Search Results for author:
Found 21 papers, 9 papers with code
FuseChat: Knowledge Fusion of Chat Models
Recently, \textsc{FuseLLM} introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training.
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models
Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs).
Knowledge Verification to Nip Hallucination in the Bud
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as \emph{hallucination}.
Knowledge Fusion of Large Language Models
In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
We present Inferflow, an efficient and highly configurable inference engine for large language models (LLMs).
Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks
Humans read texts at a varying pace, while machine learning models treat each token in the same way in terms of a computational process.
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving
Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including mathematical problem-solving.
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks.
TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design
High-quality instruction-tuning data is critical to improving LLM capabilities.
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied.
Pre-training Multi-party Dialogue Models with Latent Discourse Inference
Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows.
Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance
Proprietary Large Language Models (LLMs), such as ChatGPT, have garnered significant attention due to their exceptional capabilities in handling a diverse range of tasks.
Effidit: Your AI Writing Assistant
In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering
Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks.
Generalizable and Explainable Dialogue Generation via Explicit Action Learning
To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation.
KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation
This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation.
Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation
This approach requires complete state-action annotations of human-to-human dialogues (i. e., expert demonstrations), which is labor intensive.
MALA: Cross-Domain Dialogue Generation with Action Learning
These two components, however, have a discrepancy in their objectives, i. e., task completion and language quality.
CARL: Aggregated Search with Context-Aware Module Embedding Learning
To model and utilize the context information for aggregated search, we propose a model with context attention and representation learning (CARL).
