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Found 43 papers, 28 papers with code
VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored.
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment
As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial.
Ranked #44 on
Visual Question Answering
on MM-Vet
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences.
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback
In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure.
Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition
Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents.
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices.
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
To address this, we introduce Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing instruction tuning examples in multimodal embodied environments.
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature.
Ranked #26 on
Math Word Problem Solving
on MATH
(using extra training data)
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
AssistGUI: Task-Oriented PC Graphical User Interface Automation
Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks thereby boosting human productivity.
ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation
Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks, thereby boosting human productivity.
Silkie: Preference Distillation for Large Visual Language Models
This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context.
Ranked #47 on
Visual Question Answering
on MM-Vet
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions.
Ranked #22 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Guiding AMR Parsing with Reverse Graph Linearization
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence.
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning
To assess the quality of weights in the absence of additional validation data, we design a masked self-prediction (MSP) score that exhibits a strong correlation with the final ICL performance.
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
Rationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.
Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond
In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents.
Making Large Language Models Better Reasoners with Alignment
To address this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm, which involves three steps: 1) fine-tuning LLMs with COT training data; 2) generating multiple COT responses for each question, and categorizing them into positive and negative ones based on whether they achieve the correct answer; 3) calibrating the scores of positive and negative responses given by LLMs with a novel constraint alignment loss.
M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions.
Large Language Models are not Fair Evaluators
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e. g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models.
RepCL: Exploring Effective Representation for Continual Text Classification
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
Enhancing Continual Relation Extraction via Classifier Decomposition
In this work, we point out that there exist two typical biases after training of this vanilla strategy: classifier bias and representation bias, which causes the previous knowledge that the model learned to be shaded.
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
2) Balanced Tuning (BT) finetunes the model on the balanced memory data.
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document.
Abstract Meaning Representation
Document-level Event Extraction
+3
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potentials of PLMs can not be fully tapped.
SmartSales: Sales Script Extraction and Analysis from Sales Chatlog
2) Customer objection response assists the salespeople to figure out the typical customer objections and corresponding winning sales scripts, as well as search for proper sales responses for a certain customer objection.
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing.
Ranked #7 on
AMR Parsing
on LDC2020T02
(using extra training data)
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy.
Hierarchical Curriculum Learning for AMR Parsing
Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models.
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e. g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain.
Ranked #7 on
Few-shot NER
on Few-NERD (INTER)
Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification
However, we find they suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types, which we summarize as trigger overlapping and trigger separability.
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Table encoder extracts sentiment at token-pair level, so that the compositional feature between targets and opinions can be easily captured.
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.
Ranked #9 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews.
