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Found 11 papers, 4 papers with code
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL
Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation, bridging the gap between database structures and language models.
STaR-SQL: Self-Taught Reasoner for Text-to-SQL
Generating step-by-step "chain-of-thought" rationales has proven effective for improving the performance of large language models on complex reasoning tasks.
HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Device Scenarios
Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior.
LLM-based Multi-Agent Systems: Techniques and Business Perspectives
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents.
Hammer: Robust Function-Calling for On-Device Language Models via Function Masking
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls.
P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for data pruning in LLM Training
In the rapidly advancing field of Large Language Models (LLMs), effectively leveraging existing datasets during fine-tuning to maximize the model's potential is of paramount importance.
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
A Framework to Implement 1+N Multi-task Fine-tuning Pattern in LLMs Using the CGC-LORA Algorithm
In practice, there are two prevailing ways, in which the adaptation can be achieved: (i) Multiple Independent Models: Pre-trained LLMs are fine-tuned a few times independently using the corresponding training samples from each task.
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.
Graph Propagation Transformer for Graph Representation Learning
The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks.
Ranked #2 on
Graph Regression
on PCQM4M-LSC
(Validation MAE metric)
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods.
