED: IMPROVED TECHNIQUES FOR TRAINING LLMS AS GENERALIST EMBEDDING MODELS Anonymous authors Paper under double-blind review ABSTRACT Decoder-only large language model (LLM)-based embedding models are begin- ning to outperform BERT or T5-based embedding models in ...
MKEHCx25xp	WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild	[ 8, 6, 8 ]	Under review as a conference paper at ICLR 2025 WILDBENCH: BENCHMARKING LLMS WITH CHALLENGING TASKS FROM REAL USERS IN THE WILD Anonymous authors Paper under double-blind review ABSTRACT We introduce WildBench, an automated evaluation framework designed to bench- mark large language models (LLMs) using challenging,...
o9ewXD1JuB	OLAPH: Improving Factuality in Biomedical Long-form Question Answering	[ 5, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 OLAPH: LONG-FORM QUESTION ANSWERING IMPROVING FACTUALITY IN BIOMEDICAL Anonymous authors Paper under double-blind review ABSTRACT In the medical domain, numerous scenarios necessitate the long-form generation ability of large language models (LLMs). Specifically, whe...
IDJUscOjM3	Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 SELF-MOE: TOWARDS COMPOSITIONAL LARGE LAN- GUAGE MODELS WITH SELF-SPECIALIZED EXPERTS Anonymous authors Paper under double-blind review ABSTRACT We present Self-MoE, an approach that transforms a monolithic LLM into a com- positional, modular system of self-specialize...
0Fi3u4RCyU	Evolve: Evaluating and Optimizing LLMs For Exploration	[ 5, 8, 5, 8 ]	Under review as a conference paper at ICLR 2025 EVOLVE: EVALUATING AND OPTIMIZING LLMS FOR EXPLORATION Anonymous authors Paper under double-blind review ABSTRACT Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty....
3c4zQpIFNK	LIME: LESS IS MORE FOR MLLM EVALUATION	[ 5, 5, 8, 6 ]	Under review as a conference paper at ICLR 2025 LIME: LESS IS MORE FOR MLLM EVALUATION Anonymous authors Paper under double-blind review ABSTRACT Multimodal Large Language Models (MLLMs) are measured on numerous bench- marks like image captioning, visual question answer, and reasoning. However, these benchmarks oft...
KmmNb7631I	Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving	[ 6, 5, 8, 6 ]	Under review as a conference paper at ICLR 2025 LEARNING TO PLAN BEFORE ANSWERING: SELF- TEACHING LLMS TO LEARN ABSTRACT PLANS FOR PROBLEM SOLVING Anonymous authors Paper under double-blind review ABSTRACT In the field of large language model (LLM) post-training, the effectiveness of uti- lizing synthetic data gene...
vhPE3PtTgC	SWEb: A Large Web Dataset for the Scandinavian Languages	[ 8, 6, 6, 5 ]	Under review as a conference paper at ICLR 2025 SWEB: A LARGE WEB DATASET FOR THE SCANDINAVIAN LANGUAGES Anonymous authors Paper under double-blind review ABSTRACT This paper presents the hitherto largest pretraining dataset for the Scandinavian languages: the Scandinavian WEb (SWEb), comprising over one trillion t...
FpiCLJrSW8	More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness	[ 8, 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 MORE RLHF, MORE TRUST? ON THE IMPACT OF PREF- ERENCE ALIGNMENT ON TRUSTWORTHINESS Anonymous authors Paper under double-blind review ABSTRACT The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethical...
AqfUa08PCH	Training Language Models on Synthetic Edit Sequences Improves Code Synthesis	[ 6, 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 TRAINING LANGUAGE MODELS ON SYNTHETIC EDIT SEQUENCES IMPROVES CODE SYNTHESIS Anonymous authors Paper under double-blind review ABSTRACT Software engineers mainly write code by editing existing programs. In contrast, language models (LMs) autoregressively synthesize pr...
sfQ6XpApfS	PiCO: Peer Review in LLMs based on Consistency Optimization	[ 6, 6, 6 ]	000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 Under review as a conference paper at ICLR 2025 PICO: P EER REVIEW IN LLM S BASED ON CONSIS - TENCY OPTI...
wFs2E5wCw6	Tree of Attributes Prompt Learning for Vision-Language Models	[ 6, 6, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 TREE OF ATTRIBUTES PROMPT LEARNING FOR VISION- LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Prompt learning has proven effective in adapting vision language models for downstream tasks. However, existing methods usually append learnable p...
syThiTmWWm	Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates	[ 8, 10, 10, 8, 6, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 CHEATING AUTOMATIC LLM BENCHMARKS: NULL MODELS ACHIEVE HIGH WIN RATES Anonymous authors Paper under double-blind review ABSTRACT Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT- Bench, have become popular for evaluating language models due to...
zP8HygcAMY	Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking Using Knowledge Graphs	[ 6, 6, 5, 6 ]	Under review as a conference paper at ICLR 2025 CAN LLMS EVALUATE COMPLEX ATTRIBUTION IN QA? AUTOMATIC BENCHMARKING USING KNOWL- EDGE GRAPHS Anonymous authors Paper under double-blind review ABSTRACT The attribution of question answering (QA), which is to get evidences for sup- porting the generated answer, has att...
