Conversational Question Answering over Knowledge Graphs Using Reformulations and Reinforcement Learning

Centrala begrepp

A reinforcement learning-based model, CoRnNet, utilizes question reformulations generated by large language models to improve conversational question answering performance over knowledge graphs.

Sammanfattning

The paper proposes a reinforcement learning-based model, CoRnNet, for conversational question answering (ConvQA) over knowledge graphs (KGs).
The key highlights are:

Analysis: The authors demonstrate that while large language models (LLMs) are good at generating question reformulations, their performance still lags behind human-written reformulations.

Algorithm: CoRnNet uses a teacher-student architecture to leverage the strengths of both human-written and LLM-generated reformulations. The teacher model is trained on human-written reformulations, while the student model is trained to mimic the teacher's output using LLM-generated reformulations.

Reinforcement Learning: CoRnNet formulates the ConvQA task as a Markov Decision Process and uses reinforcement learning to locate the correct answer in the KG, guided by the learned question representations.

Experiments: Extensive experiments on two real-world ConvQA datasets show that CoRnNet outperforms state-of-the-art baselines, achieving near human-level performance.

Statistik

The knowledge graph used in the experiments contains approximately 2 billion triples from Wikidata.
The ConvQuestions dataset contains 6,720 conversations, each with 5 turns.
The ConvRef dataset also contains 6,720 conversations, each with 5 turns.

Citat

"Conversational question answering (ConvQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG."
"To address this problem, we propose a reinforcement learning (RL) based model, CoRnNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance."
"Extensive experimental results show that CoRnNet outperforms state-of-the-art ConvQA models."

Viktiga insikter från

Conversational Question Answering with Reformulations over Knowledge Graph

by Lihui Liu,Bl... på arxiv.org 04-01-2024

https://arxiv.org/pdf/2312.17269.pdf

Djupare frågor

How can the proposed teacher-student architecture be extended to other natural language processing tasks beyond ConvQA

The proposed teacher-student architecture in CoRnNet can be extended to other natural language processing tasks by adapting the model to different datasets and objectives. For tasks beyond ConvQA, the teacher model can be trained on relevant data with human-generated reformulations specific to the new task. The student model can then learn to mimic the teacher's output using reformulations generated by LLMs tailored to the new domain. By fine-tuning the architecture and training data, the teacher-student approach can be applied to tasks such as sentiment analysis, text summarization, or named entity recognition. This extension would involve adjusting the input data, training process, and evaluation metrics to suit the requirements of the specific NLP task.

What are the potential limitations of using LLM-generated reformulations, and how can they be further improved

The use of LLM-generated reformulations in CoRnNet may have limitations in terms of the quality and relevance of the reformulations produced. LLMs, while powerful, may not always capture the nuanced context or semantics required for effective question reformulation. To address this, improvements can be made by fine-tuning the LLMs on domain-specific data to enhance the quality of the generated reformulations. Additionally, incorporating feedback mechanisms to iteratively improve the reformulations based on model performance and human evaluation can help refine the output. Leveraging ensemble methods with multiple LLMs or integrating external knowledge sources can also enhance the diversity and accuracy of the reformulations. Regular monitoring and updating of the LLMs based on evolving language patterns and user feedback are essential for continuous improvement.

How can the reinforcement learning approach in CoRnNet be adapted to handle more complex conversational scenarios, such as those involving multi-hop reasoning or open-ended responses

To adapt the reinforcement learning approach in CoRnNet for more complex conversational scenarios, such as those involving multi-hop reasoning or open-ended responses, several modifications can be implemented. Firstly, the state space in the RL model can be expanded to include additional information about the conversation history, context, and potential paths for multi-hop reasoning. This would enable the agent to make more informed decisions during the conversation. Secondly, the action space can be extended to allow for more diverse and intricate actions, facilitating multi-step reasoning and exploration of different paths in the knowledge graph. Reward shaping techniques can be employed to provide feedback at each step of the conversation, encouraging the model to learn long-term dependencies and make strategic decisions. Moreover, the policy network can be enhanced with advanced architectures like hierarchical RL or attention mechanisms to handle complex conversational structures effectively. By incorporating these adaptations, CoRnNet can be tailored to address the challenges posed by more intricate conversational scenarios.