Search Results for author:
Found 33 papers, 15 papers with code
Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge
Agentic search such as Deep Research systems, where large language models autonomously browse the web, synthesize information, and return comprehensive citation-backed answers, represents a major shift in how users interact with web-scale information.
Efficient Medical VIE via Reinforcement Learning
Fine-tuning Qwen2. 5-VL-7B with our RLVR method, we achieve state-of-the-art performance on medical VIE tasks, significantly improving F1, precision, and recall.
ARM: Adaptive Reasoning Model
In this work, we propose Adaptive Reasoning Model (ARM), a reasoning model capable of adaptively selecting appropriate reasoning formats based on the task at hand.
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs).
UniEDU: A Unified Language and Vision Assistant for Education Applications
Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these materials.
LLM Agents for Education: Advances and Applications
Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.
Implicit Reasoning in Transformers is Reasoning through Shortcuts
Test-time compute is emerging as a new paradigm for enhancing language models' complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI's o1 and o3, as well as DeepSeek's R1.
Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators
By inputting medical records into our patient simulator to simulate patient responses, we conduct extensive experiments to explore the relationship between "inquiry" and "diagnosis" in the consultation process.
Baichuan4-Finance Technical Report
Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge.
Enhancing LLM Reasoning with Reward-guided Tree Search
This framework is implemented by integrating the policy model, reward model, and search algorithm.
AAAR-1.0: Assessing AI's Potential to Assist Research
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation.
Revealing the Barriers of Language Agents in Planning
Although existing studies have highlighted weak performance in agent planning, the deeper underlying issues and the mechanisms and limitations of the strategies proposed to address them remain insufficiently understood.
Boosting Deductive Reasoning with Step Signals In RLHF
Logical reasoning is a crucial task for Large Language Models (LLMs), enabling them to tackle complex problems.
Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown
Reward models (RM) play a critical role in aligning generations of large language models (LLM) to human expectations.
Reward-Robust RLHF in LLMs
As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pathway toward achieving Artificial General Intelligence (AGI).
Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning
To address these limitations, we introduce Sibyl, a simple yet powerful LLM-based agent framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools.
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation.
3D-Properties: Identifying Challenges in DPO and Charting a Path Forward
Aligning large language models (LLMs) with human preference has recently gained tremendous attention, with the canonical yet costly RLHF-PPO and the simple and straightforward Direct Preference Optimization (DPO) as two examples.
SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling
Human preference alignment is critical in building powerful and reliable large language models (LLMs).
From Persona to Personalization: A Survey on Role-Playing Language Agents
Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.
How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
By leveraging the retrieval of information from external knowledge databases, Large Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge-intensive tasks.
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks.
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents?
Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning
Recent advancements have significantly augmented the reasoning capabilities of Large Language Models (LLMs) through various methodologies, especially chain-of-thought (CoT) reasoning.
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search
In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search.
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS
Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system.
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations
Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations.
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system.
Shift Convolution Network for Stereo Matching
In this paper, we present Shift Convolution Network (ShiftConvNet) to provide matching capability between two feature maps for stereo estimation.
