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Found 34 papers, 19 papers with code
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
To address this, we propose \textbf{R1-Searcher}, a novel two-stage outcome-based RL approach designed to enhance the search capabilities of LLMs.
An Empirical Study on Eliciting and Improving R1-like Reasoning Models
This approach achieves a remarkable accuracy of 86. 67% with greedy search on AIME 2024, underscoring its effectiveness in enhancing model capabilities.
VersaGen: Unleashing Versatile Visual Control for Text-to-Image Synthesis
Despite the rapid advancements in text-to-image (T2I) synthesis, enabling precise visual control remains a significant challenge.
Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
We introduce an ``imitate, explore, and self-improve'' framework, denoted as \textbf{STILL-2}, as our primary technical approach to train the reasoning model.
Enhancing LLM Reasoning with Reward-guided Tree Search
This framework is implemented by integrating the policy model, reward model, and search algorithm.
Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models
In the extraction stage, we firstly locate key neurons that are highly related to specific abilities, and then employ them to extract the transferable ability-specific weights.
Towards Effective and Efficient Continual Pre-training of Large Language Models
To make the CPT approach more traceable, this paper presents a technical report for continually pre-training Llama-3 (8B), which significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model.
YuLan: An Open-source Large Language Model
This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters.
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment
Concretely, we first identify the neurons that are related to the human preference data by a gradient-based strategy, then identify the alignment-related key tokens by reward models for computing loss.
Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR
This paper addresses challenges in integrating new languages into a pre-trained multilingual automatic speech recognition (mASR) system, particularly in scenarios where training data for existing languages is limited or unavailable.
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
We leverage it to synthesize 6 million math problems for pre-training our JiuZhang3. 0 model, which only needs to invoke GPT-4 API 9. 3k times and pre-train on 4. 6B data.
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
To address it, we propose a new RL method named RLMEC that incorporates a generative model as the reward model, which is trained by the erroneous solution rewriting task under the minimum editing constraint, and can produce token-level rewards for RL training.
Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT
A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT.
Don't Make Your LLM an Evaluation Benchmark Cheater
Large language models~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity.
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Specially, we construct a Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to capture the common mathematical knowledge across tasks.
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specific knowledge and multi-hop reasoning.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on
Abstractive Text Summarization
on CNN/Daily Mail
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval
In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment.
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing.
Contextual Recurrent Units for Cloze-style Reading Comprehension
Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.
Secure Detection of Image Manipulation by means of Random Feature Selection
We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector.
Cryptography and Security
Dataset for the First Evaluation on Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Attention-over-Attention Neural Networks for Reading Comprehension
Cloze-style queries are representative problems in reading comprehension.
Ranked #3 on
Question Answering
on Children's Book Test
Consensus Attention-based Neural Networks for Chinese Reading Comprehension
Reading comprehension has embraced a booming in recent NLP research.
