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Found 15 papers, 10 papers with code
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
A Survey on Data Selection for Language Models
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training.
Efficient Online Data Mixing For Language Model Pre-Training
The data used to pretrain large language models has a decisive impact on a model's downstream performance, which has led to a large body of work on data selection methods that aim to automatically determine the most suitable data to use for pretraining.
RWKV: Reinventing RNNs for the Transformer Era
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on
Natural Language Inference
on WNLI
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
We also introduce a self-refinement module, which utilizes the symbolic solver's error messages to revise symbolic formalizations.
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
In this work, we focus on Few-shot Learning with Auxiliary Data (FLAD), a training paradigm that assumes access to auxiliary data during few-shot learning in hopes of improving generalization.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency.
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
NeuPSL: Neural Probabilistic Soft Logic
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains.
D-REX: Dialogue Relation Extraction with Explanations
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
Modeling Disclosive Transparency in NLP Application Descriptions
Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.
