Search Results for author:
Found 17 papers, 4 papers with code
Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning
Large language models have demonstrated impressive reasoning capabilities, yet they often suffer from inefficiencies due to unnecessarily verbose or redundant outputs.
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models
In this paper, we propose Formula Tuning (Fortune), a reinforcement learning (RL) framework that trains LMs to generate executable spreadsheet formulas for question answering over general tabular data.
Process Reward Modeling with Entropy-Driven Uncertainty
This paper presents the Entropy-Driven Unified Process Reward Model (EDU-PRM), a novel framework that approximates state-of-the-art performance in process supervision while drastically reducing training costs.
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
Tabular data analysis is crucial in many scenarios, yet efficiently identifying the most relevant data analysis queries and results for a new table remains a significant challenge.
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
We introduce DeepRetrieval, a reinforcement learning (RL) approach that trains LLMs for query generation through trial and error without supervised data (reference query).
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of retrieved context.
TableMaster: A Recipe to Advance Table Understanding with Language Models
While current language models (LMs) excel at many text-based tasks, they still face challenges in table understanding due to the complex characteristics of tabular data, such as their structured nature.
A foundation model for human-AI collaboration in medical literature mining
Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature.
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning
Mathematical reasoning has been challenging for large language models (LLMs).
Accelerating Clinical Evidence Synthesis with Large Language Models
Here, we introduce TrialMind, a generative artificial intelligence (AI) pipeline for facilitating human-AI collaboration in three crucial tasks for evidence synthesis: study search, screening, and data extraction.
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery.
AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models
Rare diseases affect millions worldwide but often face limited research focus due to their low prevalence.
PILOT: Legal Case Outcome Prediction with Case Law
We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism
To achieve this, we utilize a structured knowledge base to represent all the LLM's understanding of the world, enabling it to provide traceable gold knowledge.
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining
Argument mining is to analyze argument structure and extract important argument information from unstructured text.
GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
By evaluating these graphs, models can yield more accurate and reliable results. Our experimental results show that our graph-based verification method not only significantly enhances the reasoning abilities of LLMs but also outperforms existing verifier methods in terms of improving these models' reasoning performance.
DiagGPT: An LLM-based and Multi-agent Dialogue System with Automatic Topic Management for Flexible Task-Oriented Dialogue
A significant application of Large Language Models (LLMs), like ChatGPT, is their deployment as chat agents, which respond to human inquiries across a variety of domains.
