Search Results for author:
Found 13 papers, 7 papers with code
Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams
We present a framework for large-scale sentiment and topic analysis of Twitter discourse.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Vote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning Pipeline via Voting
Integrating large language models (LLMs) into closed-loop robotic task planning has become increasingly popular within embodied artificial intelligence.
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e. g., 60. 8 on AIME, 94. 6 on MATH500, 47. 3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3. 5 by a large margin (up to +550%).
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models
Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks.
Analyzing Nobel Prize Literature with Large Language Models
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis.
Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities.
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
In this paper, we propose UnifiedMLLM, a comprehensive model designed to represent various tasks using a unified representation.
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
For negative knowledge, we propose an innovative self-adversarial approach that generates low-quality rationales by sampling previous iterations of smaller language models, embracing the idea that one can learn from one's own weaknesses.
SpeechAlign: Aligning Speech Generation to Human Preferences
However, the integration of human feedback to align speech outputs to human preferences is often neglected.
CMP: Cooperative Motion Prediction with Multi-Agent Communication
Through extensive experiments and ablation studies on the OPV2V and V2V4Real datasets, we demonstrate the effectiveness of our method in cooperative perception, tracking, and motion prediction.
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
In this paper, we propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
