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Found 9 papers, 2 papers with code
EvoWiki: Evaluating LLMs on Evolving Knowledge
Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment.
PSPO*: An Effective Process-supervised Policy Optimization for Reasoning Alignment
Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning.
Speaker Verification in Agent-Generated Conversations
The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks.
Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models
There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, preserving stylistic and contextual essence, and 2) extending strategy that further expands existing samples at varying cognitive levels by adapting Bloom's taxonomy of educational objectives.
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source.
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Language style is necessary for AI systems to understand and generate diverse human language accurately.
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.
Building Knowledge-Grounded Dialogue Systems with Graph-Based Semantic Modeling
However, it is a challenge for the current sequence-based model to acquire knowledge from complex documents and integrate it to perform correct responses without the aid of an explicit semantic structure.
Ask to Understand: Question Generation for Multi-hop Question Answering
Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance.
