Deep Cognition and Language Research (DeCLaRe) Lab

awesome-emotion-recognition-in-conversations

A comprehensive reading list for Emotion Recognition in Conversations

awesome-sentiment-analysis

Reading list for Awesome Sentiment Analysis papers

conv-emotion

This repo contains implementation of different architectures for emotion recognition in conversations.

flan-alpaca

This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.

instruct-eval

This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.

MELD

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation

multimodal-deep-learning

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

nora

NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

tango

A family of diffusion models for text-to-audio generation.

TangoFlux

TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching

declare-lab/tango

A family of diffusion models for text-to-audio generation.

declare-lab/MELD

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation

declare-lab/TangoFlux

TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching

declare-lab/instruct-eval

This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.

declare-lab/nora

NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

declare-lab/jamify

JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment

declare-lab/LLM-PuzzleTest

This repository is maintained to release dataset and models for multimodal puzzle reasoning.

declare-lab/Emma-X

Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning

declare-lab/trust-align

Codes and datasets for the paper Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

declare-lab/HyperTTS

declare-lab/della

DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling

declare-lab/MM-Align

[EMNLP 2022] This repository contains the official implementation of the paper "MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences"

declare-lab/MM-InstructEval

This repository contains code to evaluate various multimodal large language models using different instructions across multiple multimodal content comprehension tasks.

declare-lab/resta

Restore safety in fine-tuned language models through task arithmetic

declare-lab/ferret

Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique

declare-lab/adapter-mix

declare-lab/safety-arithmetic

declare-lab/Sealing

[NAACL 2024] Official Implementation of paper "Self-Adaptive Sampling for Efficient Video Question Answering on Image--Text Models"

declare-lab/LLM-ReasoningTest

Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions

declare-lab/Auto-Scaling

[Arxiv 2024] Official Implementation of the paper: "Towards Robust Instruction Tuning on Multimodal Large Language Models"

declare-lab/OffTopicEval

declare-lab/PathFinder-PRM

This repository contains the official implementation of Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision.

declare-lab/PromptDistill

declare-lab/PROVE

declare-lab/darwin

declare-lab/dialogxpert

Codebase for ProactiveAI in conversations

declare-lab/KAIROS

KAIROS: An LLM Eval Technique to Evaluate Multi-Agent Social Interactions

declare-lab/declare-lab.github.io

declare-lab/.github

declare-lab/vlprm

This repository contains the official implementation of Training Vision-Language Process Reward Models for Test-Time Scaling in Multimodal Reasoning: Key Insights and Lessons Learned