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Found 18 papers, 11 papers with code
ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning
To address these challenges, we present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library.
Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation
This paper delves into the effects of decoding temperatures on speculative decoding's efficacy.
ParallelSpec: Parallel Drafter for Efficient Speculative Decoding
Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target model is leveraged to verify them in parallel.
RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph
Unlike traditional function-level or file-level coding tasks, AI software engineering requires not only basic coding proficiency but also advanced skills in managing and interacting with code repositories.
Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks
Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs.
Unsupervised Episode Detection for Large-Scale News Events
Episodic structures are inherently interpretable and adaptable to evolving large-scale key events.
Multi-LoRA Composition for Image Generation
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
Investigating Data Contamination for Pre-training Language Models
Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks.
Structured Chemistry Reasoning with Large Language Models
Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry.
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
Ontology Enrichment for Effective Fine-grained Entity Typing
In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples.
ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
Towards End-to-End Open Conversational Machine Reading
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base.
Logic Pre-Training of Language Models
Pre-trained language models (PrLMs) have been shown useful for enhancing a broad range of natural language understanding (NLU) tasks.
Smoothing Dialogue States for Open Conversational Machine Reading
In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference.
Fact-driven Logical Reasoning for Machine Reading Comprehension
Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms.
Ranked #23 on
Reading Comprehension
on ReClor
Dialogue Graph Modeling for Conversational Machine Reading
Conversational Machine Reading (CMR) aims at answering questions in a complicated manner.
