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Found 22 papers, 13 papers with code
A Unified Encoding of Structures in Transition Systems
Transition systems usually contain various dynamic structures (e. g., stacks, buffers).
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction
Event detection is a classic natural language processing task.
Word Reordering for Zero-shot Cross-lingual Structured Prediction
Adapting word order from one language to another is a key problem in cross-lingual structured prediction.
AntLM: Bridging Causal and Masked Language Models
For the BabyLM Challenge 2024, we propose a novel language modeling paradigm named $\textbf{AntLM}$, which integrates both CLM and MLM to leverage the advantages of these two classic paradigms.
Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
Experiments show that the method improves the actor's exploration efficiency and solution diversity, especially on challenging queries, leading to a stronger reasoning model.
Multi-Programming Language Sandbox for LLMs
We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
Our findings indicate that the ability to prevent sycophancy is predominantly observed in higher layers of the model.
Generation with Dynamic Vocabulary
The dynamic vocabulary can be deployed in a plug-and-play way, thus is attractive for various downstream applications.
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models.
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance.
Length Generalization of Causal Transformers without Position Encoding
Generalizing to longer sentences is important for recent Transformer-based language models.
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks.
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
The advancement of large language models (LLMs) has significantly propelled the field of code generation.
MouSi: Poly-Visual-Expert Vision-Language Models
This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs.
Ranked #93 on
Visual Question Answering
on MM-Vet
Secrets of RLHF in Large Language Models Part II: Reward Modeling
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data.
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
Existing evaluations of tool learning primarily focus on validating the alignment of selected tools for large language models (LLMs) with expected outcomes.
FOSS: A Self-Learned Doctor for Query Optimizer
They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints.
Evaluator for Emotionally Consistent Chatbots
One challenge for evaluating current sequence- or dialogue-level chatbots, such as Empathetic Open-domain Conversation Models, is to determine whether the chatbot performs in an emotionally consistent way.
Generating CCG Categories
Previous CCG supertaggers usually predict categories using multi-class classification.
Graph-based Dependency Parsing with Graph Neural Networks
We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing.
Ranked #12 on
Dependency Parsing
on Penn Treebank
AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task.
A Fast and Lightweight System for Multilingual Dependency Parsing
We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier.