EDoD3DgivF	On Linear Representations and Pretraining Data Frequency in Language Models	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 ON LINEAR REPRESENTATIONS AND PRETRAINING DATA FREQUENCY IN LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Pretraining data has a direct impact on the behaviors and quality of language mod- els (LMs), but we only understand the most basic p...
jjCB27TMK3	Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance	[ 8, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 DATA MIXING LAWS: OPTIMIZING DATA MIXTURES BY PREDICTING LANGUAGE MODELING PERFORMANCE Anonymous authors Paper under double-blind review ABSTRACT Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixtu...
jki6EFsZLw	OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 OMNI R: EVALUATING OMNI-MODALITY LANGUAGE MODELS ON REASONING ACROSS MODALITIES Anonymous authors Paper under double-blind review ABSTRACT We introduce Omni×R, an evaluation suite designed to benchmark state-of-the- art Omni-modality Language Models (OLMs), such as GP...
UsRKFYR4lM	Mitigating Spurious Correlations in Zero-Shot Multimodal Models	[ 6, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 MITIGATING SPURIOUS CORRELATIONS IN ZERO- SHOT MULTIMODAL MODELS Anonymous authors Paper under double-blind review ABSTRACT Multimodal models or Vision Language Models (VLMs) have reshaped the paradigm in machine learning, offering zero-shot capabilities that require ...
8EB8k6DdCU	ToolACE: Enhancing Function Calling with Accuracy, Complexity, and Diversity	[ 6, 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 TOOLACE: ENHANCING FUNCTION CALLING WITH ACCURACY, COMPLEXITY, AND DIVERSITY Anonymous authors Paper under double-blind review ABSTRACT Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse trai...
BkwCrIsTbR	Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation	[ 6, 6, 6, 6 ]	Under review as a conference paper at ICLR 2025 SCALING INSTRUCTION-TUNED LLMS TO MILLION- TOKEN CONTEXTS VIA HIERARCHICAL SYNTHETIC DATA GENERATION Anonymous authors Paper under double-blind review ABSTRACT Large Language Models (LLMs) struggle with long-context reasoning, not only due to the quadratic scaling of ...
vPOMTkmSiu	Scaling Laws for Downstream Task Performance in Machine Translation	[ 3, 6, 8, 8, 8 ]	Under review as a conference paper at ICLR 2025 SCALING LAWS FOR DOWNSTREAM TASK PERFORMANCE IN MACHINE TRANSLATION Anonymous authors Paper under double-blind review ABSTRACT Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on stu...
VOAMTA8jKu	DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models	[ 6, 6, 8, 8 ]	Under review as a conference paper at ICLR 2025 DYNAMATH: A DYNAMIC VISUAL BENCHMARK FOR EVALUATING MATHEMATICAL REASONING ROBUSTNESS OF VISION LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT The rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling m...
iv1TpRCJeK	$\forall$uto$\exists$$\lor\!\land$L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks	[ 8, 6, 5 ]	Under review as a conference paper at ICLR 2025 UTO L: AUTONOMOUS EVALUATION OF LLMS ∀ FOR TRUTH MAINTENANCE AND REASONING TASKS ∃∨∧ Anonymous authors Paper under double-blind review ABSTRACT ∀ ∀ uto uto ∃∨∧ This paper presents L, a novel benchmark for scaling Large Language Model (LLM) assessment in formal...
suz4utPr9Y	How efficient is LLM-generated code? A rigorous & high-standard benchmark	[ 6, 5, 6, 6 ]	Under review as a conference paper at ICLR 2025 HOW EFFICIENT IS LLM-GENERATED CODE? A RIGOROUS & HIGH-STANDARD BENCHMARK Anonymous authors Paper under double-blind review ABSTRACT The emergence of large language models (LLMs) has significantly pushed the frontiers of program synthesis. Advancement of LLM-based pro...
ybfmpJiKXX	AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements	[ 6, 8, 5 ]	Under review as a conference paper at ICLR 2025 AIMS.AU: A DATASET FOR THE ANALYSIS OF MODERN SLAVERY COUNTERMEASURES IN CORPORATE STATEMENTS Anonymous authors Paper under double-blind review ABSTRACT Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, ...
QxbJYBZVbE	CursorCore: Assist Programming through Aligning Anything	[ 8, 5, 6, 5 ]	Under review as a conference paper at ICLR 2025 CURSORCORE: ASSIST PROGRAMMING THROUGH ALIGNING ANYTHING Anonymous authors Paper under double-blind review ABSTRACT Large language models have been successfully applied to programming assistance tasks, such as code completion, code insertion, and instructional code ed...
