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Found 19 papers, 14 papers with code
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, yet existing supervised fine-tuning (SFT) approaches suffer from limitations such as data saturation and restricted learning capacity bounded by teacher signals.
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos.
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
To bridge this gap, we first formalize the paradigm of long-context reasoning RL, and identify key challenges in suboptimal training efficiency and unstable optimization process.
QwenLong-CPRS: Towards $\infty$-LLMs with Dynamic Context Optimization
This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during the prefill stage and the "lost in the middle" performance degradation of large language models (LLMs) during long sequence processing.
Mutual-Taught for Co-adapting Policy and Reward Models
During the preference optimization of large language models (LLMs), distribution shifts may arise between newly generated model samples and the data used to train the reward model (RM).
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
SoLoPO is compatible with mainstream preference optimization algorithms, while substantially improving the efficiency of data construction and training processes.
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
However, the two major navigation challenges in mobile device operation tasks, task progress navigation and focus content navigation, are significantly complicated under the single-agent architecture of existing work.
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
In this paper, we introduce SocialBench, the first benchmark designed to systematically evaluate the sociality of role-playing conversational agents at both individual and group levels of social interactions.
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
To assess the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations.
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task.
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses.
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog
Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses.
Generic Dependency Modeling for Multi-Party Conversation
To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances.
Joint Generator-Ranker Learning for Natural Language Generation
Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates.
Directed Acyclic Graph Network for Conversational Emotion Recognition
In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea.
Ranked #10 on
Emotion Recognition in Conversation
on DailyDialog
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data.
Ranked #2 on
Emotion Recognition in Conversation
on CPED
Constituency Lattice Encoding for Aspect Term Extraction
One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms.
Aspect Term Extraction and Sentiment Classification
Sentence
+1
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