V892sBHUbN	Rapid Response: Mitigating LLM Jailbreaks With A Few Examples	[ 5, 8, 5, 5 ]	Under review as a conference paper at ICLR 2025 RAPID RESPONSE: MITIGATING LLM JAILBREAKS WITH A FEW EXAMPLES Anonymous authors Paper under double-blind review ABSTRACT As large language models (LLMs) grow more powerful, ensuring their safety against misuse becomes crucial. While researchers have focused on develop...
womU9cEwcO	Autonomous agents from automatic reward modeling and planning	[ 6, 6, 8 ]	Under review as a conference paper at ICLR 2025 ARMAP: AUTONOMOUS AGENTS FROM AUTOMATIC REWARD MODELING AND PLANNING Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) have demonstrated remarkable capabilities across a range of text-generation tasks. However, LLMs still struggle...
3UKOzGWCVY	Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments	[ 6, 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 LEARN-BY-INTERACT: A DATA-CENTRIC FRAME- WORK FOR SELF-ADAPTIVE AGENTS IN REALISTIC ENVIRONMENTS Anonymous authors Paper under double-blind review ABSTRACT Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assi...
jlzNb1iWs3	The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling	[ 5, 6, 8, 5 ]	Under review as a conference paper at ICLR 2025 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling Anonymous authors Paper under double-blind review Abstract Biological language model performance depends heavily on pretraining data quality, diversity, and size. While metagenomi...
huuKoVQnB0	Improving Pretraining Data Using Perplexity Correlations	[ 6, 5, 8, 5, 6 ]	Under review as a conference paper at ICLR 2025 IMPROVING PRETRAINING DATA USING PERPLEXITY CORRELATIONS Anonymous authors Paper under double-blind review ABSTRACT Quality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slo...
UxkznlcnHf	Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective	[ 3, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 TOWARDS A THEORETICAL UNDERSTANDING OF SYN- THETIC DATA IN LLM POST-TRAINING: A REVERSE-BOTTLENECK PERSPECTIVE Anonymous authors Paper under double-blind review ABSTRACT Synthetic data has become a pivotal resource in post-training tasks for large lan- guage models (L...
s5epFPdIW6	MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models	[ 6, 6, 8, 8 ]	Under review as a conference paper at ICLR 2025 MMED-RAG: VERSATILE MULTIMODAL RAG SYS- TEM FOR MEDICAL VISION LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment...
ymt4crbbXh	AutoBencher: Towards Declarative Benchmark Construction	[ 5, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 AUTOBENCHER: TOWARDS DECLARATIVE BENCHMARK CONSTRUCTION Anonymous authors Paper under double-blind review ABSTRACT We present AutoBencher, a declarative framework for automatic benchmark construction, and use it to scalably discover novel insights and vulnerabilities ...
qssVptHTPN	Locality Alignment Improves Vision-Language Models	[ 5, 6, 5, 8 ]	Under review as a conference paper at ICLR 2025 LOCALITY ALIGNMENT IMPROVES VISION-LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Vision language models (VLMs) have seen growing adoption in recent years, but many still struggle with basic spatial reasoning errors. We hypothesize that thi...
cFu7ze7xUm	DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads	[ 8, 5, 5, 6, 8, 6 ]	Under review as a conference paper at ICLR 2025 DUOATTENTION: EFFICIENT LONG-CONTEXT LLM INFERENCE WITH RETRIEVAL AND STREAMING HEADS Anonymous authors Paper under double-blind review ABSTRACT Deploying long-context large language models (LLMs) is essential but poses significant computational and memory challenges....
3OyaXFQuDl	Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling	[ 8, 8, 6, 6 ]	Under review as a conference paper at ICLR 2025 SMALLER, WEAKER, YET BETTER: TRAINING LLM REASONERS VIA COMPUTE-OPTIMAL SAMPLING Anonymous authors Paper under double-blind review ABSTRACT Training on high-quality synthetic data from strong language models (LMs) is a common strategy to improve the reasoning performa...
IHRQif8VQC	Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness	[ 5, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 ENSEMBLE EVERYTHING EVERYWHERE: MULTI- SCALE AGGREGATION FOR ADVERSARIAL ROBUST- NESS Anonymous authors Paper under double-blind review ABSTRACT Adversarial examples pose a signiﬁcant challenge to the robustness, reliability and alignment of deep neural networks. We p...
OQqNieeivq	KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models	[ 6, 8, 8, 5, 6 ]	Under review as a conference paper at ICLR 2025 KASA: KNOWLEDGE-AWARE ADAPTATION OF LARGE LANGUAGE MODELS SINGULAR-VALUE Anonymous authors Paper under double-blind review ABSTRACT The increasing sizes of large language models (LLMs) result in significant com- putational overhead and memory usage when adapting thes...
cRR0oDFEBC	Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models	[ 6, 8, 8, 8, 6 ]	Under review as a conference paper at ICLR 2025 SELF-PLAY WITH EXECUTION FEEDBACK: IMPROVING INSTRUCTION-FOLLOWING CAPABILITIES OF LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT One core capability of large language models (LLMs) is to follow natural language instructions. However,...

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